Action Potential Onset Dynamics and the Response Speed of Neuronal Populations
- 1 June 2005
- journal article
- research article
- Published by Springer Nature in Journal of Computational Neuroscience
- Vol. 18 (3), 297-309
- https://doi.org/10.1007/s10827-005-0329-8
Abstract
The result of computational operations performed at the single cell level are coded into sequences of action potentials (APs). In the cerebral cortex, due to its columnar organization, large number of neurons are involved in any individual processing task. It is therefore important to understand how the properties of coding at the level of neuronal populations are determined by the dynamics of single neuron AP generation. Here, we analyze how the AP generating mechanism determines the speed with which an ensemble of neurons can represent transient stochastic input signals. We analyze a generalization of the θ-neuron, the normal form of the dynamics of Type-I excitable membranes. Using a novel sparse matrix representation of the Fokker-Planck equation, which describes the ensemble dynamics, we calculate the transmission functions for small modulations of the mean current and noise noise amplitude. In the high-frequency limit the transmission function decays as ω−γ, where γ surprisingly depends on the phase θ s at which APs are emitted. If at θ s the dynamics is insensitive to external inputs, the transmission function decays as (i) ω−3 for the case of a modulation of a white noise input and as (ii) ω−2 for a modulation of the mean input current in the presence of a correlated and uncorrelated noise as well as (iii) in the case of a modulated amplitude of a correlated noise input. If the insensitivity condition is lifted, the transmission function always decays as ω−1, as in conductance based neuron models. In a physiologically plausible regime up to 1 kHz the typical response speed is, however, independent of the high-frequency limit and is set by the rapidness of the AP onset, as revealed by the full transmission function. In this regime modulations of the noise amplitude can be transmitted faithfully up to much higher frequencies than modulations in the mean input current. We finally show that the linear response approach used is valid for a large regime of stimulus amplitudes.Keywords
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This publication has 37 references indexed in Scilit:
- Which Model to Use for Cortical Spiking Neurons?IEEE Transactions on Neural Networks, 2004
- Dynamics of Population Rate Codes in Ensembles of Neocortical NeuronsJournal of Neurophysiology, 2004
- A Universal Model for Spike-Frequency AdaptationNeural Computation, 2003
- Firing Rate of the Noisy Quadratic Integrate-and-Fire NeuronNeural Computation, 2003
- Neocortical Pyramidal Cells Respond as Integrate-and-Fire Neurons to In Vivo–Like Input CurrentsJournal of Neurophysiology, 2003
- Analytic Expressions for Rate and CV of a Type I Neuron Driven by White Gaussian NoiseNeural Computation, 2003
- Dynamics of the Firing Probability of Noisy Integrate-and-Fire NeuronsNeural Computation, 2002
- Frequency Plateaus in a Chain of Weakly Coupled Oscillators, I.SIAM Journal on Mathematical Analysis, 1984
- Isochrons and phaseless setsJournal of Mathematical Biology, 1975
- Biological rhythms and the behavior of populations of coupled oscillatorsJournal of Theoretical Biology, 1967