Contrasting the effects of adaptation and synaptic filtering on the timescales of dynamics in recurrent networks

Neural activity in awake behaving animals exhibits a vast range of timescales that can be several fold larger than the membrane time constant of individual neurons. Two types of mechanisms have been proposed to explain this conundrum. One possibility is that large timescales are generated by a network mechanism based on positive feedback, but this hypothesis requires fine-tuning of the strength or structure of the synaptic connections. A second possibility is that large timescales in the neural dynamics are inherited from large timescales of underlying biophysical processes, two prominent candidates being intrinsic adaptive ionic currents and synaptic transmission. How the timescales of adaptation or synaptic transmission influence the timescale of the network dynamics has however not been fully explored. To address this question, here we analyze large networks of randomly connected excitatory and inhibitory units with additional degrees of freedom that correspond to adaptation or synaptic filtering. We determine the fixed points of the systems, their stability to perturbations and the corresponding dynamical timescales. Furthermore, we apply dynamical mean field theory to study the temporal statistics of the activity in the fluctuating regime, and examine how the adaptation and synaptic timescales transfer from individual units to the whole population. Our overarching finding is that synaptic filtering and adaptation in single neurons have very different effects at the network level. Unexpectedly, the macroscopic network dynamics do not inherit the large timescale present in adaptive currents. In contrast, the timescales of network activity increase proportionally to the time constant of the synaptic filter. Altogether, our study demonstrates that the timescales of different biophysical processes have different effects on the network level, so that the slow processes within individual neurons do not necessarily induce slow activity in large recurrent neural networks.Author summary: Brain activity spans a wide range of timescales, as it is required to interact in complex time-varying environments. However, individual neurons are primarily fast devices: their membrane time constant is of the order of a few tens of milliseconds. Yet, neurons are also subject to additional biophysical processes, such as adaptive currents or synaptic filtering, that introduce slower dynamics in the activity of individual neurons. In this study, we explore the possibility that slow network dynamics arise from such slow biophysical processes. To do so, we determine the different dynamical properties of large networks of randomly connected excitatory and inhibitory units which include an internal degree of freedom that corresponds to either adaptation or synaptic filtering. We show that the network dynamics do not inherit the slow timescale present in adaptive currents, while synaptic filtering is an efficient mechanism to scale down the timescale of the network activity.