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

Manuel Beiran,Srdjan Ostojic,Peter E Latham

doi:10.1371/journal.pcbi.1006893

Abstract

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.

Highlights

Adaptive behavior requires processing information over a vast span of timescales [1], ranging from micro-seconds for acoustic localisation [2], milliseconds for detecting changes in the visual field [3], seconds for evidence integration [4] and working memory [5], to hours, days or years in the case of long-term memory
We explore the possibility that slow network dynamics arise from such slow biophysical processes
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

Summary

Introduction

Adaptive behavior requires processing information over a vast span of timescales [1], ranging from micro-seconds for acoustic localisation [2], milliseconds for detecting changes in the visual field [3], seconds for evidence integration [4] and working memory [5], to hours, days or years in the case of long-term memory. The first class relies on non-linear collective dynamics that emerge from synaptic interactions between neurons in the local network Such mechanisms have been proposed to model a variety of phenomena that include working memory [9], decision-making [10] and slow variability in the cortex [11]. In those models, long timescales emerge close to bifurcations between different types of dynamical states, and typically rely on the fine tuning of some parameter [12]. How the timescales of these internal processes affect the timescales of activity at the network level has not been fully explored

Methods

Results

Discussion

Conclusion