Maoz Shamir

Ben Gurion University

March, 10, 2021

 STDP and the transfer of rhythmic signals in the brain

Rhythmic activity in the brain has been reported in relation to a wide range of cognitive processes. Changes in the rhythmic activity have been related to pathological states. These observations raise the question of the origin of these rhythms: can the mechanisms responsible for generation of these rhythms and that allow the propagation of the rhythmic signal be acquired via a process of learning? In my talk I will focus on spike timing dependent plasticity (STDP) and examine under what conditions this unsupervised learning rule can facilitate the propagation of rhythmic activity downstream in the central nervous system. Next, the I will apply the theory of STDP to the whisker system and demonstrate how STDP can shape the distribution of preferred phases of firing in a downstream population. Interestingly, in both these cases STDP dynamics does not relax to a fixed-point solution, rather the synaptic weights remain dynamic. Nevertheless, STDP allows for the system to retain its functionality in the face of continuous remodeling of the entire synaptic population.

Sara Solla

Northwestern University

March 17, 2021

Low Dimensional Manifolds for Neural Dynamics

The ability to simultaneously record the activity from tens to thousands to tens of thousands of neurons has allowed us to analyze the computational role of population activity as opposed to single neuron activity. Recent work on a variety of cortical areas suggests that neural function may be built on the activation of population-wide activity patterns, the neural modes, rather than on the independent modulation of individual neural activity. These neural modes, the dominant covariation patterns within the neural population, define a low dimensional neural manifold that captures most of the variance in the recorded neural activity. We refer to the time-dependent activation of the neural modes as their latent dynamics, and argue that latent cortical dynamics within the manifold are the fundamental and stable building blocks of neural population activity.

Subkin Lim

NYU Shanghai

March 24, 2021

Hebbian learning, its inference, and brain oscillation

Despite the recent success of deep learning in artificial intelligence, the lack of biological plausibility and labeled data in natural learning still poses a challenge in understanding biological learning.  At the other extreme lies Hebbian learning, the simplest local and unsupervised one, yet considered to be computationally less efficient.  In this talk, I would introduce a novel method to infer the form of Hebbian learning from in vivo data.  Applying the method to the data obtained from the monkey inferior temporal cortex for the recognition task indicates how Hebbian learning changes the dynamic properties of the circuits and may promote brain oscillation.  Notably, recent electrophysiological data observed in rodent V1 showed that the effect of visual experience on direction selectivity was similar to that observed in monkey data and provided strong validation of asymmetric changes of feedforward and recurrent synaptic strengths inferred from monkey data.  This may suggest a general learning principle underlying the same computation, such as familiarity detection across different features represented in different brain regions.

Adrienne Fairhall

University of Washington

March, 31, 2021

TBA

TBA

Yonatan Loewenstein

The Hebrew University 

April, 7, 2021

TBA

TBA

Claudia Clopath

Imperial College London

April, 14, 2021

TBA

TBA

John Rinzel

New York University

April, 21, 2021

TBA

TBA

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Ilana Witten

CNRS, Paris

April, 28, 2021

TBA

TBA

Tatiana Engel

Cold Spring Harbor Lab

May,12, 2021

TBA

TBA

Ann Hermunstad

Janelia Research Campus

May, 5, 2021

TBA

TBA

Chengcheng Huang

University of Pittsburgh

May 19, 2021

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TBA

TBA

Ran Darshan

Janelia Research Campus 

May 26, 2021

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TBA

TBA

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Lai-Sang Young

Courant Institute

June, 2, 2021

TBA

TBA

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Ken Miller

Columbia University

June, 9, 2021

TBA

TBA

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Vincent Hakim

CNRS, Paris

June, 16, 2021

TBA

TBA

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Carina Curto

The Pennsylvania State University

June, 23, 2021

TBA

TBA

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Stephen Coombes

The University of Nottingham

June, 30, 2021

TBA

TBA

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Maneesh Sahani

UCL, London

July, 7, 2021

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TBA

TBA

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Cristina Savin

New York University

July, 14, 2021

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TBA

TBA