Zusammenfassung
Some neurons encode information about the orientation or position
of an
animal, and can maintain their response properties in the absence
of visual
input. Examples include head direction cells in rats and primates,
place
cells in rats and spatial view cells in primates. Continuous attractor
neural
networks model these continuous physical spaces by using recurrent
collateral
connections between the neurons which reflect the distance between
the neurons
in the state space (e.g. head direction space) of the animal. These
networks
maintain a localized packet of neuronal activity representing the
current state
of the animal. We show how the synaptic connections in a one-dimensional
continuous attractor network (of for example head direction cells)
could be selforganized
by associative learning. We also show how the activity packet could
be moved from one location to another by idiothetic (self-motion)
inputs, for
example vestibular or proprioceptive, and how the synaptic connections
could
self-organize to implement this. The models described use trace
associative
synaptic learning rules that utilize a form of temporal average of
recent cell
activity to associate the firing of rotation cells with the recent
change in the
representation of the head direction in the continuous attractor.
We also show
how a nonlinear neuronal activation function that could be implemented
by
NMDA receptors could contribute to the stability of the activity packet
that
represents the current state of the animal.
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