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 Wed 08 April 2020 14:00
Online seminar (map)
Note: This is an Online Seminar
Prof Kanaka Rajan
Icahn School of Medicine at Mount Sinai
Recurrent network models of adaptive and maladaptive learning
Abstract: During periods of persistent and inescapable stress,
animals can switch from active to passive coping strategies to
manage effort-expenditure. Such normally adaptive behavioral
state transitions can become maladaptive in disorders such as
depression. We developed a new class of multi-region recurrent
neural network (RNN) models to infer brain-wide interactions
driving such maladaptive behavior. The models were trained to
match experimental data across two levels simultaneously:
brain-wide neural dynamics from 10-40,000 neurons and the
realtime behavior of the fish. Analysis of the trained RNN
models revealed a specific change in inter-area connectivity
between the habenula (Hb) and raphe nucleus during the
transition into passivity. We then characterized the
multi-region neural dynamics underlying this transition. Using
the interaction weights derived from the RNN models, we
calculated the input currents from different brain regions to
each Hb neuron. We then computed neural manifolds spanning
these input currents across all Hb neurons to define subspaces
within the Hb activity that captured communication with each
other brain region independently. At the onset of stress,
there was an immediate response within the Hb/raphe subspace
alone. However, RNN models identified no early or
fast-timescale change in the strengths of interactions between
these regions. As the animal lapsed into passivity, the
responses within the Hb/raphe subspace decreased, accompanied
by a concomitant change in the interactions between the raphe
and Hb inferred from the RNN weights. This innovative
combination of network modeling and neural dynamics analysis
points to dual mechanisms with distinct timescales driving the
behavioral state transition: early response to stress is
mediated by reshaping the neural dynamics within a preserved
network architecture, while long-term state changes correspond
to altered connectivity between neural ensembles in distinct
brain regions