Computing with neural synchrony (Brette 2012)

"... In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. ..."

Model Type: Realistic Network

Model Concept(s): Synchronization; Simplified Models; Synaptic Plasticity; STDP; Rebound firing; Homeostasis; Reliability; Olfaction

Simulation Environment: Brian; Python

Implementer(s): Brette R


Brette R. (2012). Computing with neural synchrony. PLoS computational biology. 8 [PubMed]

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