##### Department of Mathematics,

University of California San Diego

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### Optimization and Data Science Seminar

## Chengcheng Huang

#### University of Pittsburgh

## Propagation and modulation of information in visual pathway

##### Abstract:

How neuronal variability impacts neural codes is a central question in systems neuroscience, often with complex and model dependent answers. Most population models are parametric, with tacitly assumed structure of neuronal tuning and population variability. While these models provide key insights, they cannot inform how the physiology and circuit wiring of cortical networks impact information flow. In this work, we study information propagation in spatially ordered neuronal networks. We focus on the effects of feedforward and recurrent projection widths relative to columnar width, as well as attentional modulation. We show that narrower feedforward projection width increases the saturation rate of information. In contrast, the recurrent projection width with spatially balanced excitation and inhibition has small effects on information. Further, we show that attention improves information flow by suppressing the internal dynamics of the recurrent network.

Host: Jiawang Nie

### April 3, 2019

### 3:00 PM

### AP&M B412

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