Sloan-Swartz Centers for Theoretical Neurobiology

Sloan-Swartz Centers 2010 Annual Meeting
Monday, June 28 – Thursday, July 1, 2010
ale University
New Haven, Connecticut

The Sloan-Swartz Centers' 2010 Summer Meeting is the latest of an annual meeting that started in 1995 and has played a catalytic role in advancing the field of computational neuroscience.


This year's meeting brings together participants from 11 Centers, and seven distinguished invited speakers. It will be a stimulating event to foster exchanges between the Sloan-Swartz Centers and interactions across fields in neuroscience.




Top Computational Neuroscientists Visit Yale (June 29, 2010):






Monday June 28


Registration & Reception


4:00-6:00 PM  Guest registration at Loria Center (190 York St.)


6:00-8:00 PM   Reception at Berkeley College (125 High St., corner of High St. and Wall St.)


Tuesday June 29


7:30-9:30 AM  Breakfast (Pierson/Trumbull College)


8:00-8:45         Guest registration at Loria Center (190 York St.)


8:45-9:00         Welcoming remarks


Sensory Processing

Session Chair: Terry Sejnowski (Salk)


9:00-10:00 AM  Invited Speaker: Marvin Chun (Yale) - Competitive interactions during visual memory encoding and retrieval


10:00-10:30   Coffee break


10:30-11:00   Ken Miller (Columbia) - Surround suppression, normalization, and balanced amplification


11:00-11:30   Xaq Pitkow (Columbia) - Exact feature probabilities in images with occlusion

Abstract: To understand the computations of our visual system, it is important to understand also the natural environment it evolved to interpret. Unfortunately, existing models of the visual environment are either unrealistic or too complex for mathematical description. Here we describe a naturalistic image model and present a mathematical solution for the statistical relationships between the image features and model variables. The world described by this model is composed of independent, opaque, textured objects which occlude each other. This simple structure allows us to calculate the joint probability distribution of image values sampled at multiple arbitrarily located points, without approximation. This result can be converted into probabilistic relationships between observable image features as well as between the unobservable properties that caused these features, including object boundaries and relative depth. Using these results we explain the causes of a wide range of natural scene properties, including highly non-gaussian distributions of image features and causal relations between pairs of edges. We discuss the implications of this description of natural scenes for the study of vision.


11:30-12:00   Saeed Saremi (Salk) - Translation-invariant independent component analysis of natural images yields "double" Gabor filters


12:00-12:30 PM     Stephanie Palmer (Princeton) - Temporal coding in the songbird brain


12:30-2:00     Lunch/Posters


Neural Networks & Cell Type

Session Chair: William Bialek (Princeton)


2:15-2:45       Jessica Cardin (Yale) - Inhibitory interneuron contributions to local cortical networks


2:45-3:15       Costas Anastassiou (Caltech) - Electric fields in the brain: bug or feature?


3:15-3:45       Terry Sejnowski (Salk) - A new view of the motor cortex 


3:45-4:15       Coffee break


4:15-5:15       Invited Speaker: John Maunsell (Harvard) - Neuronal mechanisms of attention in monkey visual cortex 


8:00-10:00     Poster session         


Wednesday June 30


7:30-9:30 AM            Breakfast (Pierson/Trumbull College)      


Memory & Reward

Session Chair: Richard Andersen (Caltech)


9:00-10:00     Invited Speaker: May-Britt Moser (NTNU/Kavli) - Mechanisms for representing and remembering space


Abstract: Navigation depends on the ability to integrate information about location, direction and distance to a coherent representation of the spatial environment and to recall the “correct” map for each environment. The medial entorhinal cortex plays a cental role in this process. A key component of the entorhinal network is the grid cell. Grid cells fire only when the animal moves through specific, regularly spaced positions within the environment. Their active firing positions form a hexagonal pattern covering, for each cell, the entire local space available to the animal. Grid cells are continuously updated by other cell types, including head-direction cells and cells which fire only when the animal is at the edge or close to a boundary of the recording environment (border cells). Together these cells form a coherent map of local space that is maintained across environments. Grid cells are likely to form a major component of the cortical input to place cells in the hippocampus. An important difference between grid cells and place cells, however, is the tendency for place cells to form orthogonal representations in different environments. This process is thought to depend on the formation of attractor states in recurrent neuronal networks. While several experimental observations are consistent with the presence of attractors in CA3, the dynamic processes supporting it, at the time scale of behaviour, are not well understood. I will show that, in response to an instantaneous transition between two familiar and similar spatial contexts, hippocampal networks often undergo short periods of flickering between pre-formed representations before settling in on the representation most consistent with the new cue configuration, several seconds after the cue change. Convergence to each representation takes place in less than one theta cycle and fully expressed representations may alternate at frequencies of a single theta cycle. Flashbacks to the original state can also occur spontaneously, at low frequencies. The data suggest that, in CA3, pattern completion dynamics repeats within each individual theta cycle. The repetition may facilitate error correction, thus enhancing the discriminative power of the system in the presence of weak and ambiguous input cues from spatial representations in entorhinal cortex and stored representations within the hippocampus.


10:00-10:30   Coffee break


10:30-11:00   Daeyeol Lee (Yale) - Prefrontal cortex and decision making     


11:00-11:30   Caleb Kemere (UCSF) - Slow down and remember: behavior modulates information flow in the hippocampus


Abstract: Memories, in conjunction with our current perceptions, guide behavior. The hippocampus is essential for encoding new memories, retrieving stored memories, and consolidating memories for long-term storage. Distinct patterns of inputs to hippocampal output area CA1 may support these functions, with external input from superficial layers of the entorhinal cortex dominant during encoding, stored representations from area CA3 dominant during consolidation and a complex interplay between internal and external representations supporting retrieval. The factors which shape the balance between external and internal drive in the hippocampus are still unclear. We combined optogenetic circuit perturbations and multi-site recordings in freely behaving rats to show that movement speed continuously modulates information processing in the hippocampal circuit. The result is a smooth transition from greater CA3 to greater entorhinal influence on CA1 as animals move more quickly. Furthermore, we have found that the balance between CA3 and entorhinal input to CA1 changes as the animal learns about a new place. Collectively, these results suggest that behavior and novelty continuously modulate the effective strengths of both the CA3 and entorhinal inputs to CA1, providing a mechanism for smooth transitions among the encoding, retrieval, and consolidation functions of the hippocampal circuit.


11:30-12:00   Raoul Memmesheimer (Harvard) - Learning sequences in spiking neuronal networks          


12:00-12:30   Christina Savin (Bernstein) - Reward-dependent learning in recurrent neural networks - emergence of short-term memory  


12:30-2:00     Lunch/Posters          


Network States

Session Chair: Tony Zador (CSHL)


2:15-2:45       Don Katz (Brandeis) - Sequences of meta-stable states in gustatory perception       


2:45-3:15       Dajun Xing (NYU) - Neuronal and physical contributions to the LFP signal in the visual cortex


3:15-3:45       Scott Makeig (UCSD) - Towards human mobile brain/body imaging   


3:45-4:15       James Bonaiuto (Caltech) - Synthetic Brain Imaging: A computational interface between electrophysiology and neuroimaging         


Abstract: A continuing challenge of systems and cognitive neuroscience is to integrate data from animal neurophysiology and human brain imaging. A powerful way to address this problem is by developing biologically plausible neural network models constrained and testable by animal data, applying or extending these models to make hypotheses on circuitry underlying some range of human behavior, then using Synthetic Brain Imaging - averaging or otherwise transforming the output of simulations with the model - to make predictions testable against the results of human brain imaging. I present an improved approach to synthetic brain imaging that incorporates the use of spiking neurons and a model of the hemodynamic response. I give three examples demonstrating the power of this technique showing how it be used to validate a computational model, refine an existing model, and support a radically different interpretation of imaging data from that based on verbal, rather than computational, analysis.


4:15-4:45       Coffee break


4:45-5:45       Invited Speaker: Yang Dan (UCB) - Neuromodulation and brain state  


6:30 PM         Banquet for invited attendees (Sterling Memorial Library Nave, 120 High St.)



Thursday July 1


7:30-9:30 AM            Breakfast (Pierson/Trumbull College)       


Motor & Navigation

Session Chair: Markus Meister (Harvard)


9:00-10:00     Invited Speaker: Nicolas Brunel (CNRS) - Impact of cellular bistability on storage capacity in cerebellar Purkinje cells      

10:00-10:30   Coffee break


10:30-11:00   Bijan Pesaran (NYU) - Looking and reaching: A model for understanding inter-areal communication?       


11:00-11:30   Jeff Erlich (Princeton) - The role of the rat frontal orienting field in movement planning           

Abstract: Anatomical, stimulation and lesion data suggest that the rat Frontal Orienting Field (FOF) may be a premotor cortical area homologous to primate premotor areas such as the frontal eye field. We investigated the functional role of the FOF in rats trained to perform memory-guided orienting interleaved with “non-memory” orienting trials using pharmacology and electrophysiology. Unilateral inactivation of the FOF resulted in impaired contralateral responses during memory trials and a weaker contralateral impairment during non-memory trials. A significant fraction (36%) of recorded FOF neurons had firing rates that encoded the direction of the rats' later orienting motion during the delay period. Moreover, these cells predicted the latency of the response on a trial-to-trial basis. These results suggest that the FOF is an essential element in the neural circuit for movement planning and/or preparation.


11:30-12:00   Allison Doupe (UCSF) - Emergence and regulation of variability in cortical-basal ganglia circuits           


12:00-12:30   Aravinthan Samuel (Harvard) - Navigating small animals           


12:30-2:00     Lunch/Posters          


Neural Coding          

Session Chair: Robert Shapley (NYU)


2:15-2:45       Yang Yang (CHSL) - Differential sensitivity of different sensory cortical areas to behaviorally relevant millisecond-scale differences in neural activity  

Abstract: Animals can detect the fine timing of some stimuli. For example, in
subcortical structures, submillisecond interaural time differences are
computed to determine the spatial localization of sound. Although
cortical neurons have not been shown to achieve comparable
submillisecond precision, neurons in auditory, visual and barrel
cortex can lock with millisecond precision to the fine timing of some
stimuli. However, the ability of these cortical neurons to fire
precisely does not demonstrate such fine timing is behaviorally

To bridge the gap between physiology and behavior, we have previously
used electrical microstimulation to determine the temporal precision
with which fine differences in cortical spike timing could be used to
drive decisions. We found that in rat auditory cortex, animals could
be trained to use timing differences as short as 3 msec to drive
decisions (Yang et al, Nat Neurosci 11, 1262-3).

Is the auditory cortex unique in its ability to utilize such fine
timing differences to drive behavior? Because audition is often
considered to be a "fast" modality---one in which subtle differences
in temporal structure can be behaviorally relevant---it would not be
unreasonable to speculate that the auditory cortex had evolved special
mechanisms for rapid processing. On the other hand, it is appealing to
hypothesize that the cortex operates according to general principles
shared across different regions; in this view, the ability to make use
of millisecond-scale differences in neuronal activity would not be
unique to the auditory cortex.

To distinguish these hypotheses, we compared the ability of different
sensory areas to resolve subtle differences in neural timing. In the
visual cortex, we found that although animals could be trained to
resolve differences as short as 15 msec in neuronal activity, they
could not resolve differences as short as 5 msec. This lower limit of
5-15 msec was significantly higher than the limit of 3 msec we had
observed in auditory cortex, and is consistent with the view that
visual cortex is "slower" than auditory cortex. Surprisingly, we found
that the barrel cortex was even "faster" than auditory cortex, with a
lower limit below 1 msec. Our results suggest that different cortical
areas are differentially able to derive behaviorally relevant
information from the fine timing of neural activity.


2:45-3:15       Jozsef Fiser (Brandeis) - The link between spontaneous activity and statistically optimal internal models in the cortex        


Abstract: A number of recent behavioral studies implied that the brain maintains statistical internal models of the environment for perception, motor control, and higher order cognition. Nevertheless, the neural correlates of such models has not been characterized so far. We developed a method to investigate the adaptation of the internal neural representation to the statistics of the natural environment, based on the hypothesis that spontaneous and sensory evoked activities represent the prior expectations and posterior inferences of the internal models, respectively. Under this assumption, we found evidence that the internal model in the primary visual cortex of the ferret gradually adapts to optimally represent the statistics of the visual environment from eye opening to adulthood. We confirmed the same hallmarks of optimality in the primary auditory cortex of adult ferrets, suggesting that our findings might uncover a general feature of representation and computation in sensory cortex. We also found that the development of neural activity with age in the primary visual cortex does not show increasing sparsification and independence, thus challenging earlier suggestions that activity in this area is optimized for reducing redundancy in the representation of natural stimuli. The proposed relation of internal models with evoked and spontaneous activity can be used for evaluating future proposals regarding the computational function of cortical circuits.


3:15-3:45       Anne Churchland (CSHL) - Variance as a signature of neural computations during decision-making          


3:45-4:15       Coffee break


4:15-5:15       Invited Speaker: Ralph Greenspan (NSI) - From sleep to attention in Drosophila       



For other meeting details, visit the 2010 annual meeting web site at:





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