Abstract
Neurons react differently to incoming stimuli depending upon their previous history of stimulation. This property can be considered as a single-cell substrate for transient memory, or context-dependent information processing: depending upon the current context that the neuron “sees” through the subset of the network impinging on it in the immediate past, the same synaptic event can evoke a postsynaptic spike or just a subthreshold depolarization. We propose a formal definition of History-Dependent Excitability (HDE) as a measure of the propensity to firing in any moment in time, linking the subthreshold history-dependent dynamics with spike generation. This definition allows the quantitative assessment of the intrinsic memory for different single-neuron dynamics and input statistics. We illustrate the concept of HDE by considering two general dynamical mechanisms: the passive behavior of an Integrate and Fire (IF) neuron, and the inductive behavior of a Generalized Integrate and Fire (GIF) neuron with subthreshold damped oscillations. This framework allows us to characterize the sensitivity of different model neurons to the detailed temporal structure of incoming stimuli. While a neuron with intrinsic oscillations discriminates equally well between input trains with the same or different frequency, a passive neuron discriminates better between inputs with different frequencies. This suggests that passive neurons are better suited to rate-based computation, while neurons with subthreshold oscillations are advantageous in a temporal coding scheme. We also address the influence of intrinsic properties in single-cell processing as a function of input statistics, and show that intrinsic oscillations enhance discrimination sensitivity at high input rates. Finally, we discuss how the recognition of these cell-specific discrimination properties might further our understanding of neuronal network computations and their relationships to the distribution and functional connectivity of different neuronal types.
Highlights
Since the beginnings of electrophysiology it has been observed that neuronal firing rate in sensory systems carries information about the presented stimulus [1,2]
In this paper we present a formal measure of the neuron’s state directly related to spike generation, namely the History-Dependent Excitability (HDE), which lumps the different historydependent dynamical variables of an arbitrary model neuron to a single, scalar value that describes its propensity to firing in any moment in time
History-Dependent Excitability (HDE) In the most general mathematical framework a neuron is described as a dynamical system: d~x dt
Summary
Since the beginnings of electrophysiology it has been observed that neuronal firing rate in sensory systems carries information about the presented stimulus [1,2]. In addition to electrophysiological studies based on a stimulusresponse paradigm, precisely timed spiking patterns have been observed in the spontaneous activity of a variety of preparations [7,8,9]. While these results might not apply universally in the nervous system [10,11,12], they are clearly advocating for an important role of precisely timed activity in neural network processing. The dynamical mechanisms underlying the sensitivity to temporally structured inputs are not completely understood
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