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Inhomogeneities in heteroassociative memories with linear learning rules
David C Sterratt1, David Willshaw
1Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, Scotland, UK. david.c.sterratt@ac.uk
Neural Computation
|December 1, 2007
Summary
Neural network inhomogeneities like synaptic noise and varying memory intensity impact associative memory performance. Stochastic transmission significantly reduces recall fidelity more than differential attenuation.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Network Science
Background:
- Feedforward associative memories model hippocampal function.
- Synaptic and neuronal inhomogeneities are biologically prevalent.
- Understanding these factors is crucial for network performance analysis.
Purpose of the Study:
- Investigate the impact of neural inhomogeneities on associative memory performance.
- Analyze signal-to-noise ratio (SNR) and memory capacity under various conditions.
- Extend previous theoretical analyses of network models.
Main Methods:
- Developed a high-level network model incorporating differential input attenuation, stochastic synaptic transmission, and varying memory intensity.
- Extended previous analytical methods to determine memory capacity for a class of local learning rules.
- Utilized distributions of attenuation for unbranched and branched dendritic trees.
- Employed biological parameters for stochastic transmission to calculate coefficient of variation (CV).
Main Results:
- Signal-to-noise ratio (SNR) depends on CVs of attenuation, transmission, memory intensity, learning rule parameters, pattern sparsity, and number of stored memories.
- Differential attenuation has a lower impact on SNR than stochastic transmission.
- Storing memories at different intensities is analogous to weight decay in learning rules.
- An optimal weight decay rate maximizes network capacity, resulting in a capacity factor of e lower than non-palimpsest equivalents.
Conclusions:
- Synaptic and neuronal inhomogeneities significantly modulate associative memory performance.
- Stochastic transmission poses a greater challenge to recall fidelity than differential attenuation.
- The palimpsest nature of continuous learning introduces a trade-off between new and old memory storage, with an optimal decay rate for capacity maximization.
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