|
Information processing in neural circuits with multiple quasi-stable attractor states| old_uid | 19241 |
|---|
| title | Information processing in neural circuits with multiple quasi-stable attractor states |
|---|
| start_date | 2021/06/18 |
|---|
| schedule | 16h-17h |
|---|
| online | no |
|---|
| summary | Circuits containing clusters of neurons with strong within-cluster excitatory connections and random between-cluster connections can possess multiple activity states, with each state defined by the set of clusters with stably high versus low neural firing rates. Noise or adaptive processes can render the states quasi-stable such that in the presence of constant input, or following successive identical inputs, the network can progress through a sequence of different activity states. Here we study the properties of such models, including the role of finite size effects in producing multistability in networks with Gaussian random cross-coupling, and we assess the extent to which such state sequences might subserve information processing and behavior in a range of cognitive tasks. |
|---|
| responsibles | <not specified> |
|---|
| |
|