Response classes of spike sequence processing

Abstract

Event Abstract Back to Event Response classes of spike sequence processing Hinrich Kielblock1, 2*, Jannes Gladrow1, Lena Ricarda Happ1, Birk Urmersbach1, Shuwen Chang3, Holger Taschenberger3 and Marc Timme1, 2 1 Max Planck Institute for Dynamics and Self-Organization, Network Dynamics Group, Germany 2 Berstein Center for Computational Neuroscience Göttingen, Germany 3 Max Planck Institute for Biophysical Chemistry, Membrane Biophysics Department, Germany The response properties of single neurons to inputs is an essential feature underlying the dynamics of neural networks. Usually, a neuron's response properties are characterized with regard to continuous input currents determining the neuron's mean output frequency in dependence of the mean input. The resulting response curve is always monotonically increasing with the input. However, in neural networks the typical input arrives at a neuron in the form of distinct pulses. We have shown recently that the response of neurons to regular spike sequences can be very different from the response to continuous input-currents [1], in particular leading to non-monotonic input-output relations. Here, we provide a classification of the generic responses and find five dynamical classes. This may help understand the underlying network dynamics in systems exhibiting regular spike sequences such as e.g. central pattern generators. Acknowledgements Partially supported by the BMBF Germany under grant no. 01GQ1005B and by a grant from the Max Planck Society to Marc Timme.