Cerebral Cortex · 2004 · 494 citations · 45 references
Life SciencesNeurobiologyNeuroscienceComputational NeuroscienceSynaptic PlasticitySystems NeuroscienceNeural Circuit DynamicsNeural Circuit FormationNeural PlasticityNeuronal DynamicsShort-term Synaptic PlasticitySpike-timing-dependent PlasticityCerebral Cortex OrganizationCortical DynamicsNeural Firing PatternsNeuronal Firing PatternsNeuronal Network DynamicsSpike Train DynamicsSpike-timing PlasticitySpiking Neural Network Models
The study simulated a cortical‑inspired neuronal network to investigate how spiking neurons self‑organize into functional groups. The model comprised 100,000 reentrantly connected neurons with realistic firing patterns, receptor kinetics, short‑term plasticity, long‑term STDP, and distributed conduction delays, enabling millisecond‑resolution analysis of group dynamics and properties such as spatial distribution, size, growth, birth rate, lifespan, and resilience to synaptic turnover. The interaction of STDP and conduction delays spontaneously generated neuronal groups that fired time‑locked spikes with millisecond precision, and localized coherent input caused receptive and projective field shifts comparable to in vivo observations.
A neuronal network inspired by the anatomy of the cerebral cortex was simulated to study the self-organization of spiking neurons into neuronal groups. The network consisted of 100 000 reentrantly interconnected neurons exhibiting known types of cortical firing patterns, receptor kinetics, short-term plasticity and long-term spike-timing-dependent plasticity (STDP), as well as a distribution of axonal conduction delays. The dynamics of the network allowed us to study the fine temporal structure of emerging firing patterns with millisecond resolution. We found that the interplay between STDP and conduction delays gave rise to the spontaneous formation of neuronal groups — sets of strongly connected neurons capable of firing time-locked, although not necessarily synchronous, spikes. Despite the noise present in the model, such groups repeatedly generated patterns of activity with millisecond spike-timing precision. Exploration of the model allowed us to characterize various group properties, including spatial distribution, size, growth, rate of birth, lifespan, and persistence in the presence of synaptic turnover. Localized coherent input resulted in shifts of receptive and projective fields in the model similar to those observed in vivo.
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Simple model of spiking neurons
Eugene M. Izhikevich · IEEE Transactions on Neural Networks · 2003 · 4.8K citations
Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex
Joshua T. Trachtenberg, Brian E. Chen, Graham Knott et al. · Nature · 2002 · 1.9K citations