- Poster presentation
- Open Access
Effective connectivity analysis explains metastable states of ongoing activity in cortically embedded systems of coupled synfire chains
© Trengove et al. 2015
- Published: 18 December 2015
- Ongoing Activity
- Effective Connectivity
- Emergent Pattern
- Synfire Chain
- Feedforward Connection
In models of the cortex, synaptic connectivity is often assumed to be random within broad constraints such as interlayer connection densities. Alternatively, the connectivity could include richly structured circuitry. A recent study  demonstrated this in a model of local cortex of order 1 mm3 in size: a large number of synfire chains (small pools of neurons sequentially linked by feedforward connections) activated by waves (sequences of propagating spike packets) were embedded in a recurrent network of excitatory and inhibitory neurons. The model exhibits stable global dynamics in the asynchronous irregular regime and stable propagation of multiple synfire waves. Background noise generated by waves destabilizes wave propagation, providing a negative feedback signal limiting their number.
The system is implemented both as a large-scale network of integrate-and-fire neurons and as a reduced model with binary-state pools as basic units. The reduced model exhibits activity patterns very similar to those of the full model and provides a valuable tool for studying the latter. We propose that the principle whereby activity patterns arise in concert with dynamically tuned effective connectivity applies to a broad class of networks with complex topologies.
Partially funded by Helmholtz portfolio theme SMHB and EU Grants 269921 (BrainScaleS) and 604102 (HBP).
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