Neural Comput - Spine head calcium as a measure of summed postsynaptic activity for driving synaptic plasticity.

Tópicos

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Resumo

We use a computational model of a hippocampal CA1 pyramidal cell to demonstrate that spine head calcium provides an instantaneous readout at each synapse of the postsynaptic weighted sum of all presynaptic activity impinging on the cell. The form of the readout is equivalent to the functions of weighted, summed inputs used in neural network learning rules. Within a dendritic layer, peak spine head calcium levels are either a linear or sigmoidal function of the number of coactive synapses, with nonlinearity depending on the ability of voltage spread in the dendrites to reach calcium spike threshold. This is strongly controlled by the potassium A-type current, with calcium spikes and the consequent sigmoidal increase in peak spine head calcium present only when the A-channel density is low. Other membrane characteristics influence the gain of the relationship between peak calcium and the number of active synapses. In particular, increasing spine neck resistance increases the gain due to increased voltage responses to synaptic input in spine heads. Colocation of stimulated synapses on a single dendritic branch also increases the gain of the response. Input pathways cooperate: CA3 inputs to the proximal apical dendrites can strongly amplify peak calcium levels due to weak EC input to the distal dendrites, but not so strongly vice versa. CA3 inputs to the basal dendrites can boost calcium levels in the proximal apical dendrites, but the relative electrical compactness of the basal dendrites results in the reverse effect being less significant. These results give pointers as to how to better describe the contributions of pre- and postsynaptic activity in the learning "rules" that apply in these cells. The calcium signal is closer in form to the activity measures used in traditional neural network learning rules than to the spike times used in spike-timing-dependent plasticity.

Resumo Limpo

use comput model hippocamp ca pyramid cell demonstr spine head calcium provid instantan readout synaps postsynapt weight sum presynapt activ imping cell form readout equival function weight sum input use neural network learn rule within dendrit layer peak spine head calcium level either linear sigmoid function number coactiv synaps nonlinear depend abil voltag spread dendrit reach calcium spike threshold strong control potassium atyp current calcium spike consequ sigmoid increas peak spine head calcium present achannel densiti low membran characterist influenc gain relationship peak calcium number activ synaps particular increas spine neck resist increas gain due increas voltag respons synapt input spine head coloc stimul synaps singl dendrit branch also increas gain respons input pathway cooper ca input proxim apic dendrit can strong amplifi peak calcium level due weak ec input distal dendrit strong vice versa ca input basal dendrit can boost calcium level proxim apic dendrit relat electr compact basal dendrit result revers effect less signific result give pointer better describ contribut pre postsynapt activ learn rule appli cell calcium signal closer form activ measur use tradit neural network learn rule spike time use spiketimingdepend plastic

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