Context-modular memory networks support high-capacity, flexible, and robust associative memories

Podlaski, William F. and Agnes, Everton J. and Vogels, Tim P.. (2020) Context-modular memory networks support high-capacity, flexible, and robust associative memories.

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Official URL: https://edoc.unibas.ch/85030/

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Context, such as behavioral state, is known to modulate memory formation and retrieval, but is usually ignored in associative memory models. Here, we propose several types of contextual modulation for associative memory networks that greatly increase their performance. In these networks, context inactivates specific neurons and connections, which modulates the effective connectivity of the network. Memories are stored only by the active components, thereby reducing interference from memories acquired in other contexts. Such networks exhibit several beneficial characteristics, including enhanced memory capacity, high robustness to noise, increased robustness to memory overloading, and better memory retention during continual learning. Furthermore, memories can be biased to have different relative strengths, or even gated on or off, according to contextual cues, providing a candidate model for cognitive control of memory and efficient memory search. An external context-encoding network can dynamically switch the memory network to a desired state, which we liken to experimentally observed contextual signals in prefrontal cortex and hippocampus. Overall, our work illustrates the benefits of organizing memory around context, and provides an important link between behavioral studies of memory and mechanistic details of neural circuits.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Neurobiology > Theoretical and computational neuroscience (Agnes)
UniBasel Contributors:Agnes, Everton Joao
Item Type:Preprint
Number of Pages:30
Note:Everton J. Agnes and Tim P. Vogels are co-senior authors. -- Publication type according to Uni Basel Research Database: Discussion paper / Internet publication
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Last Modified:10 Nov 2021 07:51
Deposited On:10 Nov 2021 07:51

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