Thursday, November 19, 2020 3pm to 4pm
About this Event
Brains have remarkable abilities to store information about the external world, on time scales that range from seconds to the lifetime
of an animal. What are the mechanisms by which information is stored in the brain, and how is this stored information retrieved from memory? One of the central hypothesis of neuroscience is that
information is stored through synaptic plasticity - modifications of synaptic connectivity between neurons. Theoretical models have explored the impact of such synaptic plasticity mechanisms on network dynamics. One scenario, in which synaptic changes are predominantly symmetric, leads to the creation of fixed point attractor states of the dynamics of the network, one for each item stored in memory. Another scenario, in which changes have a strong asymmetric component, leads to the creation of stable sequences of network activity. In this talk, I will present recent instantiations of these models, that are both simple enough to enable mean-field calculations,
but also detailed enough to enable detailed comparisons with experimental data. I will also show how heterogeneities in synaptic plasticity rules can allow networks to flexibly switch from the fixed point attractor regime to the sequence regime, and to vary the speed at which sequences are retrieved.
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