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Randomly-connected networks of integrate-and-fire (IF) neurons are known todisplay asynchronous irregular (AI) activity states, which resemble thedischarge activity recorded in the cerebral cortex of awake animals. However,it is not clear whether such activity states are specific to simple IF models,or if they also exist in networks where neurons are endowed with complexintrinsic properties similar to electrophysiological measurements. Here, weinvestigate the occurrence of AI states in networks of nonlinear IF neurons,such as the adaptive exponential IF (Brette-Gerstner-Izhikevich) model. Thismodel can display intrinsic properties such as low-threshold spike (LTS),regular spiking (RS) or fast-spiking (FS). We successively investigate theoscillatory and AI dynamics of thalamic, cortical and thalamocortical networksusing such models. AI states can be found in each case, sometimes withsurprisingly small network size of the order of a few tens of neurons. We showthat the presence of LTS neurons in cortex or in thalamus, explains the robustemergence of AI states for relatively small network sizes. Finally, weinvestigate the role of spike-frequency adaptation (SFA). In cortical networkswith strong SFA in RS cells, the AI state is transient, but when SFA isreduced, AI states can be self-sustained for long times. In thalamocorticalnetworks, AI states are found when the cortex is itself in an AI state, butwith strong SFA, the thalamocortical network displays Up and Down statetransitions, similar to intracellular recordings during slow-wave sleep oranesthesia. Self-sustained Up and Down states could also be generated bytwo-layer cortical networks with LTS cells. These models suggest that intrinsicproperties such as LTS are crucial for AI states in thalamocortical networks.

journal of computational neuroscience

Self-sustained asynchronous irregular states and Up/Down states in  thalamic, cortical and thalamocortical networks of nonlinear  integrate-and-fire neurons