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

研究聚焦于計算神經科學 ，通過數據來研究大腦的運作方式。早期研究關注 感知和運動方面，近年來從數據科學出發，在大腦功能、深度學習、個性化 醫療等諸多領域開展研究包括逆向工程完整神經系統等新方向。同時Körding教授是開放科學(open science)的主要推動者之一，計算神經科學的在線學校 Neuromatch的主要創立者。