Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network
Abstract
Multiple brain regions are able to learn and express temporal sequences, and this functionality is an essential component of learning and memory. We propose a substrate for such representations via a network model that learns and recalls discrete sequences of variable order and duration. The model consists of a network of spiking neurons placed in a modular microcolumn based architecture. Learning is performed via a biophysically realistic learning rule that depends on synaptic ‘eligibility traces’. Before training, the network contains no memory of any particular sequence. After training, presentation of only the first element in that sequence is sufficient for the network to recall an entire learned representation of the sequence. An extended version of the model also demonstrates the ability to successfully learn and recall non-Markovian sequences. This model provides a possible framework for biologically plausible sequence learning and memory, in agreement with recent experimental results.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 18, 2021
- Source ID
- 10.7554/elife.63751
Entities
People
- Harel Shouval
- Ian Cone
Organizations
- National Institute of Biomedical Imaging and Bioengineering
- Office of Naval Research
- Rice University
- University of Texas at Austin