Speaker：Prof. Konrad Körding, Neuroscience, Bioengineering, University of Pennsylvania

Time：15:00-16:30, July 22, 2024

Venue：Room 1113， Wangkezhen Building

Host：Prof. Kunlin Wei

Abstract

Attention is a key component of the visual system, important for perception, learning, and memory. Attention can a lso be seen as a solution to the binding problem: concurrent attention to all parts of an entity allows separating it from the rest. However, the rich models of attention in computational neuroscience are generally not scaled to real-world problems, and there are many behavioral and neural phenomena that current models can not explain. Here, we propose a recurrent attention model inspired by modern neural networks for image segmentation. It conceptualizes recurrent connections as a multi-stage internal gating process where bottom-up connections transmit features while top-down and lateral connections transmit attentional gating signals. We find that our model can recognize and segment simple stimuli such as digits as well as objects in natural images and can be prompted with object labels, attributes or locations. It replicates a range of behavioral findings, such as object binding, selective attention, inhibition of return, and visual search. It also replicates a range of neural findings, including increased activity for attended objects, features, and locations, attention invariant tuning, and relatively late onset attention. The ability to focus on just parts of our stimulus streams is a key capability for visual cognition. This primitive could help artificial neural networks to explain brains and better separate entities in the world.

Bio

研究聚焦于计算神经科学 ，通过数据来研究大脑的运作方式。早期研究关注 感知和运动方面，近年来从数据科学出发，在大脑功能、深度学习、个性化 医疗等诸多领域开展研究包括逆向工程完整神经系统等新方向。同时Körding教授是开放科学(open science)的主要推动者之一，计算神经科学的在线学校 Neuromatch的主要创立者。