English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We investigate extensions to the model put forward by Brody and Hopfield [1] for spike-timing based pattern recognition applied to mammalian olfaction. Their model implements a pattern recognition algorithm realized in the dynamics of a network of coupled IF neurons subject to a sine-wave rhythm. Subsets of these neurons can synchronize through the principle of one-to-one mode locking. Their network represents 3 layers of neural activity, the first two of which are inspired by the connectivity of glomeruli and mitral cells in mammalian olfactory circuits and the gamma-rhythm activity observed in the olfactory bulb. Specifically in this model a pattern of glomerular activity representing a given odor causes a particular subset of model mitral cells to synchronize and this synchronous activity can drive a "grandmother" model cortical cell through threshold triggering a recognition event. In this study we quantify the performance of their original model and compare it to our extensions of the model such as using a network-generated rhythm rather than a sine-wave, introducing inhibitive feedback and generalizing to p-q mode locking strategies. We compute the scaling with respect to the number of mitral neurons of a measure of the number of odor patterns the model can recognize. Quite remarkably we find this performance can increase very fast with increasing network size -- consistent with exponential scaling.

Scaling of spike-timing based neuron model for mammalian olfaction with network size