Continual One-Shot Learning of Hidden Spike-Patterns with Neural Network Simulation Expansion and STDP Convergence Predictions

by   Toby Lightheart, et al.

This paper presents a constructive algorithm that achieves successful one-shot learning of hidden spike-patterns in a competitive detection task. It has previously been shown (Masquelier et al., 2008) that spike-timing-dependent plasticity (STDP) and lateral inhibition can result in neurons competitively tuned to repeating spike-patterns concealed in high rates of overall presynaptic activity. One-shot construction of neurons with synapse weights calculated as estimates of converged STDP outcomes results in immediate selective detection of hidden spike-patterns. The capability of continual learning is demonstrated through the successful one-shot detection of new sets of spike-patterns introduced after long intervals in the simulation time. Simulation expansion (Lightheart et al., 2013) has been proposed as an approach to the development of constructive algorithms that are compatible with simulations of biological neural networks. A simulation of a biological neural network may have orders of magnitude fewer neurons and connections than the related biological neural systems; therefore, simulated neural networks can be assumed to be a subset of a larger neural system. The constructive algorithm is developed using simulation expansion concepts to perform an operation equivalent to the exchange of neurons between the simulation and the larger hypothetical neural system. The dynamic selection of neurons to simulate within a larger neural system (hypothetical or stored in memory) may be a starting point for a wide range of developments and applications in machine learning and the simulation of biology.


Biologically-Inspired Spatial Neural Networks

We introduce bio-inspired artificial neural networks consisting of neuro...

Few-shot Continual Learning: a Brain-inspired Approach

It is an important yet challenging setting to continually learn new task...

A Spiking Network that Learns to Extract Spike Signatures from Speech Signals

Spiking neural networks (SNNs) with adaptive synapses reflect core prope...

Spiking Associative Memory for Spatio-Temporal Patterns

Spike Timing Dependent Plasticity is form of learning that has been demo...

Notes on Generalized Linear Models of Neurons

Experimental neuroscience increasingly requires tractable models for ana...

STDP allows close-to-optimal spatiotemporal spike pattern detection by single coincidence detector neurons

By recording multiple cells simultaneously, electrophysiologists have fo...

Please sign up or login with your details

Forgot password? Click here to reset