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How neuronal adaption shapes spiking and network dynamics

Josef Ladenbauer:

 

Many types of neurons exhibit spike rate adaptation, a gradual decrease in spiking activity following a sudden increase in stimulus intensity. This phenomenon is typically produced by slowly deactivating transmembrane potassium currents, which e ffectively inhibit neuronal responses and can be controlled by neuromodulators [1]. Here we examine (i) how these adaptation currents change the relationship between in-vivo like fluctuating synaptic input, spike rate output and the spike train statistics of single neurons and (ii) how they contribute to spike rate oscillations and resonance in recurrent networks of excitatory and inhibitory neurons. We consider (networks of) thresholdmodel neurons which include two types of adaptation currents and can well reproduce the activity of cortical neurons. To calculate the neuronal spike rates and inter-spike intervals we use a mean-fi eld approach based on the Fokker-Planck equation. We show that adaptation currents diff erentially change the neuronal response properties, depending on the type of current activation. Adaptation currents which are driven by the subthreshold membrane voltage increase the threshold for spiking and the inter-spike interval variability. Suprathreshold (spike-dependent) adaptation currents, on the other hand, decrease the spike rate gain and high spiking variability caused by strongly fluctuating inputs [2]. For recurrent networks we find that neuronal adaptation (i) can mediate slow oscillations for sufficiently strong recurrent synaptic excitation, (ii) promotes fast oscillations originating from recurrent excitation-inhibition loops and (iii) ampli fies the network response to oscillatory external inputs for a narrow band of frequencies [3]. Our results therefore identify the diff erent roles of adaptation currents for controlling neuronal response properties and rhythms in recurrent networks.

 

[1] D.A. McCormick: Progr. Neurobiol. 39:337-388 (1992)

[2] J. Ladenbauer, M. Augustin and K. Obermayer: (submitted)

[3] M. Augustin, J. Ladenbauer and K. Obermayer: Front. Comput. Neurosci. 7:9 (2013)

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