Monday

Neural Circuits and Neural Computation: A Systems-level Perspective

David C. Van Essen

Washington University in St. Louis

The amazing computational capabilities of the human brain reflect the dynamic flow of information through its fabulously complex neural circuitry. Elucidating the wiring diagram of the primate brain in general and the human brain in particular represents one of the grand challenges of neural computation. As the dominant structure of the brain (and in humans the most variable structure), the cerebral cortex is especially intriguing but also especially challenging to decipher. This presentation will focus on new neuroimaging approaches that show much promise for revealing the circuitry and functional organization of cerebral cortex in humans and nonhuman primates. A quantitative understanding of neural connectivity patterns at both macroscopic and microscopic levels will allow fundamental advances in modeling biologically plausible neural circuits that emulate many functions of the human brain.

"Internal models, adaptation, and the timescales of memory"

Reza Shadmehr

Johns Hopkins University

When the brain generates a motor command, it also predicts the sensory consequences of that command via an "internal model". The reliance on predictions makes the brain able to sense the world better than is possible from the sensors alone. However, this happens only when the models are accurate. To keep the models accurate, the brain must constantly learn from prediction errors. Here I use examples from saccade and reach adaptation to demonstrate that learning is guided by multiple timescales: a fast system that strongly responds to error but rapidly forgets, and a slow system that weakly responds to error but has good retention. What are these systems learning? In principle, the brain could be learning to more accurately predict the sensory consequences of motor commands and correct movements as they occur (i.e., learn a forward model). Using the theoretical framework of stochastic optimal control, I show that such adaptation should leave its signature in saccade trajectories. Experiments on a novel form of saccade adaptation seems to bare out the predictions. Therefore, it appears that motor errors give rise to multiple timescales of adaptation, and the fastest timescales learn forward models.

Insect vision: Physical constraints in natural information processing.

Rob de Ruyter van Steveninck

Department of Physics, Indiana University, Bloomington

Information processing by the brain is often understood to be constrained by the properties of the neural hardware that carries out the underlying computations. However, living systems cannot freely choose the quality of their sensory input. That is dictated by physical properties of the environment and by the necessity to respond to external stimuli in time. In other words, there are constraints on information processing, independent of the animal or its neural substrate, and these constraints are to some extent universal. Our knowledge of neural processing has mostly come from laboratory experiments, and so our understanding of those constraints, as they arise in real natural conditions, is still in its infancy. It will be interesting to quantify them, to see how they affect information processing strategies in real animals, and to assess whether the solutions that animals use are close to optimal in a way that we can understand.

The visual system is a good model for a study of these questions, because vision naturally operates over an enormous range of light intensities, that is, an enormous range of signal to noise ratios. Insect visual systems in particular are generally very amenable to quantitative analysis. I will introduce the subject with some historic examples that illuminate problems and solutions in insect vision, ranging from the optics of the insect eye, to motion vision, and behavior. Then I will discuss some of our early experiments and analyses on motion estimation in a natural context, illustrating the need for the system to adapt its computational strategies in order to cope with large variations in signal and noise. Work in this vein is still in its early stages. For the not too distant future, it is my hope that a combined effort in experiment and theory can achieve a deeper and more quantitative understanding of sensory information processing in the much richer context offered by the complexities and uncertainties of the natural world.

The Threefold Way in Computational Neuroscience

Henry Abarbanel

University of California, San Diego

There appear to be three (at least) identifiable approaches to Computational Neuroscience. After trying to identify these views, and comment on them with my own opinion, I will focus on the view I think will be most productive both for Neuroscience as a whole and for organizations such as the Los Alamos National Laboratory specifically.

This op-ed introduction will be followed by a discussion of a specific problem solved by neural systems in a variety of different ways: telling time. On scales from a few microseconds to many hours animals need to address the passage of time. I will review some of the known strategies for this and speculate on others.

Not to be too mysterious about the connections between this and the beginnings of the talk: I choose the second of the three fold approaches.

The critical role of electrical coupling in the generation of population oscillations in neocortex, at frequencies from <1 Hz to >100 Hz

Roger Traub

SUNY Downstate Medical Center

The neocortex generates oscillations at many different frequencies, the pattern of which correlates (in vivo) with the sleep/wake cycle and, in the waking state, with sensory stimulation and cognitive tasks. There are also correlations with the initiation and progression of epileptic seizures. Many of these oscillations can be replicated in brain slices, from both rodents and (more recently) humans, with a remarkable similarity, at the cellular level, to in vivo oscillations. In addition, detailed network simulations have advanced to the state where cellular oscillation patterns can be replicated and specific experimentally testable predictions offered - in some cases, already verified. Remarkably, most oscillation types in the neocortex, and hippocampus also, depend on electrical coupling between pyramidal neurons, and such coupling appears to exist at an unexpected site - between axons. I shall review the morphological data on this type of coupling, and also the phenomenology and mechanisms of gamma (30 - 80 Hz), beta2 (20 - 30 Hz) and very fast (>80 Hz) oscillations; and I shall outline how large-scale modeling of a thalamocortical column, using multi-compartment, multi-conductance neurons, has contributed to our understanding.

For the future, it is safe to say that models and theories of neocortical function will need to take account of electrical coupling between neurons, in addition to chemical synaptic interactions.

Toward a new science of connectomics

Sebastian Seung

Howard Hughes Medical Institute and MIT

Judging from current progress in nanoscale imaging and cutting, histochemical and genetic methods for staining, and computational algorithms for image analysis, it should soon be possible to create automated systems that will take a sample of brain tissue as input and generate its “connectome,” a list of all synaptic connections between the neurons inside. Such systems will give rise to a new field called "connectomics," defined by the high-throughput generation of data about neural connectivity, and the subsequent mining of that data for knowledge about the brain. I will discuss the possible impact that connectomics could have on our understanding of how the brain wires and rewires itself, the dynamics of activity in neural networks, and the neuropathological basis of mental disorders.

Memory and the Computational Brain

C. Randy Gallistel

Rutgers University

A read-write memory (Turing’s tape) is implied by behavioral evidence for the kinds of computations performed by even insect brains (e.g., dead reckoning) together with what computer scientists understand about the limitations that a finite state architecture places on computational power. However, neuroscientists have not looked for and (therefore?) not found a read-write memory mechanism. The absence of such a mechanism is often taken as a virtue, despite its relegation of the nervous system to the computationally weaker class of finite state machines. Computational models in contemporary cognitive science routinely presuppose the much more powerful Turing architecture, which is why they are “neurobiologically implausible.” I argue that this is a problem for neuroscience, not cognitive science. There must be a read-write memory mechanism. Its role in the causation of behavior is as central as the role of the read-only molecular genetic memory mechanism in the causation of biological structure. Its discovery will transform our understanding of neurobiology, just as the discovery of the structure of the gene transformed biochemistry.

Tuesday

Ensemble coding of visual motion in the primate retina and its readout in the brain

E.J. Chichilnisky

The Salk Institute

One of the great challenges in neuroscience is to understand the function of population codes. This entails answering at least three major questions: (1) how do populations of neurons encode information in their collective activity? (2) how are population codes read out by downstream neurons? (3) how do population codes influence sensation and behavior? The primate visual system illustrates these problems in abundance. Specifically, as signals flow from the peripheral to the central visual system, receptive fields become increasingly large and complex, reflecting readout of population coded signals at successive stages of processing. A comprehensive investigation of these computations therefore requires that one be able to experimentally monitor the entire population code and its readout, a demand that until recently has been technically prohibitive. In this talk I will describe our studies of a behaviorally important population code and its readout in the primate visual system. Visual motion is represented in the retina by traveling waves of activity in many non-direction-selective neurons. The direction and speed of these waves are read out by downstream neurons to control perception and behavior. We exploited a newly developed large-scale electrophysiological recording system to measure a substantial fraction of the population code for visual motion over a significant region of primate retina. To test how effectively the population code is read out by central neurons, we compared speed estimates obtained from retinal activity to speed estimates performed by human observers in matched stimulus conditions. We find that for brief, small stimuli, behavioral motion sensing performance approaches the limits imposed by the retinal signal, suggesting that population code readout can be efficient and nearly noiseless. On the other hand, for extended stimuli, behavioral motion sensing performance falls far short of limits imposed by the retinal signal, indicating that central readout of the peripheral population code can place the ultimate limit on sensation and behavior. We discuss the implications of these findings for how motion is computed in the brain. We also discuss the factors that have made it possible to obtain a comprehensive view of the population code, and the parallels that might be expected in future investigations of neural population codes.

Grand challenges in auditory research

Israel Nelken

Hebrew University

The auditory system has highly developed subcortical structures which are among the best understood in the brain. Furthermore, a number of rather simple rules, with rough understanding of peripheral representations, are sufficient to account for a surprisingly large number of perceptual phenomena. Nevertheless, we understand very little about the way these lower representations are combined to solve the 'hard' problems of audition, such as pitch representation, spatial localization in realistic conditions, speech understanding, or even such seemingly simpler processes such as segregating the incoming sound into its component 'objects'. I will argue that the common feature of these hard problems is the need to integrate information across both frequency and time, neither of which occur at the lower representation levels. I will present a number of (not necessarily mutually-exclusive) views of how auditory cortex may participate in these tasks. In order to discriminate between these possibilities it will be necessary to combine behavioral studies, multi-single neuron recordings and active manipulation of neural activity at a single-neuron resolution.

Wednesday

Nerve cell networks on microelectrode arrays: platforms for investigations of neuronal dynamics underlying information processing.

Guenter W. Gross

University of North Texas, Denton

It is unlikely that we will achieve a quantitative understanding of information processing in the vertebrate brain until we understand spatio-temporal action potential pattern processing in small neuronal ensembles or networks. All information enters in parallel, is processed in parallel, and shapes behavioral patterns in parallel. Computation seems to be performed primarily by colliding patterns with associated constructive and destructive interference. These phenomena are superimposed on spontaneous activity with complex effects on gating sensory information. In the extreme, spontaneous activity is either anticipatory, which facilitates rapid output pattern generation, or antagonistic, which can block incoming sensory information, as is seen in thalamo-cortical circuitry during sleep.

The requirement to quantify spatio-temporal patterns is unavoidable, and methods must be developed that capture the simultaneity of neuronal output patterns in neuronal circuits, networks, or ensembles. Although single neuron behaviour cannot be ignored, it is the cell group that provides reproducibility, fault tolerance, storage or experience-dependent responses, and (possibly) “decision states”. Cell group dynamics must receive emphasis for a “bottom-up” construction of brain function, but is difficult to study in situ. Primary cultures on microelectrode arrays (MEAs) form stable, spontaneously active networks that provide superior, long-term readout from many discriminated units, and simultaneous optical information on network morphology. In the past decade they have received extensive pharmacological and toxicological attention and can be considered “histiotypic”, as their responses are highly similar to those of the parent tissue in situ.

Given their thorough pharmacological characterization, it is now prudent to explore the more difficult domains of structure-function relationships and network dynamics with these platforms. Electrical stimulation is possible through the recording electrodes and responses to weak, pulsed magnetic fields have been demonstrated. Recently, it was shown that such networks in culture are weakly disassortative small world graphs, which differ significantly in their structure from randomized graphs with the same average connectivity (1). It is now possible to explore the internal dynamics of self-organized neuronal systems and ask key questions such as: (a) What is the origin and purpose of spontaneous activity? (b) What is the nature of biological fault tolerance? (c) How do networks select or develop specific spatio-temporal patterns? (d) What are the mechanisms and manifestations of pattern storage? (d) Can specific patterns be imposed on network via external stimulation? How do several networks interact if coupled electrically? Spontaneously active mammalian tissue on MEAs opens a window to the internal dynamics of networks with realistic applications to studies of pattern processing and to basic theoretical questions on the nature of information processing. They also find applications as tissue-based biosensors, and in areas such as toxicology and drug development. This presentation will summarize the progress made with these platforms, discuss the remaining problems, and outline realistic future research efforts.

(1) Bettencourt et al, 2007, Physical Review E. (in press).

Neurogrid: Emulating a million neurons in the cortex

Kwabena Boahen

Stanford University

I will present a proposal for Neurogrid, a specialized hardware platform that will perform cortex-scale emulations while offering software-like flexibility. Recent breakthroughs in brain mapping present an unprecedented opportunity to understand how the brain works, with profound implications for society. To interpret these richly grow-ing observations, we have to build models—the only way to test our understanding—since building a real brain out of biological parts is currently infeasible. Neurogrid will emulate (simulate in real-time) one million neurons connected by six billion synapses with Analog VLSI techniques, matching the performance of a one-megawatt, 500-teraflop supercomputer while consuming less than one watt. Neurogrid will provide the programmability required to implement various models, replicate experimental manipulations (and con-trols), and elucidate mechanisms by augmenting Analog VLSI with Digital VLSI, a mixed-mode approach that combines the best of both worlds. Realizing programmability without sacrificing scale or real-time op-eration will make it possible to replicate tasks laboratory animals perform in biologically realistic models for the first time, which my lab plans to pursue in close collaboration with neurophysiologists.

Design Principles that Endow the Brain with a Scalable Architecture.

Charles F. Stevens

Salk Institute

One of the Grand Challenges is to learn what mathematical operations are performed by neuronal circuits. The vertebrate brain has a scalable architecture – the computations become better in some way as the size of a circuit is increased – and understanding the scalability can place constraints on the types of computations done or offer clues about the nature of the computations. I will outline some methods for studying scalability rules in vertebrate brains, and illustrate these methods with a particular example of a universal scaling law and its underlying principle.

Imaging Associative Neural Plasticity in Man

Claudia D. Tesche

Univeristy of New Mexico

Magnetoencephalography (MEG) provides an opportunity to observe the dynamics of human brain function with exquisite temporal resolution. Aversive (fear) conditioning may result from the repeated pairing of a neutral “conditioned” visual stimulus (CS) with an aversive “unconditioned” auditory stimulus (US). This association leads to a learned response: presentation of the CS in isolation elicits behaviors associated with the US, even though no such stimulus is presented. Although aversive conditioning has been studied intensively in animal models, little is known about the dynamics of the conditioned response in the normal human brain. We utilized a MEG array to study associative neural plasticity in normal adults. CS presented in isolation following training elicited activation of auditory cortex and amygdala. In a subsequent study, the inter-stimulus interval between CS and US was shortened from 1500 ms to 418 ms. Visual CS predictive of aversive noise continued to elicit responses in auditory cortex, as well as frontal areas and cerebellum, although activation of amygdala was strongly suppressed.