Elsevier

Current Opinion in Neurobiology

Volume 25, April 2014, Pages 149-155
Current Opinion in Neurobiology

Single neuron dynamics and computation

https://doi.org/10.1016/j.conb.2014.01.005Get rights and content

Highlights

  • LNP models provide a precise description of the static and dynamic responses of neurons.

  • Dynamic responses depend on neuron electrophysiological properties and support various computations.

  • Various modes of dendritic input summation enrich neuron computational repertoires.

  • Synaptic dynamics further provides differential and frequency-dependent signalling.

Abstract

At the single neuron level, information processing involves the transformation of input spike trains into an appropriate output spike train. Building upon the classical view of a neuron as a threshold device, models have been developed in recent years that take into account the diverse electrophysiological make-up of neurons and accurately describe their input-output relations. Here, we review these recent advances and survey the computational roles that they have uncovered for various electrophysiological properties, for dendritic arbor anatomy as well as for short-term synaptic plasticity.

Introduction

The computation performed by single neurons can be defined as a mapping from afferent spike trains to the output spike train which is communicated to their postsynaptic targets. This mapping is stochastic, because of various sources of noise that include channel and synaptic noise; and plastic, because of various sources of plasticity, both intrinsic and synaptic.

For many years, the dominant conceptual model for single neuron computation was the binary Mc-Culloch-Pitts neuron [45]. In this model, the input vector is multiplied by a weight vector, and then passed through a threshold (see Fig. 1a). Adjusting synaptic weights and thresholds lead to neurons being able to learn arbitrary linearly separable dichotomies of the space of inputs [63].

This model has been conceptually tremendously useful, but it ignores fundamental temporal and spatial properties of neurons: the complex dynamics generated by a panoply of voltage-gated ionic currents; and the fact that synaptic inputs are stochastic, history-dependent and spread over a large dendritic tree. In this paper, we will review recent advances in our understanding of how these properties affect computation in single neurons.

Section snippets

Computation and dynamics: LNP/GL models and their relationship to neuronal biophysics

Electrophysiological data in various sensory systems have been successfully fitted by linear-non-linear-Poisson (LNP) or generalized linear models (GLM) [65]. In the LNP model, the inputs are first convolved linearly with a temporal filter (also called a kernel - the L operation). This convolution is then passed through a static non-linearity (the N operation), yielding an instantaneous firing rate. Finally, an inhomogeneous Poisson process is generated from the instantaneous firing rate (the P

Impact of dendritic non-linearities on computation

Dendritic trees are highly complex structures allowing for computations that are richer than mere linear summation 39, 9, 67, 42. Qualitatively, four different types of behavior can arise at the level of local dendritic branches, shown schematically in Figure 2:

  • (i)

    Sub-linear summation due to passive cable properties of thin dendrites has been observed in cerebellar stellate cells [3], which could allow these cells to be selective to sparse, rather than focused, presynaptic activity;

  • (ii)

    Linear

Synaptic computation and filtering

The dynamics of synaptic transmission lead to a form of pre-post cell-class specific short-term plasticity that shapes amplitudes of successive post-synaptic potentials (PSPs). This history dependence of the synaptic response (Fig. 3) can be characterised as exhibiting either: depression in which the successive synaptic amplitudes decrease due to depletion of presynaptic resources such as neurotransmitter vesicles that take a finite time - of the order of 100s of milliseconds - to replace; or

Acknowledgements

We thank Dr Gilad Silberberg for use of the data in Figure 3.

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