Roudi, Y; Latham, PE; (2007) A balanced memory network. PLOS COMPUT BIOL , 3 (9) , Article e141. 10.1371/journal.pcbi.0030141.eor.
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A fundamental problem in neuroscience is understanding how working memory-the ability to store information at intermediate timescales, like tens of seconds-is implemented in realistic neuronal networks. The most likely candidate mechanism is the attractor network, and a great deal of effort has gone toward investigating it theoretically. Yet, despite almost a quarter century of intense work, attractor networks are not fully understood. In particular, there are still two unanswered questions. First, how is it that attractor networks exhibit irregular firing, as is observed experimentally during working memory tasks? And second, how many memories can be stored under biologically realistic conditions? Here we answer both questions by studying an attractor neural network in which inhibition and excitation balance each other. Using mean-field analysis, we derive a three-variable description of attractor networks. From this description it follows that irregular firing can exist only if the number of neurons involved in a memory is large. The same mean-field analysis also shows that the number of memories that can be stored in a network scales with the number of excitatory connections, a result that has been suggested for simple models but never shown for realistic ones. Both of these predictions are verified using simulations with large networks of spiking neurons.
|Title:||A balanced memory network|
|Open access status:||An open access publication. A version is also available from UCL Discovery.|
|Additional information:||© 2007 Roudi and Latham. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. YR and PEL were supported by the Gatsby Charitable Foundation and by US National Institute of Mental Health grant R01 MH62447.|
|Keywords:||TO-NOISE ANALYSIS, PRIMATE PREFRONTAL CORTEX, ANALOG NEURAL-NETWORKS, SHORT-TERM-MEMORY, LOW FIRING RATES, ASSOCIATIVE MEMORY, AUTOASSOCIATIVE MEMORIES, NEURONAL NETWORKS, VISUAL-CORTEX, MEAN-FIELD|
|UCL classification:||UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit|
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