Neural Comput - Sequential activity in asymmetrically coupled winner-take-all circuits.

Tópicos

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Resumo

Understanding the sequence generation and learning mechanisms used by recurrent neural networks in the nervous system is an important problem that has been studied extensively. However, most of the models proposed in the literature are either not compatible with neuroanatomy and neurophysiology experimental findings, or are not robust to noise and rely on fine tuning of the parameters. In this work, we propose a novel model of sequence learning and generation that is based on the interactions among multiple asymmetrically coupled winner-take-all (WTA) circuits. The network architecture is consistent with mammalian cortical connectivity data and uses realistic neuronal and synaptic dynamics that give rise to noise-robust patterns of sequential activity. The novel aspect of the network we propose lies in its ability to produce robust patterns of sequential activity that can be halted, resumed, and readily modulated by external input, and in its ability to make use of realistic plastic synapses to learn and reproduce the arbitrary input-imposed sequential patterns. Sequential activity takes the form of a single activity bump that stably propagates through multiple WTA circuits along one of a number of possible paths. Because the network can be configured to either generate spontaneous sequences or wait for external inputs to trigger a transition in the sequence, it provides the basis for creating state-dependent perception-action loops. We first analyze a rate-based approximation of the proposed spiking network to highlight the relevant features of the network dynamics and then show numerical simulation results with spiking neurons, realistic conductance-based synapses, and spike-timing dependent plasticity (STDP) rules to validate the rate-based model.

Resumo Limpo

understand sequenc generat learn mechan use recurr neural network nervous system import problem studi extens howev model propos literatur either compat neuroanatomi neurophysiolog experiment find robust nois reli fine tune paramet work propos novel model sequenc learn generat base interact among multipl asymmetr coupl winnertakeal wta circuit network architectur consist mammalian cortic connect data use realist neuron synapt dynam give rise noiserobust pattern sequenti activ novel aspect network propos lie abil produc robust pattern sequenti activ can halt resum readili modul extern input abil make use realist plastic synaps learn reproduc arbitrari inputimpos sequenti pattern sequenti activ take form singl activ bump stabli propag multipl wta circuit along one number possibl path network can configur either generat spontan sequenc wait extern input trigger transit sequenc provid basi creat statedepend perceptionact loop first analyz ratebas approxim propos spike network highlight relev featur network dynam show numer simul result spike neuron realist conductancebas synaps spiketim depend plastic stdp rule valid ratebas model

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