Abstract
Experimentally it is known that some neurons encode preferentially information about low-frequency (slow) components of a time-dependent stimulus while others prefer intermediate or high-frequency (fast) components. Accordingly, neurons can be categorized as low-pass, band-pass or high-pass information filters. Mechanisms of information filtering at the cellular and the network levels have been suggested. Here we propose yet another mechanism, based on noise shaping due to spontaneous non-renewal spiking statistics. We compare two integrate-and-fire models with threshold noise that differ solely in their interspike interval (ISI) correlations: the renewal model generates independent ISIs, whereas the non-renewal model exhibits positive correlations between adjacent ISIs. For these simplified neuron models we analytically calculate ISI density and power spectrum of the spontaneous spike train as well as approximations for input-output cross-spectrum and spike-train power spectrum in the presence of a broad-band Gaussian stimulus. This yields the spectral coherence, an approximate frequency-resolved measure of information transmission. We demonstrate that for low spiking variability the renewal model acts as a low-pass filter of information (coherence has a global maximum at zero frequency), whereas the non-renewal model displays a pronounced maximum of the coherence at non-vanishing frequency and thus can be regarded as a band-pass filter of information.
Highlights
Neurons encode time-dependent sensory signals in sequences of action potentials, so-called spike trains
Before we systematically explore the dependence of spectra and coherence on system parameters, let us highlight the main effect of positive interspike interval (ISI) correlations on signal transfer, i.e. the main difference between the renewal and the non-renewal models
The statistical distributions of threshold and reset were for both models chosen as an inverse Gaussian probability density, such that the resulting ISI density of the IF model has the form of an inverse Gaussian, a simple statistical distribution that fits the ISI density of some neurons surprisingly well [21, 17]
Summary
Neurons encode time-dependent sensory signals in sequences of action potentials, so-called spike trains. We explore how robust the band-pass filter effect is and how well the two theories can describe spectrum and coherence for different intrinsic variability (different CVs) and different values of the signal amplitude (different values of ε).
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
