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Continuous attractor neural networks (CANNs)

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Presentation on theme: "Continuous attractor neural networks (CANNs)"— Presentation transcript:

1 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 ?

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

3 `Basic/standard’ CANNmodel

4 Activity packet

5 Phase transitions in the weight-parameter space

6 Various gain functions

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

8 Dimensionality discovery

9 Path-integration

10 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:

11 Drifting activity packets
NMDA stabilization:

12

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