P157 Non-monotonic Subthreshold Information Filtering in a Coupled Resonator-Integrator System
Franquelin Lambert1
1Université de Moncton, Département de physique et d'astronomie Introduction:Subthreshold dynamics play a key role in spike generation, and it is well-known that some neurons exhibit a frequency preference when integrating subthreshold input– so-called resonators [1,2]. It has been shown, however, that despite the existence of subthreshold resonance, a single resonator neuron exhibits low-pass, i.e., monotonic, information filtering(as measured by the spectral coherence). In other words, in the subthreshold regime, band-pass impedance does not translate to band-pass information filtering. Instead, nonlinearities, such as spiking dynamics, are needed to create band-pass information transfer [3,4].
Methods:Here, we study a similar question in an electrically coupled pair of neurons. Our goal is to evaluate whether this resonance profile imparts non-trivial information filtering capabilities to the coupled system. We numerically simulate an electrically coupled integrate-and-fire to resonate-and-fire system in the subthreshold regime, and we investigate the stimulus-response spectral coherence function of the system under perturbation by coloured noise (Ornstein-Uhlenbeck process).
Results:For electrical coupling between a resonator and an integrator, we show that a Fano-like resonance profile appears in the impedance, i.e., a narrow, asymmetric peak with anti-resonance [5]. Moreover, we observe that the coherence function is non-monotonic, with a minimum around the frequency of the opposite neuron.
Discussion:This challenges the claim that neurons require nonlinearities to relay bandpass information filtering properties. This new perspective places informationfiltering in the context of connection motifs where a small number of resonators and integrators interact, rather than the context of individual neurons.
Acknowledgements no acknowledgements References [1] Izhikevich, Eugene M.Dynamical systems in neuroscience. MIT press, 2007. [2]https://doi.org/10.1016/S0893-6080(01)00078-8 [3]https://doi.org/10.1109/TMBMC.2016.2618863 [4]https://doi.org/10.1007/s10827-015-0580-6
