title: Information filtering in resonant neurons.
abstract: Neuronal information transmission is frequency specific. In single cells, a band-pass like frequency preference can arise from the subthreshold dynamics of the membrane potential, shaped by properties of the cell's membrane and its ionic channels. In these cases, a cell is termed resonant and its membrane impedance spectrum exhibits a peak at non-vanishing frequencies. Here, we show that this frequency selectivity of neuronal response amplitudes need not translate into a similar frequency selectivity of information transfer. In particular, neurons with resonant but linear subthreshold voltage dynamics (without threshold) do not show a resonance of information transfer at the level of subthreshold voltage; the corresponding coherence has low-pass characteristics. Interestingly, we find that when combined with nonlinearities, subthreshold resonances do shape the frequency dependence of coherence and the peak in the subthreshold impedance translates to a peak in the coherence function. In other words, the nonlinearity inherent to spike generation allows a subthreshold impedance resonance to shape a resonance of voltage-based information transfer. We demonstrate such nonlinearity-mediated band-pass filtering of information at frequencies close to the subthreshold impedance resonance in three different model systems: the resonate-and-fire model, the conductance-based Morris-Lecar model, and linear resonant dynamics combined with a simple static nonlinearity. In the spiking neuron models, the band-pass filtering is most pronounced for low firing rates and a high variability of interspike intervals, similar to the spiking statistics observed in vivo. We show that band-pass filtering is achieved by reducing information transfer over low-frequency components and, consequently, comes along with an overall reduction of information rate. Our work highlights the crucial role of nonlinearities for the frequency dependence of neuronal information transmission.
authors.affiliation: Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.|Institute for Theoretical Biology and Physics Department, Humboldt-Universität zu Berlin, Berlin, Germany.
authors.forename: Sven
authors.initials: S
authors.lastname: Blankenburg
authors.affiliation: Physics Department, Humboldt-Universität zu Berlin, Berlin, Germany.
authors.forename: Wei
authors.initials: W
authors.lastname: Wu
authors.affiliation: Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.|Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, 230026, Anhui, China.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Lindner
authors.affiliation: Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany. s.schreiber@hu-berlin.de.|Institute for Theoretical Biology and Physics Department, Humboldt-Universität zu Berlin, Berlin, Germany. s.schreiber@hu-berlin.de.
authors.forename: Susanne
authors.initials: S
authors.lastname: Schreiber
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D007473:Ion Channels / Q000502:physiology; D055641:Mathematical Concepts; D008564:Membrane Potentials; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D017966:Pyramidal Cells / Q000502:physiology
chemical_list: D007473:Ion Channels
keywords: Coherence function; Frequency tuning; Information filtering; Resonate-and-fire models; Static nonlinearity