Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning
Abstract
Sequential activity is a prominent feature of many neural systems, in multiple behavioral contexts. Here, we investigate how Hebbian rules lead to storage and recall of random sequences of inputs in both rate and spiking recurrent networks. In the case of the simplest (bilinear) rule, we characterize extensively the regions in parameter space that allow sequence retrieval and compute analytically the storage capacity of the network. We show that nonlinearities in the learning rule can lead to sparse sequences and find that sequences maintain robust decoding but display highly labile dynamics to continuous changes in the connectivity matrix, similar to recent observations in hippocampus and parietal cortex.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 11, 2020
- Source ID
- 10.1073/pnas.1918674117
Entities
People
- Maxwell Gillett
- Nicolas Brunel
- Ulises Pereira
Organizations
- Duke University
- Foundation for the National Institutes of Health
- Office of Naval Research
- University of Chicago