Log In Sign Up

Short-term synaptic plasticity optimally models continuous environments

by   Timoleon Moraitis, et al.

Biological neural networks operate with extraordinary energy efficiency, owing to properties such as spike-based communication and synaptic plasticity driven by local activity. When emulated in silico, such properties also enable highly energy-efficient machine learning and inference systems. However, it is unclear whether these mechanisms only trade off performance for efficiency or rather they are partly responsible for the superiority of biological intelligence. Here, we first address this theoretically, proving rigorously that indeed the optimal prediction and inference of randomly but continuously transforming environments, a common natural setting, relies on adaptivity through short-term spike-timing dependent plasticity, a hallmark of biological neural networks. Secondly, we assess this theoretical optimality via simulations and also demonstrate improved artificial intelligence (AI). For the first time, a largely biologically modelled spiking neural network (SNN) surpasses state-of-the-art artificial neural networks (ANNs) in all relevant aspects, in an example task of recognizing video frames transformed by moving occlusions. The SNN recognizes the frames more accurately, even if trained on few, still, and untransformed images, with unsupervised and synaptically-local learning, binary spikes, and a single layer of neurons - all in contrast to the deep-learning-trained ANNs. These results indicate that on-line adaptivity and spike-based computation may optimize natural intelligence for natural environments. Moreover, this expands the goal of exploiting biological neuro-synaptic properties for AI, from mere efficiency, to computational supremacy altogether.


page 1

page 2

page 3

page 4

page 5

page 19

page 20

page 21


Spiking neurons with short-term synaptic plasticity form superior generative networks

Spiking networks that perform probabilistic inference have been proposed...

Spike-Timing-Dependent Inference of Synaptic Weights

A potential solution to the weight transport problem, which questions th...

From Biological Synapses to Intelligent Robots

This review explores biologically inspired learning as a model for intel...

Synaptic Plasticity Dynamics for Deep Continuous Local Learning

A growing body of work underlines striking similarities between spiking ...

Spiking Neural Predictive Coding for Continual Learning from Data Streams

For energy-efficient computation in specialized neuromorphic hardware, w...

Making a Spiking Net Work: Robust brain-like unsupervised machine learning

The surge in interest in Artificial Intelligence (AI) over the past deca...

An Adaptive Synaptic Array using Fowler-Nordheim Dynamic Analog Memory

In this paper we present a synaptic array that uses dynamical states to ...