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

By Simon Schug, Frederik Benzing, Angelika Steger. 2021

20 citations recorded by the source corpus.

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.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Liyuan Wang
2. Qian Li
3. Yi Zhong
4. Jun Zhu
5. Xingxing Zhang
6. Hang Su
7. Mingtian Zhang
8. Xuchen Zhang

In Cognitive Neuroscience

1. Qian Li
2. Liyuan Wang
3. Jun Zhu
4. Mingtian Zhang
5. Xingxing Zhang
6. Hang Su
7. Yi Zhong

In Electrical and Electronic Engineering

1. Liyuan Wang
2. Jun Zhu
3. Hang Su
4. Xingxing Zhang

Citations in the corpus

• Incorporating neuro-inspired adaptability for continual learning in artificial intelligence (2023, methodology): “Here we formulate this process with the framework of Bayesian learning, which has been hypothesized to well model biological synaptic plasticity by tracking the probability distribution of synaptic weights under dynamic sensory inputs [42, 43].”