Network Topology influences Synchronization and Intrinsic Read-out

In this note, we want to show (a) how the statistics of synaptic input influences the read-out of stored intrinsic properties for two-dimensional model neurons and (b) the effects of network topology on synchronization, i.e. the correlations of synaptic input generated in the network. This theoretical work relates to the biological properties of neuromodulation by presynaptic and postsynaptic effects on connectivity and intrinsic properties ([6]). We have previously found that highly synchronous inputs drives neurons, such that small differences in intrinsic properties disappear, while distributed inputs lets intrinsic properties determine output behavior ([5]). We choose a parametrizable two-dimensional neural oscillator model (similar to a Morris-Lecar model), to investigate the effects of changed network topology for larger collections of neurons. We focus on intrinsic properties that encode frequency-specificity. We suggest that presynaptic modulation can be an effective way of rapidly altering network topology. We investigate changes in network topology along the dimensions of small-world connectivity vs. random graph connectivity. We hypothesize that SW graphs produce more globally synchronized feedforward behavior with lower link density than comparable random graphs. In accordance with the hypothesis, we find that in a SW graph, because of highly synchronous inputs, the difference between neuronal intrinsic properties is minimized, while a random graph allows read out of neuronal intrinsic properties. Thus, altering network topology can alter the balance between intrinsically vs. synaptically driven determined network activity. Based on a presentation at the AMS Meeting on Dynamical Neuroscience, March 2005, Santa Barbara. Thanks to E. Izhikevich and R. Hecht-Nielsen for helpful discussions.