Neural Comput - Neuronal assembly dynamics in supervised and unsupervised learning scenarios.

Tópicos

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Resumo

The dynamic formation of groups of neurons--neuronal assemblies--is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system's variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions.

Resumo Limpo

dynam format group neuronsneuron assembliesi believ mediat cognit phenomena mani level detail oper mechan interact still uncov one hypothesi suggest synchron oscil underpin format function focus tempor structur neuron signal context investig neuron assembl dynam two complementari scenario first supervis spike pattern classif task noisi variat collect spike correct label second unsupervis minim cognit evolutionari robot task evolv agent cope multipl possibl conflict object case tradit dynam analysi system variabl pair informationtheoret techniqu order get broader pictur ongo interact within network neural network model inspir kuramoto model coupl phase oscil allow one finetun network synchron dynam assembl configur experi explor comput power redund general capabl neuron circuit demonstr perform depend nonlinear number assembl neuron network show framework can exploit generat minim cognit behavior dynam assembl format account vari degre stimuli modul sensorimotor interact

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