Neural Comput - Dynamical synapses enhance neural information processing: gracefulness, accuracy, and mobility.

Tópicos

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Resumo

Experimental data have revealed that neuronal connection efficacy exhibits two forms of short-term plasticity: short-term depression (STD) and short-term facilitation (STF). They have time constants residing between fast neural signaling and rapid learning and may serve as substrates for neural systems manipulating temporal information on relevant timescales. This study investigates the impact of STD and STF on the dynamics of continuous attractor neural networks and their potential roles in neural information processing. We find that STD endows the network with slow-decaying plateau behaviors: the network that is initially being stimulated to an active state decays to a silent state very slowly on the timescale of STD rather than on that of neuralsignaling. This provides a mechanism for neural systems to hold sensory memory easily and shut off persistent activities gracefully. With STF, we find that the network can hold a memory trace of external inputs in the facilitated neuronal interactions, which provides a way to stabilize the network response to noisy inputs, leading to improved accuracy in population decoding. Furthermore, we find that STD increases the mobility of the network states. The increased mobility enhances the tracking performance of the network in response to time-varying stimuli, leading to anticipative neural responses. In general, we find that STD and STP tend to have opposite effects on network dynamics and complementary computational advantages, suggesting that the brain may employ a strategy of weighting them differentially depending on the computational purpose.

Resumo Limpo

experiment data reveal neuron connect efficaci exhibit two form shortterm plastic shortterm depress std shortterm facilit stf time constant resid fast neural signal rapid learn may serv substrat neural system manipul tempor inform relev timescal studi investig impact std stf dynam continu attractor neural network potenti role neural inform process find std endow network slowdecay plateau behavior network initi stimul activ state decay silent state slowli timescal std rather neuralsign provid mechan neural system hold sensori memori easili shut persist activ grace stf find network can hold memori trace extern input facilit neuron interact provid way stabil network respons noisi input lead improv accuraci popul decod furthermor find std increas mobil network state increas mobil enhanc track perform network respons timevari stimuli lead anticip neural respons general find std stp tend opposit effect network dynam complementari comput advantag suggest brain may employ strategi weight differenti depend comput purpos

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