Abstract
An important task performed by a neuron is the selection of relevant inputs from among thousands of synapses impinging on the dendritic tree. Synaptic plasticity enables this by strenghtening a subset of synapses that are, presumably, functionally relevant to the neuron. A different selection mechanism exploits the resonance of the dendritic membranes to preferentially filter synaptic inputs based on their temporal rates. A widely held view is that a neuron has one resonant frequency and thus can pass through one rate. Here we demonstrate through mathematical analyses and numerical simulations that dendritic resonance is inevitably a spatially distributed property; and therefore the resonance frequency varies along the dendrites, and thus endows neurons with a powerful spatiotemporal selection mechanism that is sensitive both to the dendritic location and the temporal structure of the incoming synaptic inputs.
Highlights
Neurons are constantly bombarded by thousands of synaptic inputs, so it is essential that neurons are able to listen selectively to subsets of these inputs
The first admittance, Geff ðvÞ, is an effective leak that is mostly associated with the classic membrane passive RC-circuit, and which acts as a shunt at high frequencies as Neurons are constantly bombarded by thousands of inputs
By dissecting the biophysical mechanism underlying neuronal resonance we find that neurons express a wide range of resonance frequencies spatially distributed along their dendrites
Summary
Neurons are constantly bombarded by thousands of synaptic inputs, so it is essential that neurons are able to listen selectively to subsets of these inputs. The focus of this study is the latter mechanism of resonance, membrane resonance, which has been traditionally considered a scalar property of a neuron: one neuron has one preferred resonance frequency [11,14] This view, is inconsistent with the increasing awareness of the complexity of dendritic ramifications, the non-uniform spatial distribution of their ionic channels and highly localized non-linearities. Such elaborate biophysics can endow single neurons with multiple resonances occuring at a wide range of frequencies and bandwidths, and enable neurons to act as multi-dimensional input classifiers. Our findings counter the widely-held assumption that input selection is based on a single prefered frequency band regardless the location of the synaptic input
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