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Physical Diversity of Synapses: Applications to Plasticity, and Learning Models (eg HTM)

While most artificial neuron networks continue to assume a point model for spiking in neurons (a single weighted sum of all of its synapses, who play standard roles), several new neuroscience studies suggest that the spatial proximity of synapses relative to the neuron body (eg proximal, distal, apicall), is surprisingly definitive. Meanwhile, new studies in plasticity and meta-plasticity confirm the structural importance of synaptic structural differences in learning in brain circuits.

In addition to these broad topics, a critical analysis of the Hierarchical Temporal Model (HTM) algorithm and model proposed by Hawkins based on some of these ideas is timely and may be very relevant to the participants.

The group is meant to be casual and quite eclectic, oriented primarily around white-boarding ideas based on readings from a few papers. If time permits, open-source code for HTM model may be explored. Being at the nexus of neuroscience, machine learning , and neuromorphic circuits expertise in all of the areas is certainly not required to participate, an expertise in any will certainly help to make the conversations richer.

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Timetable

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Leader

Christopher Bennett

Members

Christopher Bennett
Simon Davidson
Gabriel Andres Fonseca Guerra
Michael Hopkins
Marina Ignatov
Guido Novati
Melika Payvand
Francesca Puppo
Saray Soldado Magraner
Evangelos Stromatias
Nikolaos Vasileiadis
Bernhard Vogginger
Borys Wrobel
Qi Xu
André van Schaik