title: History-dependent excitability as a single-cell substrate of transient memory for information discrimination.
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.
authors.affiliation: Grupo de Neurocomputacion Biologica, Departamento de Ingeniería Informatica, Escuela Politecnica Superior, Universidad Autonoma de Madrid, Madrid, Spain. fabiano.baroni@uam.es
authors.forename: Fabiano
authors.initials: F
authors.lastname: Baroni
authors.forename: Joaquín J
authors.initials: JJ
authors.lastname: Torres
authors.forename: Pablo
authors.initials: P
authors.lastname: Varona
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001703:Biophysics / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D008568:Memory; D008959:Models, Neurological; D008962:Models, Theoretical; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000378:metabolism / Q000502:physiology*; D009991:Oscillometry / Q000379:methods; D013997:Time Factors