huang.png

 

Chengcheng Huang

University of Pittsburgh

May 19, 2021

Neuronal variability and spatiotemporal dynamics

in cortical network models 

Neuronal variability is a reflection of recurrent circuitry and cellular physiology. The modulation of neuronal variability is a reliable signature of cognitive and processing state. A pervasive yet puzzling feature of cortical circuits is that despite their complex wiring, population-wide shared spiking variability is low dimensional with all neurons fluctuating en masse. We show that the spatiotemporal dynamics in a spatially structured network produce large population-wide shared variability.  When the spatial and temporal scales of inhibitory coupling match known physiology, model spiking neurons naturally generate low dimensional shared variability that captures in vivo population recordings along the visual pathway. Further, we show that firing rate models with spatial coupling can also generate chaotic and low-dimensional rate dynamics. The chaotic parameter region expands when the network is driven by correlated noisy inputs, while being insensitive to the intensity of independent noise.  

darshan.jpeg

 

Ran Darshan

Janelia Research Campus 

May 26, 2021

TBA

Young_Lai_Sang.jpg

Lai-Sang Young

Courant Institute

June, 2, 2021

A dynamical model of the visual cortex

In the past several years, I have been involved in building a biologically realistic model of the monkey visual cortex. Work on one of the input layers (4Ca) of the primary visual cortex (V1) is now nearly complete, and I would like to share some of what I have learned with the community. After a brief overview of the model and its capabilities, I would like to focus on three sets of results that represent three different aspects of the modeling. They are: (i) emergent E-I dynamics in local circuits; (ii) how visual cortical neurons acquire their ability to detect edges and directions of motion, and (iii) a view across the cortical surface: nonequilibrium steady states (in analogy with statistical mechanics) and beyond.

kenmiller.jpg

Ken Miller

Columbia University

June, 9, 2021

TBA

TBA

hakim.jpg

Vincent Hakim

CNRS, Paris

June, 16, 2021

TBA

TBA

curto.jpg

Carina Curto

The Pennsylvania State University

June, 23, 2021

TBA

TBA

coombes.jpeg

Stephen Coombes

The University of Nottingham

June, 30, 2021

TBA

TBA

A geometric framework to predict structure from function

in neural networks

Maneesh Sahani

UCL, London

July, 7, 2021

TBA

TBA

Cristina Savin

New York University

July, 14, 2021

TBA

TBA

Watch the talk on YouTube