A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data

TL;DR

This paper introduces a biologically plausible neural network model that learns the principal subspace of streaming data using Hebbian and anti-Hebbian rules derived from a multidimensional scaling cost function, capable of tracking nonstationary data.

Contribution

It derives a new neural network for subspace learning from a principled cost function, bridging the gap between biological plausibility and theoretical rigor.

Findings

Weights converge to the principal subspace in stationary data.

Network can track changes in nonstationary data.

Uses only local Hebbian and anti-Hebbian learning rules.

Abstract

Neural network models of early sensory processing typically reduce the dimensionality of streaming input data. Such networks learn the principal subspace, in the sense of principal component analysis (PCA), by adjusting synaptic weights according to activity-dependent learning rules. When derived from a principled cost function these rules are nonlocal and hence biologically implausible. At the same time, biologically plausible local rules have been postulated rather than derived from a principled cost function. Here, to bridge this gap, we derive a biologically plausible network for subspace learning on streaming data by minimizing a principled cost function. In a departure from previous work, where cost was quantified by the representation, or reconstruction, error, we adopt a multidimensional scaling (MDS) cost function for streaming data. The resulting algorithm relies only on biologically plausible Hebbian and anti-Hebbian local learning rules. In a stochastic setting, synaptic weights converge to a stationary state which projects the input data onto the principal subspace. If the data are generated by a nonstationary distribution, the network can track the principal subspace. Thus, our result makes a step towards an algorithmic theory of neural computation.