Speaker：Prof. Konrad K?rding, 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(jì)算神經(jīng)科學(xué) ，通過(guò)數(shù)據(jù)來(lái)研究大腦的運(yùn)作方式。早期研究關(guān)注 感知和運(yùn)動(dòng)方面，近年來(lái)從數(shù)據(jù)科學(xué)出發(fā)，在大腦功能、深度學(xué)習(xí)、個(gè)性化 醫(yī)療等諸多領(lǐng)域開(kāi)展研究包括逆向工程完整神經(jīng)系統(tǒng)等新方向。同時(shí)K?rding教授是開(kāi)放科學(xué)(open science)的主要推動(dòng)者之一，計(jì)算神經(jīng)科學(xué)的在線學(xué)校 Neuromatch的主要?jiǎng)?chuàng)立者。
2024-07-15