INC Chalk Talk Series

The INC chalk talk series meets bi-weekly as a forum for interactive exchange on all aspects of neural computation. The purpose of these meetings is to foster the collaborative interactions between INC members and with colleagues across campus, and to stimulate new ideas and research initiatives.

Each meeting features one of the core or affiliated INC faculty labs/groups, with informal presentation of late-breaking research and new research directions. The meetings are open to the community, and we encourage broad participation across campus.

If you would like to subscribe to the INC Seminar/Talks Mailing list click here...

Contact: for further information, or to schedule a presentation.

When: Thursdays bi-weekly Fall through Spring

Time: 12:30 p.m. – 1:30 p.m.

San Diego Supercomputer Center, East Expansion
South Wing, Level B1, E129E

Sponsored by:
Brain Corporation,
Qualcomm Corporation,

Coming Up


Traveling waves across multiple scales synchronized to the rhythmic production of speech

Joaquin Rapela UCSD

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The recent introduction of multichannel recording techniques has made it possible to examine neural dynamics of single cortical areas. Using these methods traveling waves (TWs) have been reported in anesthetized (e.g., Benucci et al., 2007) and awake (e.g., Rubino et al., 2006) non-human animals, and more recently in humans during sleep (Muller et al., 2016). In this talk I will describe yet unreported TWs from a human subject rhythmically producing consonant-vowel syllables (CVSs) while we perform high-density electrocorticography recordings of his brain activity.

I will show that these TWs are precisely synchronized (in dynamical systems terms) to produced CVSs. This synchronization is observed in both TWs at the frequency of CVS production and in TWs at higher harmonics (c.f., Arnold tongues), suggesting that the observed TWs are not a trivial consequence of the rhythmic production of CVSs.

Our recordings show a strong coupling between phases at the slow frequency of CVS production and amplitudes in the high-gamma range. This coupling displays a peculiar spatial organization, which generates TWs of coupled high-gamma amplitude. That is, our recordings contain TWs at multiple scales: TWs in voltages filtered around the slow frequency of speech production and TWs in coupled high-gamma amplitude. I will demonstrate extended TWs of the former type traveling from primary to premotor cortex and TWs of the later type traveling along the same path but in reverse direction. These pairs of TWs might be a neural mechanism for the coordination between the control of vocal articulators in the premotor cortex and the perception of self-produced speech in the primary auditory cortex.

From an engineering standpoint, could these TWs be useful? I will present preliminary evidence on the consistency of TWs across repetitions of the same CVS, suggesting that TWs could be used for decoding intended speech from cortical activity.

Preprints related to this talk can be found in Rapela (2016) and in Rapela (2017).


Andrea Benucci, Robert A Frazor, and Matteo Carandini. Standing waves and traveling waves distinguish two circuits in visual cortex. Neuron, 55(1):103–117, 2007.

Lyle Muller, Giovanni Piantoni, Dominik Koller, Sydney S Cash, Eric Halgren, and Terrence J Sejnowski. Rotating waves during human sleep spindles organize global patterns of activity that repeat precisely through the night. eLife, 5:e17267, 2016.

Joaquín Rapela. Rhythmic production of consonant-vowel syllables synchronizes traveling waves in speech- processing brain regions, 2017. URL

Joaquı́n Rapela. Entrainment of traveling waves to rhythmic motor acts, 2016. URL

Doug Rubino, Kay A. Robbins, and Nicholas G. Hatsopoulos. Propagating waves mediate information transfer in the motor cortex. Nature neuroscience, 9(12):1549–1557, 2006.

Past Talks


How the brain got language: Challenges for computational cognitive neuroscience

Michael Arbib UCSD

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The Mirror System Hypothesis (MSH) for how the brain got language charts a course from a mirror system for manual action in LCA-m (Last Common Ancestor of humans and monkeys; informed by data on present-days monkeys) via simple imitation and manual gesture in LCA-c (informed by data on chimpanzees and other great apes) and thence via complex imitation, pantomime, protosign and protospeech to a "language-ready brain" in Homo sapiens, setting the stage for cultural evolution to yield the emergence of language. However, rather than assess the data pro and con this account of how the brain got language, the focus of the talk will be on current and possible future models in computational cognitive neuroscience that may aid the quest to refine MSH or replace it with something better.


Neural Evidence of the Cerebellum as a State Predictor

Hirokazu Tanaka Japan Advanced Institute of Science and Technology

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This talk provides neural evidence that the cerebellar circuit can predict future inputs from present outputs, a hallmark of an internal forward model. Evidence from clinical observations and psychophysical experiments indicates that impairments of the cerebellum lead to motor ataxia characterized by incoordination and dysmetria in multi-joint movements. Still, the precise mechanisms by which the cerebellum coordinates body movements are not yet understood. Recent computational studies hypothesize that the cerebellum performs state prediction known as a forward model. I analyzed firing rates of mossy fibers (inputs to the cerebellar cortex), Purkinje cells (output from the cerebellar cortex to dentate nucleus), and dentate nucleus cells (cerebellar output), all recorded from a monkey performing wrist tracking movements. To test the forward-model hypothesis, I then investigated if the current outputs of the cerebellum (dentate cells) could predict the future inputs of the cerebellum (mossy fibers). The firing rates of mossy fibers at time t+t1 were well reconstructed from as a weighted sum of firing rates of dentate cells at time t, thereby proving that the dentate activities contained predictive information about the future inputs. The linear equations derived from the firing rates resembled those of a predictor known as Kalman filter composed of prediction and filtering steps. This analogy leads to a speculation that the Purkinje and the dentate cells perform the prediction and the filtering steps, respectively. In summary, my analysis of cerebellar activities supports the forward-model hypothesis of the cerebellum.


Sparse Coding, Dimensionality Reduction, and Synaptic Plasticity: Evolving and Validating Biologically Realistic Models

Jeff Krichmar UC Irvine

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We have developed novel methodologies for the evolution and evaluation of spiking neural networks. This series of studies involved the use of GPU-accelerated, parallelized evolutionary algorithms. The project was intended to aid collaboration efforts between theoretical and experimental neuroscientists, who often spend tremendous time and money developing experiments that may not provide useful results. It was also intended to develop a veridical way of modeling neural systems by matching experimentally observed neurophysiological data. The networks evolve such that higher-order features of the region, such as functional behavior and population coding, emerge by virtue of replicated firing patterns. We developed an automated tuning framework and applied it to a case study using a dataset recorded from rat retrosplenial cortex (RSC). The framework successfully takes as input the recorded behavioral metrics associated with neuronal firing patterns which are encoded by idealized input neurons and evolves spike timing dependent plasticity parameters to create a spiking neural network that matches the experimentally observed data. Using the framework, novel experimental designs can be simulated and model response patterns can be recorded. By simulating experiments such as lesioning of the network and manipulation of behavioral inputs, new predictions can be made about the function of the brain region, and new experiments to probe that function can be designed without expending unnecessary time and effort on the part of experimentalists. To show how this might work, we link spike-timing dependent plasticity to dimensionality reduction in the brain by applying a statistical algorithm known as nonnegative matrix factorization (NMF) to the same dataset. We show that similar results, and a similar model of RSC functionality, can be achieved simply through nonnegative and parts-based dimensionality reduction, and propose that nonnegative sparse coding may be a canonical computation performed by plasticity rules in the brain to handle high-dimensional input spaces.


New Generations of ANNs for Conscious and Creative Robots

Vladimir Gontar Ben-Gurion University of the Negev

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We are presenting a new mathematical model for multiple artificial neural networks (ANN) based on a physicochemical principles and laws of nature. Initially this mathematical model was formulated for living and thinking systems dynamics and called discrete chaotic biochemical reactions dynamics (BRDCD) [1].

In this work we will demonstrate that the BRDCD within the individual neurons are accompanied and controlled by an “information exchange” within and between the brain neurons composing multiple neural network. We intend to show that BRDCD of the multiple neural networks responsible for a brain’s various cognitive functions. Both the qualitative and quantitative meaning of “information” and “information exchange” between the neurons and different neural networks have been formulated within its relation to a neuron’s chaotic states and formally introduced into the basic mathematical equations [2]. As will be shown in this work, proposed ANN not only exploiting extension of the fundamental physicochemical principles for answering questions about living and thinking systems driving forces, but also enable to simulate specific properties of living systems and brain such as “self-organization” and “self-synchronization", emergence and support of living states and "thoughts" among the others specific features. These all are resulted from the emergence of “phenomenological” states in the form of complex patterns (discrete time – space distributions of biochemical constituents composing brain neurons within the neural networks) which we associate with brain consciousness, cognition and creativity. Proposed ANN generates practically unlimited variety of discrete time and space creative patterns which are controlled by the continuous parameters of the mathematical models. This fact provided us with the confidence and prove that after specific learning process we can construct the proper ANN configuration and mathematical model for the desired conscious, creative and intelligence artificial system behavior directed to the various problems rational solution.
Results of numerical simulations will be presented in a form of creative 2D and 3D dynamical discrete time-space distributed patterns. Application of the artificial brain system for autonomous conscious creative & rational robot path planning will be presented and discussed in this talk.

[1] V. Gontar, Entropy as a driving force for complex and living systems dynamics, Chaos, Solitons & Fractals, 11, 2000, pp.231-236
[2] V. Gontar, Artificial brain systems based on neural network discrete chaotic dynamics. Toward the development of conscious and rational robots, in book, R. Mittu, D.Sofge, A. Wagner(eds), Robust Intelligence and Trust in Autonomous Systems, chapter 6, Springer, 2016, pp.97-115.


New Generations of ANNs for Conscious and Creative Robots

Todd Hylton, UC San Diego

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Thermodynamic concepts intimately pervade all of science and engineering, yet in computing today they appear only as an “engineering constraint” to an overarching computing and informational paradigm.  In this talk I examine the challenges in the current computing paradigm and propose a radical rethinking of computing in which thermodynamics plays the central role.  I will also connect ideas in thermodynamics to those in machine learning and biology.


Nonlinear Dynamics of Human Cognition

Mikhail Rabinovich BioCircuits Institute, UC San Diego

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In this talk we discuss a novel paradigm for the mathematical description of mental functions such as consciousness, creativity, decision making and prediction of the future based on the past. Such cognitive functions are described in the framework of canonical nonlinear dynamical models that form joint global hierarchical networks. Sub-networks cooperate and compete with each other by inhibition. The suggested approach uses heteroclinic dynamics to represent transitivity and sequential interaction of different cognitive modalities at all levels of network hierarchy. We build a model of global network dynamics based on a set of kinetic ecological equations describing the interaction with emotion at each level of the hierarchy. This makes the model applicable for the description and understanding of perception, creativity and other complex cognitive processes. We discuss the creativity phenomenon, for example, in a joint "human-robot mind" considering the approximation in which the artificial partner is responsible for the binding and retrieving of multimodal perception information. The formation of chunks and the creation of working memory is a joint effort – human-robot mind. The human mind is responsible for the evaluation of the information in working memory. Creativity is estimated by values of positive Lyapunov exponents. As an example, we discuss joint human-robot musical improvisation, which can be generalized for many applications, in particular, in the context of artificial intelligence applications and also to address several psychiatric disorders.


Reduced-memory deep residual networks for image classification using stochastic quantization

Mark McDonnell University of South Australia

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Motivated by the goal of enabling more efficient learning in deep neural networks, we describe a method for modifying the backpropagation algorithm that significantly reduces the memory usage during the training phase. The method is inspired by recent work on seeking neurobiological correlates of backpropagation-based learning that calculate gradients imprecisely. Specifically, our method introduces stochastic binarization of hidden-unit activations for use in the backward pass, after they are no longer used in the forward pass. We show that without stochastic binarization the method is far less effective. We trained wide residual networks with 20 weight layers on the CIFAR-10 and CIFAR-100 image classification benchmarks, achieving error rates of 5.43\%, 23.01\% respectively. These error rates compare with 4.53\% and 20.51\% on the same network trained without stochastic binarization. Moreover, we also investigated learning binary-weights in deep residual networks and demonstrate, for the first time, that Reduced-memory deep residual networks for image classification using stochastic quantizationnetworks using binary weights at test time can perform equally to full-precision networks on CIFAR-10, with both achieving ~4.5%. On Imagenet, we are still experimenting, but to date our binary-weights method at test time had a top-5 error rate of 20%.


Neuromorphic Deep Learning Machines

Emre Neftci UC Irvine

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An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory, and precise operations that are difficult to realize in neuromorphic hardware.

Remarkably, recent work showed that exact backpropagated weights are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. The rule requires only one addition and two comparisons for each synaptic weight using a two-compartment leaky Integrate & Fire (I&F) neuron, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving nearly identical classification accuracies on permutation invariant datasets compared to artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.


Towards Autonomous Surgery Delivered by Expert Robots

Michael Yip UC San Diego

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Surgical robotics offers an unprecedented ability to place and dexterously control small robotic instruments, immersive stereo imaging and other sensing modalities deep within inaccessible locations in the body. This presents major opportunities to in the medical domain to treat diseases (e.g. cardiac arrhythmia, lung cancer, colon cancer) in a minimally invasive fashion beyond. Yet, as these devices get smaller, more flexible and more mechanically complex, we are presented with a new challenge: do we rely on the doctor to sort out the challenging control of the devices while simultaneously processing the multi-modal biosignals from onboard sensing? Or do we off-load the low-level control of the surgery from human teleoperation onto a semi-autonomous or fully-autonomous framework? I will discuss our work in developing robot-assisted surgeries that analyze a multimodal spectrum of sensory information, physics models, and imaging information in real-time to optimally plan and perform semi-autonomous surgery. This includes real-time learning-based controllers for automating catheter and endoscopic robots within difficult anatomy, modular snake-like devices for efficient locomotion in difficult environments, visual computation methods for image-guided robotics, and robot intelligence for robot-human teams. Finally, I will discuss directions we aim to pursue in reinforcement learning such that with limited self-training, our robot-assistive devices learn to become expert robot surgeons.


Design of a heterogeneous neural network accelerator ASIC

Douglas A. Palmer KnuEdge Inc.

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In an effort to accelerate large-scale, sparse, heterogeneous neural network modeling a dedicated ASIC was designed, produced, and tested. The resulting device, a joint effort between Calit2 and KnuEdge Inc., is a router based, cloud-on-a-chip, 256-core, MPMD (Multiple-Program Multiple Data), machine that scales to 512K devices. Latency between devices is less than 400 ns. And random addressing benchmarks (GUPs) exceed 1 billion. Performance testing has shown that it is many times faster than existing CPU and GPU architectures for scatter/gather operations such as K-means clustering, FFTs, and heterogeneous sparse neural network models.

Dr. Palmer specializes in unconventional signal processing. He holds over a dozen U.S. patents and has founded or participated in the startup of many companies. He spent 8 years at the Stanford Linear Accelerator and then went on at Linkabit Corp, Western Research Corporation, became head of R&D Director at Hecht-Nielsen Neurcomputer, and then moved on to ThermoTrex, a subsidiary of ThermoElectron. In 1998 Dr. Palmer cofounded Path1 Network Technologies where he developed the world’s first video over IP systems. In 2002 he joined Calit2 at UCSD. He has been working with KnuEdge Inc. since 2006. Dr. Palmer received his MPhil and Ph.D. in High Energy Physics from Yale University after earning his B.A. in physics from UCSD Revelle College.


Multistable Winner-Takes-All neural networks with NMDARs and feedback inhibition

Patrick Shoemaker Computational Science Research Center, SDSU

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As a result of magnesium blockade, the macroscopic current-voltage relation of ion channels associated with the NMDA class of glutamatergic receptors is nonmonotonic. In conjunction with other membrane conductances, this feature can give rise to bi- and multi-stable dynamical regimes in neurons that have NMDA receptors. I describe a very simple neuronal network that displays winner-takes-all behavior as a consequence of this property. I first discuss the properties of this network under stationary or quasistatic conditions, and then proceed to consider dynamics, in particular network stability.

Pat Shoemaker received the Ph.D. degree in Bioengineering from UCSD in 1984. He has a longstanding interest in neural information processing and bio-inspired systems. From 1984 to 1999 he was with the Space and Naval Warfare Systems Center, where he worked among other things on hardware implementations of artificial neural networks. From 1999 to 2015 he was with Tanner Research, Inc., where he focused on bio-inspired systems and developed an growing interest in natural neural networks. Since the early 2000's he has collaborated with several neurobiologists on studies of visual processing in insects. He is currently a Research Associate Professor at the Computational Science Research Center at SDSU.


Rhythm in speech, music and movement: towards a common analytical framework for temporal structure

Andrea Ravignani Vrije Universiteit Brussel

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Behavioural research on the temporal properties of speech, music and movement often requires quantification of rhythmic structure. However, different research traditions investigating rhythmic behaviours have different methodologies, hindering comparability. Here, I present a suite of analytical tools to quantify rhythmic patterns across behaviours and domains. In particular, I focus on meaningful interpretation of simple techniques borrowed across disciplines, such as the normalised pairwise variability index, phase space plots, auto-regressive time series, and Granger causality. For each technique, I show its application to speech and music corpora, human psychological experiments, or chimpanzee behaviour.


New processor architecture for machine learning

Amir Khosrowshahi Nervana,

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Nervana is a San Diego-based startup providing a cloud platform for deep learning as a service. Deep learning is now state-of-the-art in a wide variety of domains including speech, images, and text, and is being quickly adopted in industry. Nervana's core technology is a novel distributed processor architecture for deep learning which aims to improve speed, scalability, and efficiency by an order of magnitude over the current state-of-the-art. I will present our work in the context of a variety of various promising efforts to build new hardware for advancing computation.

Amir Khosrowshahi is co-founder and CTO of Nervana. He studied computational neuroscience at Berkeley and physics and math at Harvard. Nervana was recently acquired by Intel where Amir is now VP of machine learning solutions in its data center group.


Rhythmic activity drives efficient search for maximally consistent states in neural networks and neuromorphic chips

Hesham Mostafa Integrated System Neuroengineering Lab, UCSD

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Humans and animals display a remarkable ability for constructing a rich and consistent interpretation of the surrounding environment based on imperfect and incomplete sensory inputs. This is a challenging problem that can be formulated as finding a configuration of variables that maximally satisfies a set of constraints encoding a model of the environment, while being consistent with the observed sensory input. We show that this problem can be efficiently solved using simple coupled attractor networks if these networks include a basic model of Gamma-band oscillations. By dynamically modulating the effective network connectivity, neuronal rhythms allow simple networks to collectively and efficiently search for maximally consistent configurations. We show that these rhythms give rise to network behavior that is functionally very similar to that of stochastic networks, providing an alternative framework for modeling probabilistic reasoning in the brain.
Since the oscillatory networks can efficiently solve difficult constraint satisfaction problems (CSPs), we developed a neuromorphic VLSI chip that captures the salient features of these networks and used the chip to solve Boolean satisfiability (SAT) and graph coloring problems. Empirically, we have shown that in the case of SAT problems, the search implemented by interacting oscillatory elements is as efficient as state of the art stochastic search algorithms. Our results highlight the benefits and pitfalls involved in taking neural dynamics in the brain as a source of inspiration for building physically realizable, non von-Neumann computing models, and they establish an unexpected and fundamental link between CSPs and the behavior of simple oscillatory systems.


Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network

Filip Piekniewski Brain Corp.

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Cognitive Learning using Evolutionary Computation

Will Browne

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Ulysses Bernardet Simon Fraser University, Surrey

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At each moment in time an animal is faced with a myriad of behavioral options; why does an animal initiate and persist in certain behaviors as opposed to others? Thematically this question of action selection and behavior regulation stands at the core of much of my past and present research. I will begin by presenting work on systems theory and neurobiology based models of social motivation and behavior regulation in insects, respectively. This will be followed by presenting current work that uses autonomous virtual characters to develop and test psychologically grounded models of nonverbal behavior. These models include the regulation of spatial behavior in a social setting, and work on a reflexive behavior architecture for virtual humans.


Our Brain Oscillations Follow Our Motor Rhythms

Joaquin Rapela Swartz Center for Computational Neuroscience, INC, UCSD

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A remarkable early observation on brain dynamics (Adrian and Mathews, 1934) is that when humans are exposed to rhythmic stimulation their brain oscillations can follow this rhythm. More recently, it has been found that attention can adjust the way in which oscillations follow periodic stimulation, in such a way that neurons are in a state of maximal excitability when an attended stimulus is expected to occur (Lakatos et al., 2008). Using what today are the neural recordings with highest spatial resolution, directly from the cortical surface of humans (ECoG grid with 4 mm interelectrode separation; Bouchard et al., 2013), covering most speech production and perception brain regions, I will describe a recent finding on this fascinating field of brain rhythms: when we speak in a rhythmic fashion, our brain oscillations follow our speech rhythm. Evidence for this finding comes from the alignment of the phases of brain oscillations at behaviorally relevant time points (highlighting the role of phase coherence in understanding the neural code; Makeig et al. 2002), from the coupling between low-frequency brain oscillations related to behavior and high-frequency oscillations related to neural spiking (phase-amplitude coupling; Canolty et al, 2006), and from the detection of traveling waves confined to the brain region that controls the vocal articulators (Rubino et al, 2006). This research is still on early stages, but it is worth sharing with the UCSD community.


Adrian ED, Matthews BH. The interpretation of potential waves in the cortex. J Physiol. 1934 Jul 31;81(4):440-71.

Bouchard KE, Mesgarani N, Johnson K, Chang EF. Functional organization of human sensorimotor cortex for speech articulation. Nature. 2013 Mar 21;495(7441):327-32.

Canolty RT, Edwards E, Dalal SS, Soltani M, Nagarajan SS, Kirsch HE, Berger MS, Barbaro NM, Knight RT. High gamma power is phase-locked to theta oscillations in human neocortex. Science. 2006 Sep 15;313(5793):1626-8.

Lakatos P, Karmos G, Mehta AD, Ulbert I, Schroeder CE. Entrainment of neuronal oscillations as a mechanism of attentional selection. Science. 2008 Apr 4;320(5872):110-3.

Makeig S, Westerfield M, Jung TP, Enghoff S, Townsend J, Courchesne E, Sejnowski TJ. Dynamic brain sources of visual evoked responses. Science. 2002 Jan 25;295(5555):690-4.

Rubino D, Robbins KA, Hatsopoulos NG. Propagating waves mediate information transfer in the motor cortex. Nat Neurosci. 2006 Dec;9(12):1549-57.


Multichannel recordings in neuroscience: methods for spatiotemporal dynamics

Lyle Muller

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Multichannel recording techniques in neuroscience have recently come of age. From dense multielectrode arrays to large-scale optical imaging techniques, novel recording technologies can now capture the fast dynamics of active cortical circuits in vivo. These technologies present the opportunity to probe the spatiotemporal dynamics of cortical circuits across a wide range of network states, from active sensation to the internally generated oscillations of sleep.

Concomitant with the rise of these technologies, however, is the need for novel and precise computational methods that can see through recording noise and capture the full complexity of cortical activity states. In recent work, we have introduced a non-parametric, phase-based method for detecting traveling waves in noisy multichannel data. This method requires no spatial smoothing, thus minimizing signal distortion and controlling false detections. Analysis of voltage-sensitive dye (VSD) imaging data from the visual cortex of the monkey with this method revealed that the population response to a small visual stimulus travels as a wave across the cortex, with a specific trial invariance. Extending this computational approach to more general spatiotemporal forms, we have now begun to study the large-scale structure of oscillations in electrocorticogram (ECoG) recordings of human cortex during sleep, where we find that a well-known sleep oscillation exhibits a specific, robust spatiotemporal pattern.


Towards Neuroadaptive Technology: Symmetrical Human‐Computer Interaction based on a cognitive user model generated by automatically probing the operator's mind

Thorsten O. Zander Team PhyPA, Biological Psychology and Neuroergonomics, TU Berlin, Germany

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Today's human‐machine interaction is asymmetrical in the sense that (a) the operator has access to any and all details concerning the machine's internal state, while the machine only has access to the few commands explicitly communicated to it by the human, and (b) while the human user is capable of dealing with and working around errors and inconsistencies in the communication, the machine is not. With increasingly powerful machines this asymmetry has grown, but our interaction techniques have remained the same, presenting a clear communication bottleneck: users must still translate their high level concepts into machine‐mandated sequences of explicit commands, and only then does a machine act. During such asymmetrical interaction the human brain is continuously and automatically processing information concerning its internal and external context, including the environment the human is in and the events happening there. I will discuss how this information could be made available in real time and how it could be interpreted automatically by the machine to generate a model of its operator's cognition. This model then can serve as a predictor to estimate the operator's intentions, situational interpretations and emotions, enabling the machine to adapt to them. Such adaptations can even replace standard input, without any form of explicit communication from the operator. I will illustrate this approach by several brief examples. The above‐mentioned cognitive model can be refined continuously by giving agency to the technological system to probe its operator's mind for additional information. It could deliberately and iteratively elicit, and subsequently detect and decode cognitive responses to selected stimuli in a goal‐directed fashion. Effectively, the machine can pose a question directly to a person's brain and immediately receive an answer, potentially even without the person being aware of this happening. This cognitive probing allows for the generation of a more fine‐grained user model. It can be used to fully replace any direct input to the machine, establishing effective, goal‐oriented implicit control of a computer system. I will give a more detailed example showing the potential of this approach. These approaches fuse human and machine information processing, introduce fundamentally new notions of 'interaction', and allow completely new neuroadaptive technology to be developed. This technology bears specific relevance to auto‐adaptive experimental designs, but opens up paradigm shifting possibilities for human‐machine systems in general, addressing the issue of asymmetry and widening the above‐mentioned communication bottleneck.


A neurobiological learning model inspired by deep learning, and its application to image classification

Mark D. McDonnell

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In computer science, 'deep learning' approaches are at last realizing the decades-old theoretical potential of artificial neural networks (ANNs), now to frequently achieve better-than-human performance on difficult pattern recognition tasks. When applied to classification and detection of objects in images, deep convolutional ANNs are used, and are often characterized as "biologically inspired." This is due to the hierarchy of layers of nonlinear processing units and pooling stages, and learnt spatial filters resembling simple and complex cells. An open challenge for computational neuroscience is to identify whether the spectacular performance of deep learning can be replicated in detailed models of cortical neurobiology that are constrained by known anatomical and physiology. Of particular importance is to identify neurobiologically-plausible learning rules that can produce equal performance to the backpropagation and stochastic gradient descent algorithms used as standard methods when training deep ANNs. Motivated by this goal, in this talk I will show mathematically how a standard cost-function used for supervised training of ANNs can be decomposed into an unsupervised decorrelation stage and a supervised Hebbian-like stage. Using the method to train a network with the MNIST handwritten digits image database results in classification of the MNIST test image set with less than a 1% error rate. This performance is comparable with state of the art deep-learning algorithms applied to this well-known benchmark. Surprisingly, this result is achieved by relying on untrained random synaptic weights and/or convolutional filters in all network layers except the final one. In the remainder of the talk I will posit that the method is plausible as a neurobiological learning mechanism in recurrently-connected layer 2/3 and layer 4 cortical neurons. I will demonstrate this using a conceptual model that includes:

* nonlinear dendritic activation;

* anti-Hebbian plasticity at synapses on distal dendrites receiving lateral input from other principal cells;

* top-down modulation during learning;

* lateral inhibition enforcing winner-take-all effects to determine inference.



A/Prof. Mark D. McDonnell received a PhD in electronic engineering and applied mathematics
from The University of Adelaide, Australia, in 2006. He is currently Associate Research Professor at the University of South Australia, which he joined in 2007. He has been awarded two research fellowships by the Australian Research Council, from 2007-2009 and 2010-2014, and the South Australian Tall Poppy of Science award. McDonnell's research focuses of the use of computational and engineering methods to advance knowledge about the influence of noise and random variability in neurobiological computation. McDonnell has published over 80 refereed papers, including several review articles, and a book on stochastic resonance, published by Cambridge University Press. McDonnell is a member of the editorial board of PLoS One and Fluctuation and Noise Letters, and has served as a Guest Editor for Proceedings of the IEEE and Frontiers in Computational Neuroscience.


Micro-movement statistics biomarkers may help diagnose and develop therapies for individuals with Autism Spectrum Disorders

Jorge Jose James H. Rudy Distinguished Professor of Physics
Condensed Matter Physics and Biophysics (Theoretical)

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Our daily movements are made of variable behaviors that can be studied at different time and length scales: For example, most people can easily achieve the simple task of reaching a cup in front them, but no two people will have exactly the same movements when we zoom in their trajectories at millisecond time scales. Most current movement studies are mainly based on visual observations of performances in motor tasks, which may leave out important information at finer time scales, often considered as noise. Atypical behaviors are actually highly heterogeneous in people with neurological disorders, e.g. like Autism Spectrum Disorders (ASD), Parkinson and Schizophrenia. This heterogeneity has particularly impeded developing efficient and quantitative biological diagnoses for these disorders when they are only based on human eye observations. There is thus a critical need to identify objective and data-driven biomarkers for these disorders as guides for basic biological research studies. Recent advent of high-resolution wearable sensing devices enable continuous motion recordings at milliseconds time scales, away from detection of the naked eye. Using this technology, we asked the question as to whether we could extract information leading to quantitative biomarkers for these disorders based on natural movement studies. I will only discuss our results for ASD individuals. By studying in detail the movement's statistics of human natural hand movements, we unraveled a new data-type characterized by the smoothness levels of the speed kinematics. Our statistical analysis led to a parameter plane that provides an automatic screening of different ASD subjects linking it, a posteriori, with their verbal speaking abilities. We also found different maturation paths in ASD compared to those typically developing. Unexpected similarities are also found among ASD parents and their progenies. Our studies are presently being used as part of a clinical trial testing for a genetically generated type of Autism.


Applying Perceptual Learning Principals to Brain Training Games

Aaron Seitz Professor, Department of Psychology, and Director of the Brain Games Center
University of California, Riverside

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Imagine if you could see better, hear better, have improved memory, and even become more intelligent through simple training done on your own computer, smartphone, or tablet. Currently brain training approaches are making these promises, however the reality falls short of the potential. Here I discuss how research in the field of perceptual learning can be translated to potentially yield a new generation of brain training approaches that are more effective and transfer to real world activities. In the present research, we adopted an integrative approach where the goal is not to achieve highly specific learning but instead to achieve general improvements to vision. We combined multiple perceptual learning approaches that have individually contributed to increasing the speed, magnitude and generality of learning into a perceptual-learning based video-game. Our results demonstrate broad-based benefits of vision in a healthy adult and visually impaired populations. We find improvements in near and far central vision peripheral acuity and contrast sensitivity, and real world on-field benefits in baseball players. The use of this type of this custom video game framework built up from psychophysical approaches takes advantage of the benefits found from video game training while maintaining a tight link to psychophysical designs that enable understanding of mechanisms of perceptual learning and has great potential both as a scientific tool and as a basis for future brain training approaches.


Computational Ethnography and Multimodal Sensing for Healthcare

Nadir Weibel CSE Department,
DesignLab, Center for Wireless and Population Health Systems, Calit2

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The advent of new sensing modalities, from ubiquitous and mobile computing to big data, is opening up new avenues for better understanding human cognition and behavior. Technology such as depth cameras, eye-tracking, or wearable sensing devices enable the tracking of people's activity in the real world, and online social media presence often reveals much of our day-to-day lives. While these new kind of data promise to advance our knowledge in many domains, applying this technology to healthcare has the potential to have an impact on the lives of many people from single individuals to larger groups.

In this talk I will introduce our approach towards new methodologies for multimodal sensing and visualization of healthcare-related activity in the real world. I will introduce our Lab-in-a-Box infrastructure, and how the combination of a multimodal sensing infrastructure and a multimodal visualization tool allow us to understand real-world healthcare in different ways. I will discuss results from tracking activity in the medical office and introduce our initial work in the context of surgical ergonomics, stroke evaluation and sign language analysis, including novel visualization approaches.


Dr. Nadir Weibel is a Research Faculty at UC San Diego's CSE Department and a Research Health Science Specialist at the VA San Diego. His work spans computer science and engineering, cognitive science, and the health domain and focuses on studying the impact of interactive technology on healthcare. As a member of the DesignLab (, as well as the Center for Wireless and Population Health Systems ( at UCSD he is spending his time between developing novel methodologies to better understand behavior and activity in healthcare, and designing new prototypes and interactive technology at the intersection of Human-Computer Interaction and Ubiquitous computing to better support patients, care-givers and health professionals. His research is funded by the National Institute of Health (NIH), the National Science Foundation (NSF), the Center for AIDS Research (CFAR), the Agency for Healthcare Research and Quality (AHRQ), as well as by UC San Diego internal funding and the Moxie Foundation.


Can porn be addictive? The use of the Research Domain Criteria (RDoC) framework in studies of new psychological disorders

Mateusz Gola Swartz Center for Computational Neuroscience, UCSD, Institute of Psychology, Polish Academy of Sciences

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Engineering Superpowers: Leveraging Theoretical Neuroscience to Maximize Human Potential

Vivienne Ming Founder & Executive Chair

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A wide-variety of societal problems can be framed as the challenge of connecting abstract, longer-term gains to highly local, individual decisions.

How can smartphone data across tens of thousands of individuals predict manic episodes in bipolar sufferers for prophylactic treatment? What should a recruiter look for in a candidate to optimize company-wide productivity over time? What can a parent do right now to maximize a child's health and educational outcomes?

In this talk, Dr. Ming will discuss a series of projects which apply theoretical neuroscience methodology to high-level problems in computational social science and are deployed in "the wild". Dr. Ming's goal is to maximize human potential by combining neuroscience, labor economics, machine learning, and product development.


Training for Transfer: Opportunities and Challenges for Application in Schools

Zewelanji Serpell Associate Professor, Dept. of Psychology
Virginia Commonwealth University

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Recent advances in cognitive science support the view that cognitive skills, such as executive functions, are malleable in childhood and through adolescence. This talk presents findings from a set of studies testing the efficacy of one-on-one and computer-based cognitive training programs with adolescents in lab and school settings. Findings suggest some success in improving cognitive skills, particularly working memory. Training modality matters, however, and there is little evidence of far transfer to academic skills. The talk goes on to describe our efforts to develop more ecologically valid and culturally responsive methods to train African American elementary school students by applying cognitive training principles within a school-based chess program. To conclude, I discuss the challenges associated with achieving and measuring transfer of cognitive training gains to academic and behavioral domains that are meaningful to schools.


Towards Pervasive and Real-World Neuroimaging and BCI

Tim Mullen Director, Qusp Labs (formerly Syntrogi Labs)
Co-Founder & CEO, Qusp

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I will discuss and demonstrate recent efforts by our group towards evolving a new generation of real-world and pervasive brain-computer interface (BCI) and neuroimaging technology. I will discuss some of our recent research in this domain, including a recent collaboration between Qusp, Cognionics and INC developing a high-resolution dry mobile BCI system supporting real-time artifact rejection, imaging of distributed cortical network dynamics, and inference of cognitive state with a 64-channel dry-electrode wireless EEG headset. I will also briefly outline Qusp's vision of enabling easy integration of advanced bio-signal processing methods into diverse everyday applications. I will discuss and demonstrate applications of NeuroScale - a cloud-based software platform, providing continuous real-time interpretation of brain and body signals through an Internet API - as well as Neuropype - a Python-based graphical software environment for rapid design and deployment of pipelines for (real time) bio-signal processing and machine learning.


Prospective optimization with limited resources

Joe Snider Institute for Neural Computation, UCSD

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The future is uncertain because some forthcoming events are unpredictable and also because our ability to foresee the myriad consequences of our own actions is limited. We designed a task in which humans select actions from an exponentially expanding number of prospects on a branching multivalued visual stimulus. A triangular grid of disks of different sizes scrolled down a touch screen at variable speeds. The larger disks represented larger rewards. The task was to maximize the cumulative reward by touching disks one at a time in a rapid sequence, forming an upward path across the grid. Every step along the path constrained the part of the grid accessible in the future. This task captured some of the complexity of the natural behavior in the risky and dynamic world, where ongoing decisions alter the landscape of future rewards. Comparisons of human behavior with the behavior of ideal actors revealed the strategies used by humans in terms of how far into the future they looked (their "depth of computation") and how often they attempted to incorporate new information about the future rewards (their "recalculation period"). For a given task difficulty, humans traded off their depth of computation for the recalculation period. The form of this tradeoff was consistent with a complete, brute-force exploration of all possible paths up to a resource-limited finite depth. A step-by-step analysis of the human behavior revealed that participants took into account very fine distinctions between the future rewards and abstained from some simple heuristics in assessment of the alternative paths, such as seeking only the largest disks or avoiding the smaller disks. The participants preferred to reduce their depth of computation or increase their recalculation period rather than sacrifice the precision of computation.


Beyond Steering in Human-Centered Closed-Loop Control

Lewis Chuang Max Planck Institute for Biological Cybernetics

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Machines provide us with the capacity to achieve goals beyond our physical limitations. For example, automobiles and aircraft extend our physical mobility, allowing us to travel vast distances far ahead of the time it would take us otherwise. It is truly remarkable that our natural perceptual and motor capabilities are able to adapt, with sufficient training, to the unnatural demands posed by vehicle handling. While much progress has been achieved in formalizing the control relationship between the human operator and the controlled vehicle, considerably less is understood with regards to how human cognition influences this control relationship. Such an understanding is particularly important in the prevalence of autonomous vehicular control, which stands to radically modify the responsibility of the human operator from one of control to supervision. In this talk, I will first explain how the limitations of a classical cybernetics approach can reveal the necessity of understanding high-level cognition during control, such as anticipation and expertise. Next, I will present our research that relies on unobtrusive measurement techniques (i.e., gaze-tracking, EEG/ERP) to understand how human operators seek out and process relevant information whilst steering. Examples from my lab will be used to demonstrate of how such findings can effectively contribute to the development of human-centered technology in the steering domain, such as with the use of warning cues and shared control. Finally, I will briefly present some efforts in modeling an augmented aerial vehicle (e.g., civil helicopters), with the goal of making flying a rotorcraft as easy as driving (


Biography: Lewis Chuang received his PhD. In Neuroscience in 2011 from the University of Tübingen. He currently leads a research group in the Max Planck Institute for Biological Cybernetics that investigates information seeking and processing behavior during closed-loop steering. He is also a principal investigator in a recently established research center for Quantitative Methods for Visual Computing (


A Framework for General Purpose Computation Using Neurons, Precise Timing, Delays, and Synchrony

Ryad Benosman Vision and Natural Computation Group
Institut National de la Sante et de la Recherche Medicale, Paris, France

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There has been significant research over the past two decades in developing new platforms for spiking neural computation. Current neural computers are primarily developed to mimic biology. They use neural networks, which can be trained to perform specific tasks to mainly solve pattern recognition problems. These machines can do more than simulate biology; they allow us to rethink our current paradigm of computation. The ultimate goal is to develop brain-inspired general purpose computation architectures that can breach the current bottleneck introduced by the von Neumann architecture. This work proposes a new framework for such a machine. We show that the use of neuron-like units with precise timing representation, synaptic diversity, and temporal delays allows us to set a complete, scalable compact computation framework. The framework provides both linear and nonlinear operations, allowing us to represent and solve any function. We show usability in solving real use cases from simple differential equations to sets of nonlinear differential equations leading to chaotic attractors.


Bayesian Inference in Distributed Architecture for Mobil Applications

Marcela Mendoza Bioengineering, and Neural Interaction Lab, UCSD

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Emerging mobile applications necessitate wireless transmission of large datasets and generate the need for efficient energy consumption. Exactly digitizing and transmitting these data is energy costly and leaves devices vulnerable to security attacks. Most decisions made with these data are statistical. From a Bayesian point of view, an accurate way to represent uncertainty and minimize risk in decision-making is via the posterior distribution. However, a way of accurately calculating the posterior has been traditionally unobtainable.

In this talk, I will present a distributed framework for finding the full posterior distribution and show its implementation in a suit of energy-efficient architectures. We focus on problems where the latent signal can be modeled as sparse (LASSO). We leverage our recent results of formulating Bayesian inference as a KL divergence minimization problem. We show that drawing samples from the Bayesian LASSO posterior can be done by iteratively solving LASSO problems in parallel. We instantiate this result with an analog-implementable solver and with a Graphics Processor Unit solution. These architectures are amenable to mobile applications and only transmit the minimal relevant information (e.g. the posterior) for optimal decision-making.


EEGLAB -- Recent Developments and Future Directions

Arnaud Delorme Swartz Center for Computational Neuroscience, INC, UCSD

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EEGLAB is a software environment developed by the Swartz Center for Computational Neuroscience at the University of California, San Diego, running on the very broadly established MATLAB platform to be a processing environment that can be applied to all major EEG hardware configurations and that provides a broad palette of the most advanced analysis procedures for research in this increasingly exciting functional brain imaging modality. A survey of 687 research respondents has reported EEGLAB to be the software environment most widely used for electrophysiological data analysis, worldwide, by a wide margin ( In this presentation I will highlight recent developments to the EEGLAB software environment, such as how to perform statistics on collection of single trials across subjects and future directions such as hierarchical statistical analysis using general linear models for group analysis.


Slowly oscillating periodic solutions for stochastic DDEs with positivity constraints

Ruth Williams

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Dynamical system models with delayed feedback, state constraints and small noise arise in a variety of applications in science and engineering. Under certain conditions oscillatory behavior has been observed. Here we consider a prototypical fluid model approximation for such a system --- a one-dimensional delay differential equation with non-negativity constraints. We explore conditions for the existence, uniqueness and stability of slowly oscillating periodic solutions of such equations. We illustrate our findings with simple examples from Internet rate control and gene regulation.

Based on joint work with David Lipshutz.


Dealing with Uncertainty: DARPA's New Paradigm for the 21st Century

Frank Fernandez

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Dr. Frank Fernandez was Director of the Defense Advanced Research Projects Agency (DARPA), the central R&D organization of the Department of Defense, from 1998 to 2001. He was a member of the Chief of Naval Operations (CNO) Executive Panel from 1983 until his appointment at DARPA. In this capacity, he provided advice to the CNO on a variety of issues. Currently, Dr. Fernandez is Chairman of the Naval Research Advisory Committee (NRAC), a committee chartered by law to advise the Secretary of the Navy on critical R&D issues. He is also a member of the Department of Homeland Security Science and Technology Advisory Panel, reporting to the Undersecretary for Science and Technology.

Dr. Fernandez received his Bachelor of Science in Mechanical Engineering and Master of Science in Applied Mechanics from Stevens Institute of Technology in New York, 1960-1961; and his Ph.D. in Aeronautics from California Institute of Technology in 1969. He was a Distinguished Research Professor in Systems Engineering and Technology Management at Stevens Institute of Technology in Hoboken, New Jersey.


Estimating Phasic and Sustained Dynamic Information Transfer in the Human Brain

Stephen Robinson MEG Core Facility, National Institute of Mental Health

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A bivariate nonlinear and nonparametric dynamical measure of directional information transfer is described that is suitable for analyzing electrophysiological signals such as magnetoencephalography (MEG), electroencephalography (EEG), and electrocorticography (ECoG). This analysis, "temporo-dynamic symbolic transfer entropy" (tdSTE), was applied to a representative MEG recording of a normal control subject while performing a working memory (n-back) task. A simultaneous linearly constrained minimum variance (LCMV) beamformer was used to estimate the source waveforms at nine selected brain locations. The tdSTE analysis was then applied to pairs of source waveforms, estimating both their forward and reverse directional information flow. The transfer entropy (TE) time-series were then averaged relative to the stimulus markers, either stimuli or responses, for each of the n-back tasks. The tdSTE analysis was evaluated for higher frequencies, above 50 Hz, avoiding the confound of lower frequency rhythms and emphasizing multi-unit cortical activity (MUA). The experimental tdSTE results reveal the presence of both sustained and phasic (event-related) components. The magnitude of the sustained components was much larger than their associated phasic components. Furthermore, we observed that the participation of information exchange between regions in each of the n-back tasks was encoded in the relative magnitudes of their sustained components. This was observed under condition that the TE for each n-back condition was based upon the probability distribution functions (PDFs) computed a priori from the corresponding blocks of data for the 0, 1, and 2-back trials. When PDFs were derived from the cumulative data of all three n-block tasks, little or no difference between 0, 1, and 2-back was observed. These results were validated against a variant of sequence shuffled, "surrogate" data, showing that tdSTE can reliably estimate directional information flow from the MEG data of single, individual subjects.


Insights Into Insight: What EEG Reveals about Problem Solving Across Multiple Domains

Ying Wu Swartz Center for Computational Neuroscience, INC

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Problems can be solved in a variety of ways. One might systematically evaluate a known space of possible solutions until the right one is found. Alternatively, it may prove necessary to enlarge or restructure the expected problem space – so called "thinking outside the box." This approach can yield an experience of unexpected insight or feeling of Aha!. Whereas the subjective suddenness of an "Aha!" moment may lead to the impression that insight must be precipitated by a set of discrete, short-lived neural events, I will present evidence that even before a problem is presented, scalp-recorded measures of resting or baseline brain states are linked with future performance and likelihood of experiencing insight during the search for a solution. Additionally, I will show that compared to more systematic problem solving approaches, insight is accompanied by differences in cortical and likely cognitive engagement that are detectable throughout much of the problem solving phase, rather than being confined to a distinct interval immediately preceding the dawn of a solution.


Role of Neuromodulators and Neural Correlations in Network Encoding

Victor Minces UCSD Cognitive Science
Temporal Dynamics of Learning Center

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A fundamental variable in understanding the relationship between brain activity and sensory processing is the coding efficiency, or how much information about a set of stimuli a neuronal pool represents. Coding efficiency depends on the information represented by the individual neurons (associated with their signal to noise ratios), but also on the statistical dependencies among neurons (associated with their correlated activity); the influence of the latter becomes more important as the size of the neural pool under consideration is larger. I present a novel, simple way to estimate the encoding efficiency of neuronal pools in terms of signal to noise ratios and pairwise correlations. This approach allows exploration of the role of neuronal correlations in shaping coding efficiency. I apply this formulation to experimental data gathered from the visual cortex of the awake mouse, and show that neuromodulator acetylcholine shapes neural correlations in a manner that is compatible with enhanced encoding efficiency, learning, and attention.


Cell Assemblies of the Basal Forebrain

Douglas A. Nitz Dept. of Cognitive Science, UCSD

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Cortically-projecting basal forebrain neurons play a critical role in learning and attention, and their degeneration accompanies age-related impairments in cognition. Despite the impressive anatomical and cell-type complexity of this system, currently available data suggest that basal forebrain neurons lack complexity in their response fields, with activity primarily reflecting only macro-level brain states such as sleep and wake, onset of relevant stimuli and/or reward obtainment. The current study examined spiking activity of basal forebrain neuron populations across multiple phases of a selective attention task. Clustering techniques applied to the full population revealed bursting and non-bursting subtypes as well as a number of distinct categories of task-phase-specific activity patterns. Distinct population firing-rate vectors defined each task phase and most categories of task-phase-specific firing had counterparts with opposing firing patterns. Finally, among all subtypes of simultaneously recorded basal forebrain neurons, co-activity patterns evidenced grouping of neurons into cell assemblies whose spiking activity was optimally synchronized at a beta frequency (~20 Hz). Thus, consistent with known anatomical complexity, basal forebrain population dynamics are capable of differentially modulating their cortical targets over beta-frequency time intervals and according to the unique sets of environmental stimuli, motor requirements, and cognitive processes associated with different task phases.


Biography: Douglas Nitz received his PhD from UCLA in 1995 working primarily on brainstem mechanisms of rapid-eye-movement sleep production. As a post-doctoral student at the University of Arizona, he turned his attention to the problem of determining how single neurons and the ensemble activity patterns they compose map spatial relationships between an organism and its environment. This work continued at the Neurosciences Institute in San Diego where he worked between 1998-2008. Nitz joined UCSD's Department of Cognitive Science in 2008 and continues to work on neural mechanisms for spatial cognition and its translation into decisions and actions. The basal forebrain work to be presented is the outgrowth of a new research project undertaken with Andrea Chiba, also of the UCSD Cognitive Science Department.


Cognitive Networks and the Noisy Brain

Bradley Voytek UCSD Cognitive Science, Neurosciences, and INC

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Perception, cognition, and social discourse depend upon coordinated neural activity. This coordination operates within noisy, overlapping, and distributed neural networks operating at rapid timescales. These networks are built upon a structural scaffolding with intrinsic neuroplasticity that changes with development, aging, disease, and personal experience. While the exact mechanisms for interregional communication are unknown, there is increasing evidence that oscillatory local field synchronization between neuronal groups facilitates communication at specific phases of the preferred oscillatory frequency. Successful interregional communication may rely upon the transient synchronization between distinct low frequency (< 80 Hz) oscillations, allowing for brief windows of communication via phase-coordinated local neuronal spiking. However such a communication scheme would be susceptible to small perturbations in spiking rate, probability, and/or synchronization. I will explore the consequences of this theory in terms of understanding cognition and a variety of neurological and psychiatric disorders.


High-Resolution EEG Source Imaging

Zeynep Akalin Acar UCSD INC Swartz Center for Computational Neuroscience

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Accurate electroencephalographic (EEG) source localization requires a forward electrical head model incorporating accurate conductivity values for the major head tissues. While consistent values have been reported for scalp, brain, and cerebrospinal fluid, measured brain-to-skull conductivity ratio (BSCR) estimates have varied between 8 and 80, likely reflecting both measurement method and inter-subject differences. In simulations, mis-estimation of skull conductivity produce source localization errors as large as 31 mm (Akalin Acar and Makeig 2013). In this presentation, I will describe a gradient-based iterative source conductivity and localization estimation (SCALE) approach for estimating head tissue conductivities and spatial brain source distributions simultaneously in a magnetic resonance (MR) head image-derived head model based on scalp maps of near-dipolar sources found by adequate independent component analysis (ICA) decomposition of sufficient high-density EEG data. I will show validations using simulated data, and applications on real EEG data from two adults and babies. The ability to accurately estimate skull conductivity non-invasively from recorded EEG data itself, in combination with an electrical head model derived from a subject anatomic MR head image, could remove a barrier to using EEG as a cm-scale accurate 3-D functional cortical imaging modality.


Neuromorphic Cognition

Emre Neftci INC and BCI, UCSD

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Our ability to evoke intelligent processing on artificial neural systems goes hand in hand with a confluence of neuroscience, machine learning and engineering. I will describe recent advances in neuromimetic inference and learning algorithms that address this challenge from a neuromorphic systems perspective. These algorithms range from finite state machines synthesized with neural models of working memory, attention and action selection for solving cognitive tasks; to the learning of probabilistic generative models with models of stochastic sampling and plasticity in spiking neural networks. These advances form the groundwork for a domain-specific language for probabilistic models that can be compiled against neural substrates. Combined with state-of-the-art neuromorphic electronic hardware, this framework will provide a unique technology for studying the processes of the mind at multiple levels of investigation.


Memcomputing: Computing with and in Memory Using Collective States

Massimiliano Di Ventra Department of Physics, UCSD

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I will discuss a novel computing paradigm we named memcomputing [1] inspired by the operation of our own brain which uses (passive) memory circuit elements or memelements [2] as the main tools of operation. I will first introduce the notion of universal memcomputing machines (UMMs) as a class of general-purpose computing machines based on systems with memory. We have shown [3] that the memory properties of UMMs endow them with universal computing power--they are Turing-complete--, intrinsic parallelism, functional polymorphism, and information overhead, namely their collective states can support exponential data compression directly in memory. It is the presence of collective states in UMMs that allows them to solve NP-complete problems in polynomial time using polynomial resources. As an example I will show the polynomial-time solution of the subset-sum problem implemented in a simple hardware architecture that uses standard microelectronic components [4]. Even though we have not proved NP=P within the Turing paradigm, the practical implementation of these UMMs would represent a paradigm shift from present von Neumann architectures bringing us closer to brain-like neural computation [5].


[1] M. Di Ventra and Y.V. Pershin, Computing: the Parallel Approach, Nature Physics, 9, 200 (2013).
[2] M. Di Ventra, Y.V. Pershin, and L.O. Chua, Circuit Elements with Memory: Memristors, Memcapacitors, and Meminductors, Proc. IEEE, 97, 1717 (2009).
[3] F. L. Traversa and M. Di Ventra, Universal Memcomputing Machines, IEEE Transactions on Neural Networks and Learning Systems, (in press), arXiv:1405.0931.
[4] F. L. Traversa, C. Ramella, F. Bonani, and M. Di Ventra, Memcomputing NP-complete problems in polynomial time using polynomial resources and collective states, arXiv:1411.4798
[5] F. L. Traversa, F. Bonani, Y.V. Pershin and M. Di Ventra, Dynamic Computing Random Access Memory, Nanotechnology 25, 285201 (2014).


Bio: Massimiliano Di Ventra obtained his undergraduate degree in Physics summa cum laude from the University of Trieste (Italy) in 1991 and did his PhD studies at the Ecole Polytechnique Federale de Lausanne (Switzerland) in 1993-1997. He has been Research Assistant Professor at Vanderbilt University and Visiting Scientist at IBM T.J. Watson Research Center before joining the Physics Department of Virginia Tech in 2000 as Assistant Professor. He was promoted to Associate Professor in 2003 and moved to the Physics Department of the University of California, San Diego, in 2004 where he was promoted to Full Professor in 2006. Di Ventra's research interests are in the theory of electronic and transport properties of nanoscale systems, non-equilibrium statistical mechanics, DNA sequencing/polymer dynamics in nanopores, and memory effects in nanostructures for applications in unconventional computing and biophysics. He has been invited to deliver more than 200 talks worldwide on these topics (including 6 plenary/keynote presentations, 7 talks at the March Meeting of the American Physical Society, 5 at the Materials Research Society, 2 at the American Chemical Society, and 1 at the SPIE). He serves on the editorial board of several scientific journals and has won numerous awards and honors, including the NSF Early CAREER Award, the Ralph E. Powe Junior Faculty Enhancement Award, fellowship in the Institute of Physics and the American Physical Society. He has published more than 140 papers in refereed journals (13 of these are listed as ISI Essential Science Indicators highly-cited papers of the period 2003-2013), co-edited the textbook Introduction to Nanoscale Science and Technology (Springer, 2004) for undergraduate students, and he is single author of the graduate-level textbook Electrical Transport in Nanoscale Systems (Cambridge University Press, 2008).


Corticospinal Computation of Sensorimotor Control for Normal and Abnormal Movements

Ning Lan Institute of Rehabilitation Engineering
Med-X Research Institute
School of Biomedical Engineering
Shanghai Jiao Tong University

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Evidence in human motor behaviors suggests that separate motor modules are used for control of movement and posture in the central nervous system (CNS). Each contains private central programming and corticospinal pathway of motor commands to the spinal alpha and gamma motoneurons (MNs). Abnormal motor behaviors, such as tremor in patients with Parkinson's disease (PD), demonstrate the similar feature of modularity. In this presentation, I will discuss a combined behavioral and computational approach to understanding the corticospinal computation of sensorimotor control for both normal and abnormal movements. A modular control model for movement and posture is proposed based on the dual spinal alpha-gamma sensorimotor system. In this study, we ask these fundamental questions. How can the alpha-gamma sensorimotor system implement modular control? And what is the computational role of propriospinal neurons (PN) in modular control of movements (both normal and abnormal)? Simulated model behaviors capture kinematic and EMG features of reach-and-hold human movements. Furthermore, the modular control model is able to predict pathological behaviors of action tremor in essential tremor (ET) patients and resting (or posture) tremor in PD patients. These results suggest a computational gating function of PN network for transmission and processing descending motor commands (both normal and abnormal), and support the hypothesis that modular control of posture and movement can be achieved with the dual alpha-gamma sensorimotor system.

Bio: Professor Ning Lan obtained the B.S. degree in Precision Instruments from Shanghai Jiao Tong University (SJTU) in 1982, and Ph.D. degree in Biomedical Engineering from Case Western Reserve University (CWRU) in 1989. Before joining SJTU, he was on the faculty in Biokinesiology and Physical Therapy of University of Southern California. Currently, he serves as a guest associate editor of Frontiers in Computational Neuroscience of the Nature Publishing Group, and is on the editorial board of ISRN Computational Biology, and Physical Medicine and Rehabilitation - International. He also serves as the Founding Deputy Director of The Strategic Alliance for Research and Development of Rehabilitation and Assistive Technologies for Medical Industries in China. He was one of the founding members of Neural Engineering Committee of the Chinese Society of Neuroscience, and served the founding depute director from 1995 to 1999. From 1997 to 2001, he served as Assistant Editor of IEEE Transactions on Rehabilitation Engineering (now IEEE Transactions on Neural Systems and Rehabilitation Engineering), and Associate Editor of Chinese Journal of Rehabilitation Theory and Practice from 1997-1999. He organized 1st, 2nd and 3rd International Conference on Rehabilitation Medical Engineering (CRME) in Shanghai, China in 2012, 2013 and 2014. His research interests are in neural electrical stimulation, neuromodulation for patients with Parkinson's disease, stroke and spinal cord injury, and neural and computational modeling of movement control.


Nanoscale engineering mediating neural function and activity

Ratnesh Lal MAE, Bioengineering and CNME/IEM

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Coordinated activity of ion channels and receptors in brain cells control electrical and chemical signal transduction and their synaptic transmission mediating normal brain activity and pathologies. Current emphasis of the BRAIN Initiative has been to design enabling technology to understand ensemble brain activity. Defining nanoscale (< 10 nm) structural conformations of ion channel/receptors mediating brain activity (though essential for controlling intricate brain connectivity) is unappreciated and yet these nanostructures would ultimately be driving any remedial paradigm(s) resulting from the functional mapping initiative. Unfortunately, there aren't many techniques to image 1-10 nm biological structures in liquid. We have been developing an array-atomic force microscope (AFM) integrated with functional analytical tools (e.g., electrical conductance measurement, FRET, TIRF), each individual AFM consisting of an array of conducting cantilevered probes with self-sensing and actuation capabilities. The new AFM-array will enable 1) imaging the synaptic network at the scales of its organization, nano-to-macro scale, 2) measuring localized electrical and chemical activity, and 3) interfacing with animal and human subjects. This novel technology will allow for force controlled imaging of live neural cells at multiple locations simultaneously with independent imaging feedback. Integration of an ion sensing tip on the cantilevers will allow for localized and highly parallel electrical recording of synaptic activity. This technology will enhance our understanding of how synaptic networks mediate global neural communication.


Advances in measurement of sleep

Conor Heneghan University College Dublin, and ResMed

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Despite the fact that we spend nearly one third of our lives asleep, surprisingly little was known about sleep until the 20th century. Now, sleep medicine is firmly established as a significant branch of medical practice, taking its roots strongly from the work of Nathaniel Kleitman and colleagues at the University of Chicago in the 1950s. The field progressed in the 1960s, with an increasing standardization of physiological signal recording that led to the current standard for sleep measurement—the polysomnogram (PSG). Recently, there has been continued interest in developing sleep measurement technologies that can provide useful information about sleep, over multiple nights, and with minimal interference to the subject. One technology that shows a lot of promise in this area is radio-frequency (RF) biomotion sensing of sleep. For the last several years, our research team has focused on producing a noncontact RF biomotion sensor, which is practical for use in home and lab-based sleep measurement. Our goal has been to simplify the process of sleep and respiration measurement, allowing continuous monitoring over multiple nights—permitting individuals to understand their own sleep patterns or enabling medical professionals to provide improved care and guidance to individuals suffering from a number of sleep and respiratory disorders. We have developed algorithms that can map the movement signal into useful information about sleep and respiration. In studies where the sensor and algorithm are compared with the gold-standard PSG measurements, the noncontact system agrees with the sleep/wake classification of the PSG more than 85% of the time. This is comparable with the best actigraphy systems. Moreover, since the system can measure respiratory effort, it can be used to identify apnea and hypopnea events with a good degree of accuracy. In a study of 74 subjects suspected of having sleep apnea, the noncontact sensor system was 90% sensitive and 92% specific in recognizing patients with and without sleep apnea, using the standard cutoff of an Apnea Hypopnea Index greater than 15 to define sleep apnea. The ongoing challenge is to further improve the accuracy and sensitivity of the technology and, ideally, to add in further information without compromising the convenience and noninvasiveness of the overall system from a user's point of view.


Conor Heneghan, "Wireless Sleep Measurement: Sensing Sleep and Breathing Patterns Using Radio-Frequency Sensors," IEEE EMBS Pulse Magazine, September 21, 2014.


Conor Heneghan, PhD, is Chief Engineer with ResMed's Strategy and Ventures Group, and Adjunct Associate Professor at University College Dublin School of Electrical, Electronic and Communications Engineering. He received his PhD in Electrical Engineering from Columbia University, New York in 1995, and was co-founder of BiancaMed, a pioneer in non-contact sleep measurement which was acquired by ResMed in 2011. His research interests are biomedical signal processing and analysis, particularly focused in the areas of sleep, cardiovascular and respiratory disorders.



Characterizing Neural Ensembles from High-Resolution Physiological Recordings

Joaquin Rapela Swartz Center for Computational Neuroscience, UCSD

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If we observe a fluid at the molecular level we see random motions, but if we look at it macroscopically we may see a smooth flow. An intriguing possibility is that by analyzing brain activity at a macroscopic level, i.e., at the level of neural ensembles, we may discover patterns not apparent at the single-neuron level, that are as useful as velocity or temperature are to understand, and predict, the motion of fluids. Several models have been developed to simulate the activity of ensembles of neurons, but only now, with the availability of high-resolution neural recordings, it is possible to accurately estimate parameters in these models from physiological data, and learn from these parameters how ensembles represent information in the brain. In this talk I will describe methods that we are developing to characterize neural ensembles from electrophysiological recordings, and comment on two applications of these methods that we are currently pursuing.

I will show how starting from a model of single neuron of a given type (e.g., Hodgkin and Huxley) it is possible to derive accurate dynamical models of ensembles of homogeneous neurons of the given type. We call these models ensemble density models or EDMs. EDMs are high-dimensional nonlinear dynamical models. To facilitate the estimation of state variables and parameters in large networks of EDMs from physiological data, we derived a method that significantly reduced the dimensionality in EDMs, with minor degradation of approximation power. We are using a faster maximum-likelihood method for the estimation of connectivity parameters in networks of EDMs, and an MCMC algorithm that approximates the expected value, as well as higher moments, of both states and connectivity parameters, conditioned on observed data. I will outline two applications of these methods: 1) the study of the role of connectivity among neural ensembles for the control of vocal articulators during speech production, using high-resolution ECoG recordings in humans; and 2) the estimation of ensemble receptive fields in sensory cortices.

We want to apply these tools to characterize diverse ensemble electrophysiological recordings. If you have these type of recordings, and you may want to analyze them at the ensemble level, please contact the speaker at


Reference: J. Rapela, M. Kostuk, P. Rowat, T. Mullen, K. Bouchard, and E. Chang, "Characterizing Neural Activity at the Ensemble Level," IEEE EMBS BRAIN Grand Challenges Conference, Washington DC, Nov. 13-14, 2014. Available at



Faculty Spotlight

Tzyy-Ping Jung
Elevated to IEEE Fellow for contributions to blind source separation for biomedical applications.

...more info

Staff Spotlight

INC's First Annual Hike N' Lunch Event
INC Staff at the Torrey Pines Glider Port.

...see more