Neural Comput - Frequency selectivity emerging from spike-timing-dependent plasticity.

Tópicos

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Resumo

Periodic neuronal activity has been observed in various areas of the brain, from lower sensory to higher cortical levels. Specific frequency components contained in this periodic activity can be identified by a neuronal circuit that behaves as a bandpass filter with given preferred frequency, or best modulation frequency (BMF). For BMFs typically ranging from 10 to 200?Hz, a plausible and minimal configuration consists of a single neuron with adjusted excitatory and inhibitory synaptic connections. The emergence, however, of such a neuronal circuitry is still unclear. In this letter, we demonstrate how spike-timing-dependent plasticity (STDP) can give rise to frequency-dependent learning, thus leading to an input selectivity that enables frequency identification. We use an in-depth mathematical analysis of the learning dynamics in a population of plastic inhibitory connections. These provide inhomogeneous postsynaptic responses that depend on their dendritic location. We find that synaptic delays play a crucial role in organizing the weight specialization induced by STDP. Under suitable conditions on the synaptic delays and postsynaptic potentials (PSPs), the BMF of a neuron after learning can match the training frequency. In particular, proximal (distal) synapses with shorter (longer) dendritic delay and somatically measured PSP time constants respond better to higher (lower) frequencies. As a result, the neuron will respond maximally to any stimulating frequency (in a given range) with which it has been trained in an unsupervised manner. The model predicts that synapses responding to a given BMF form clusters on dendritic branches.

Resumo Limpo

period neuron activ observ various area brain lower sensori higher cortic level specif frequenc compon contain period activ can identifi neuron circuit behav bandpass filter given prefer frequenc best modul frequenc bmf bmfs typic rang hz plausibl minim configur consist singl neuron adjust excitatori inhibitori synapt connect emerg howev neuron circuitri still unclear letter demonstr spiketimingdepend plastic stdp can give rise frequencydepend learn thus lead input select enabl frequenc identif use indepth mathemat analysi learn dynam popul plastic inhibitori connect provid inhomogen postsynapt respons depend dendrit locat find synapt delay play crucial role organ weight special induc stdp suitabl condit synapt delay postsynapt potenti psps bmf neuron learn can match train frequenc particular proxim distal synaps shorter longer dendrit delay somat measur psp time constant respond better higher lower frequenc result neuron will respond maxim stimul frequenc given rang train unsupervis manner model predict synaps respond given bmf form cluster dendrit branch

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