Continuous attractor neural networks (CANNs)

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Presentation transcript:

Continuous attractor neural networks (CANNs) Thomas P. Trappenberg Dalhousie University, Canada CANN models and their relation to ANN Phase transitions in the weight-parameter space Hebbian learning and dimensionality discovery Path-integration Drifting activity packets and NMDA stabilization ?

`Basic/standard’ Grossberg-Hopfield type recurrent networks or spiking versions

`Basic/standard’ CANNmodel

Activity packet

Phase transitions in the weight-parameter space

Various gain functions

( or … ) Hebbian Learning Training on Gaussian patterns: Also, Kechen Zhang ’96: Gradient decent training …

Dimensionality discovery

Path-integration

Continuous dynamic (leaky integrator): The model equations: Continuous dynamic (leaky integrator): : activity of node i : firing rate : synaptic efficacy matrix : global inhibition : visual input : time constant : scaling factor : #connections per node : slope : threshold NMDA-style stabilization: Hebbian learning:

Drifting activity packets NMDA stabilization: