Spike-timing Dynamics of Neuronal Groups

Abstract

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.

Region-specific network plasticity in simulated and living cortical networks: comparison of the center of activity trajectory (CAT) with other statistics

“…Calcium imaging of cortical slices reveals reactivation of sequences of neurons, 'cortical songs', with distinct spatiotemporal structures over tens of seconds (Ikegaya et al 2004). Robust recurrent spike patterns were also found in a detailed cortical simulation (Izhikevich et al 2004) and in living slices (Fellous et al 2004). CAT provides a new and simple statistic to detect spatiotemporal patterns in networks and extends the previous studies by quantifiably demonstrating its ability to discern plasticity.…”

“…Briefly, 1000 leaky-integrateandfire (LIF) model neurons, with a total of 50 000 synapses, were placed randomly in a 3 mm by 3 mm area (see figure 1(c)). All synapses were frequency dependent (Markram et al 1998, Izhikevich et al 2004 to model synaptic depression. 70% of the neurons were excitatory, with spike-timing-dependent plasticity (STDP) (Song et al 2000).…”

Computação por assembleias neurais em redes neurais pulsadas.

“…Algumas simulações em redes neurais pulsadas têm inserido algum tipo de plasticidade de curto-prazo; por exemplo, em [90] usa-se o modelo desenvolvido por Markram e colaboradores, descrito acima; mas existem ainda os modelos de Abbott, de Tsodyks-Markram (ver [39]), além de outros. A questão que se coloca é como este tipo de plasticidade pode afetar computação por assembleias?…”

“…A atualização do peso sináptico wij ocorre no momento do disparo pós (potenciação: pré-então-pós) proporcional a xi(t), e também no momento do disparo pré (depreciação: pós-então-pré) proporcional a yj(t). Figuras adaptadas de [90] e [39].…”

“…No caso das assembleias, optou-se por modelagem, simulação e computação em tempo discreto, uma vez que é da natureza da computação proposta que os eventos sejam fenômenos discretos, por exemplo, o acontecimento de uma coalizão, a detecção de um padrão etc. Baseado em experiências e simulações relatadas na literatura [90], [91]), a melhor resolução temporal para o sistema é de 1 ms, que equivale ao menor evento a ocorrer na rede:…”