Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma

Angelika Steger,Simon Schug,Frederik Benzing

doi:10.7554/elife.69884

Abstract

When an action potential arrives at a synapse there is a large probability that no neurotransmitter is released. Surprisingly, simple computational models suggest that these synaptic failures enable information processing at lower metabolic costs. However, these models only consider information transmission at single synapses ignoring the remainder of the neural network as well as its overall computational goal. Here, we investigate how synaptic failures affect the energy efficiency of models of entire neural networks that solve a goal-driven task. We find that presynaptic stochasticity and plasticity improve energy efficiency and show that the network allocates most energy to a sparse subset of important synapses. We demonstrate that stabilising these synapses helps to alleviate the stability-plasticity dilemma, thus connecting a presynaptic notion of importance to a computational role in lifelong learning. Overall, our findings present a set of hypotheses for how presynaptic plasticity and stochasticity contribute to sparsity, energy efficiency and improved trade-offs in the stability-plasticity dilemma.

Highlights

It has long been known that synaptic signal transmission is stochastic (del Castillo and Katz, 1954)
This approach has proven fruitful to explain synaptic failures (Levy and Baxter, 2002; Harris et al, 2012), low average firing rates (Levy and Baxter, 1996) as well as excitation-inhibition balance (Sengupta et al, 2013) and is supported by fascinating experimental evidence suggesting that both presynaptic glutamate release (Savtchenko et al, 2013) and postsynaptic channel properties (Harris et al, 2015, 2019) are tuned to maximise information transmission per energy
While there are various sources of stochasticity in synapses, here, we focus on modelling synaptic failures where action potentials at the presynapse fail to trigger any postsynaptic depolarisation

Summary

Introduction

It has long been known that synaptic signal transmission is stochastic (del Castillo and Katz, 1954). One important line of work proposes that individual synapses do not merely maximise information transmission, but rather take into account metabolic costs, maximising the information transmitted per unit of energy (Levy and Baxter, 1996). This approach has proven fruitful to explain synaptic failures (Levy and Baxter, 2002; Harris et al, 2012), low average firing rates (Levy and Baxter, 1996) as well as excitation-inhibition balance (Sengupta et al, 2013) and is supported by fascinating experimental evidence suggesting that both presynaptic glutamate release (Savtchenko et al, 2013) and postsynaptic channel properties (Harris et al, 2015, 2019) are tuned to maximise information transmission per energy

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