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2008 NIPS NeurIPS 2008

Tracking Changing Stimuli in Continuous Attractor Neural Networks

Abstract

Continuous attractor neural networks (CANNs) are emerging as promising models for describing the encoding of continuous stimuli in neural systems. Due to the translational invariance of their neuronal interactions, CANNs can hold a continuous family of neutrally stable states. In this study, we systematically explore how neutral stability of a CANN facilitates its tracking performance, a capacity believed to have wide applications in brain functions. We develop a perturbative approach that utilizes the dominant movement of the network stationary states in the state space. We quantify the distortions of the bump shape during tracking, and study their effects on the tracking performance. Results are obtained on the maximum speed for a moving stimulus to be trackable, and the reaction time to catch up an abrupt change in stimulus.

🌉 Interdisciplinary Bridge — Deep Learning and Interdisciplinary and Machine Learning
🧭 Keyword Pioneer — neutral stability
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Robotics, Speech & Audio
📈 Trend Setter — Neural Networks
🐣 Hot Topic Early Bird — neural network

Authors

K. Wong , Si Wu , Chi Fung

Topics

Machine Learning > Core Methods > Representation Learning Deep Learning > Architectures > Neural Networks Interdisciplinary > Cognitive Science > Cognitive Modeling Machine Learning > Learning Types > Representation Learning Deep Learning > Models > Neural Networks Machine Learning > Core Methods > Neural Networks Interdisciplinary > Science > Neuroscience

Keywords

neural dynamics neural encoding neutral stability stimulus tracking attractor dynamics neural population encoding continuous attractor continuous attractor neural network neural network perturbative approach
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