Multi state neurons

TL;DR

This paper explores the concept of multi-state neurons, highlighting their biological basis, potential advantages over binary neurons, and possible applications in artificial neural networks for more efficient and controllable learning.

Contribution

It introduces the idea of multi-state neurons, discusses their biological functions, and proposes their potential benefits for neural computation and artificial intelligence.

Findings

Multi-state neurons can dynamically change states over seconds or minutes.

They enable selective de-activation of synapses, shaping neural networks.

Potential for more efficient and scalable neural network designs.

Abstract

Neurons, as eukaryotic cells, have powerful internal computation capabilities. One neuron can have many distinct states, and brains can use this capability. Processes of neuron growth and maintenance use chemical signalling between cell bodies and synapses, ferrying chemical messengers over microtubules and actin fibres within cells. These processes are computations which, while slower than neural electrical signalling, could allow any neuron to change its state over intervals of seconds or minutes. Based on its state, a single neuron can selectively de-activate some of its synapses, sculpting a dynamic neural net from the static neural connections of the brain. Without this dynamic selection, the static neural networks in brains are too amorphous and dilute to do the computations of neural cognitive models. The use of multi-state neurons in animal brains is illustrated in hierarchical Bayesian object recognition. Multi-state neurons may support a design which is more efficient than two-state neurons, and scales better as object complexity increases. Brains could have evolved to use multi-state neurons. Multi-state neurons could be used in artificial neural networks, to use a kind of non-Hebbian learning which is faster and more focused and controllable than traditional neural net learning. This possibility has not yet been explored in computational models.