Spike frequency adaptation supports network computations on temporally dispersed information.

Darjan Salaj,Ceca Kraisnikovic,Anand Subramoney,Wolfgang Maass,Guillaume Bellec,Robert Legenstein

doi:10.7554/elife.65459

Abstract

For solving tasks such as recognizing a song, answering a question, or inverting a sequence of symbols, cortical microcircuits need to integrate and manipulate information that was dispersed over time during the preceding seconds. Creating biologically realistic models for the underlying computations, especially with spiking neurons and for behaviorally relevant integration time spans, is notoriously difficult. We examine the role of spike frequency adaptation in such computations and find that it has a surprisingly large impact. The inclusion of this well-known property of a substantial fraction of neurons in the neocortex - especially in higher areas of the human neocortex - moves the performance of spiking neural network models for computations on network inputs that are temporally dispersed from a fairly low level up to the performance level of the human brain.

Highlights

Since brains have to operate in dynamic environments and during ego-motion, neural networks of the brain need to be able to solve ‘temporal computing tasks’, that is, tasks that require integration and manipulation of temporally dispersed information from continuous input streams on the behavioral time scale of seconds
We found that an spiking neural networks (SNNs) consisting of 500 neurons with spike frequency adaptation (SFA), whose adaptive firing threshold had a time constant of t a 1⁄4 800 ms, was able to solve this task with an accuracy above 99% and average firing activity of 13.90 ± 8.76 Hz
The simplest type of temporal computing task just requires to hold one item, which typically can be characterized by a single bit, during a delay in a working memory, until it is needed for a behavioral response. This can be modeled in neural networks by creating an attractor in the network dynamics that retains this bit of information through persistent firing during the delay

Summary

Introduction

Since brains have to operate in dynamic environments and during ego-motion, neural networks of the brain need to be able to solve ‘temporal computing tasks’, that is, tasks that require integration and manipulation of temporally dispersed information from continuous input streams on the behavioral time scale of seconds. It is well known that biological neurons and synapses are subject to a host of slower dynamic processes, but it has remained unclear whether any of these can be recruited for robust temporal computation on the time scale of seconds. A rigorous survey of time constants of SFA is still missing, the available experimental data show that SFA does produce history dependence of neural firing on the time scale of seconds, up to 20 s according to Pozzorini et al, 2013, Pozzorini et al, 2015. The biophysical mechanisms behind SFA include inactivation of depolarizing currents and the activity-dependent activation of slow hyperpolarizing or shunting currents (Gutkin and Zeldenrust, 2014; Benda and Herz, 2003)

Methods

Results

Conclusion