Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
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Simultaneous recordings of many single neurons reveals unique insights into network processing spanning the timescale from single spikes to global oscillations. Neurons dynamically self-organize in subgroups of coactivated elements referred to as cell assemblies. Furthermore, these cell assemblies are reactivated, or replayed, preferentially during subsequent rest or sleep episodes, a proposed mechanism for memory trace consolidation. Here we employ Principal Component Analysis to isolate such patterns of neural activity. In addition, a measure is developed to quantify the similarity of instantaneous activity with a template pattern, and we derive theoretical distributions for the null hypothesis of no correlation between spike trains, allowing one to evaluate the statistical significance of instantaneous coactivations. Hence, when applied in an epoch different from the one where the patterns were identified, (e.g. subsequent sleep) this measure allows to identify times and intensities of reactivation. The distribution of this measure provides information on the dynamics of reactivation events: in sleep these occur as transients rather than as a continuous process.