Large time step discrete-time modeling of sharp wave activity in hippocampal area CA3

Reduced models of neuronal spiking activity simulated with a fixed integration time step are frequently used in studies of spatio-temporal dynamics of neurobiological networks. The choice of fixed time step integration provides computational simplicity and efficiency, especially in cases dealing with large number of neurons and synapses operating at a different level of activity across the population at any given time. A network model tuned to generate a particular type of oscillations or wave patterns is sensitive to the intrinsic properties of neurons and synapses and, therefore, commonly susceptible to changes in the time step of integration. In this study, we analyzed a model of sharp-wave activity in the network of hippocampal area CA3, to examine how an increase of the integration time step affects network behavior and to propose adjustments of intrinsic properties of neurons and synapses that help minimize or remove the damage caused by the time step increase. Highlights Spiking models of neural network activity are sensitive to the integration step Larger integration time steps are preferable in simulating large networks Case study of CA3 sharp waves shows time step increase damages network dynamics Neuronal and synaptic parameters adjustments rescue the dynamics at large time step 1