Post and pre-compensatory Hebbian learning for categorisation.

A system with some degree of biological plausibility is developed to categorise items from a widely used machine learning benchmark. The system uses fatiguing leaky integrate and fire neurons, a relatively coarse point model that roughly duplicates biological spiking properties; this allows spontaneous firing based on hypo-fatigue so that neurons not directly stimulated by the environment may be included in the circuit. A novel compensatory Hebbian learning algorithm is used that considers the total synaptic weight coming into a neuron.

Analysis of genotoxicity data in a regulatory context.

Analytical methods for regulatory tests usually must be defined before testing. To take this into account and to minimise equivocal interpretations, a sequential strategy is recommended. Assay validity must be verified and results then classed as clearly negative, clearly positive, or uncertain based on historical data.