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Polychronization: Computation with Spikes

Neural Computation · 2005 · Vol. 18(2) · pp. 245–282
Eugene M. Izhikevich

Abstract

We present a minimal spiking network that can polychronize, that is, exhibit reproducible time-locked but not synchronous firing patterns with millisecond precision, as in synfire braids. The network consists of cortical spiking neurons with axonal conduction delays and spike-timing-dependent plasticity (STDP); a ready-to-use MATLAB code is included. It exhibits sleeplike oscillations, gamma (40 Hz) rhythms, conversion of firing rates to spike timings, and other interesting regimes. Due to the interplay between the delays and STDP, the spiking neurons spontaneously self-organize into groups and generate patterns of stereotypical polychronous activity. To our surprise, the number of coexisting polychronous groups far exceeds the number of neurons in the network, resulting in an unprecedented memory capacity of the system. We speculate on the significance of polychrony to the theory of neuronal group selection (TNGS, neural Darwinism), cognitive neural computations, binding and gamma rhythm, mechanisms of attention, and consciousness as "attention to memories."

Neural dynamics and brain functionAdvanced Memory and Neural ComputingPhotoreceptor and optogenetics researchComputer scienceMillisecondSpike-timing-dependent plasticityModels of neural computationSpiking neural networkNeuroscienceArtificial neural networkSpike (software development)SurpriseArtificial intelligence

MeSH terms

AttentionBrainMemoryModels, NeurologicalNeuronal PlasticityNeuronsNeural Networks, Computer
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