A descriptive analysis of olfactory sensation and memory in Drosophila and its relation to artificial neural networks

TL;DR

This paper analyzes Drosophila olfactory sensation and memory, highlighting principles relevant to designing shallow, highly interconnected artificial neural networks with fixed sensory representations and associative learning.

Contribution

It provides a descriptive analysis of insect olfactory coding and discusses its implications for artificial neural network design, based on a large dataset and literature review.

Findings

Networks are shallow and highly interconnected.

Communication often occurs within the same layer.

Learning involves forming associations with fixed sensory representations.

Abstract

This article provides a background and descriptive analysis of insect memory and the coding of olfactory sensation in Drosophila, presenting graphs and summary statistics from a large dataset of neurons and synapses that was recently made publicly available and also discussing findings from the existing empirical literature. Some general principles from Drosophila olfaction are discussed as they apply to the design of analogous systems in artificial neural networks: (1) the networks used for coding are shallow; (2) the level of connectedness varies widely across neurons in the same layer; (3) much communication is between neurons in the same layer; (4) in most olfactory learning, the manner in which sensory inputs are represented in stored memory is largely fixed, and the learning process involves developing positive or negative associations with existing categories of inputs.