A (patented) method and synapse mechanism which facilitates multiple weights per a connection, where each weight within the connection can be set to learn and forget at a different rate from the others. That is, a separate learning method and forget process can be specified for each of the multiple weights used to make up a single given connection between two objects.


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Weights Can Learn From Other Weights

A learning algorithm called weight-to-weight learning has been developed specifically for multitemporal synapses. This learning method uses one or more of the multiple weights associated with a connection to produce a learning factor, which is then used to train another weight in the same connection.

This allows, for example, a connection to be specified with a long term (slow) connection strength to learn directly from transient learning, which occurs on weights in the same connection that learn and forget more quickly.



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Resources

• Multitemporal Synapses and Our Perception of a Present Moment
• Introducing: Multitemporal Synapses
• Multitemporal Synapses Awarded a Patent