Reference library
Stimulus-Dependent Suppression of Chaos in Recurrent Neural Networks
Paper summary
Random continuous-time rate networks driven by periodic inputs with random neuronal phases can transition from chaotic background activity to entrained periodic motion. The critical drive amplitude depends nonmonotonically on frequency. Mean-field calculations are compared with network simulations; suppression can occur without simply saturating the entire network. Connectivity alone therefore does not fix the activity regime independently of input.
Relevance to the project
Assistant interpretation: motivates separately measuring endogenous dynamics under constant input and conditional stability under replayed inputs. It does not select F/H gains or show that the adopted adaptive rule stabilizes activity.
Fixed Gaussian synapses, large-network rate approximation, specified nonlinearity and periodic drive with phase diversity. No online synaptic adaptation, task-performance optimization or realistic spiking-circuit demonstration. Model frequency and amplitude thresholds are not transferable numerical defaults.
Source record
- Reference ID
- stimulus-chaos-suppression-2010
- Archived version
- arXiv:0912.3513v2 (2010), preprint associated with PRE82,011903
- Summary provenance
- reference-library/papers/stimulus-chaos-suppression-2010/metadata.json · summary
- Review status
- source-scoped reading; not replication
- Reading scope
- All 12 extracted PDF pages read, including main text 1–9 and references 10–12; no figure-by-figure visual audit or independent replication.