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ISHITA DASGUPTA:
I'm Ishita Dasgupta,
I'm going into my
third year of my PhD
at Harvard in Computational
Cognitive Science.
DAVID ROLNICK:
I'm David Rolnick.
I'm just getting into my
fourth year with my PhD at MIT.
I am in the applied
math department.
ISHITA DASGUPTA: So we're
working with Hopfield networks,
which is a kind of--
it's a concept in which tiny
neurons are kind of connected--
they're all connected
together, and the way
they update each
other, basically
determines the state
they're going to be in.
It has been used in the
past to model memories.
It's basically that there
are certain kinds of states
that the neurons prefer to be in
given the way that they're all
connected together.
And you can make them
go into these states
by initializing at
a different point.
And so it's been used to
store memories before,
but these are static memories.
Like, once you're in
one of those memories,
you just stay there.
So we were working
with this kind of model
to make some changes
to it and have
it be such that
you can go from one
such memory to
another such memory
and decide what probability
it is that you're
going to go to one memory
or to another memory,
and so basically add
some stochastic dynamics
to a Hopfield network.
DAVID ROLNICK: Well,
the idea is that there
are many situations where
the living brain is going
to be faced with the task of
reconstructing or simulating
a stochastic
sequence of actions.
So for instance, if
one were simulating
an event in which
one didn't know quite
what the probabilities were that
something was going to happen,
then you can imagine
playing it out in your mind
and imagining one
way of realizing it
and each state in
your mental sequence
would be determined
by the previous state.
So if something's falling, then
its state when it's falling
is determined by the
state when it was upright.
And if we can understand
how we could use memory
to generate these sequences of
patterns that are determined
by stochastic
rules, then we would
be able to get a better sense of
what kind of imagination memory
connections there are
possible even in a very
simple model of the brain.
And we're working with sort of
the simplest model of memory,
but it still turns out to be
extremely powerful in being
able to create these patterns
of stochastic sequences Markov
chains.
ISHITA DASGUPTA:
So far, we've just
been modeling it on using--
computationally modeling
what we think should happen.
For us, there's a
bit of theory work
to figure out what
kind of connections
we should put in there
so that it should work,
and then we actually
set up those connections
and see if it does work.
And we're hoping at some point
to be able to tie it back
to actual real world
situations in which
this kind of stochastic
sequence of events
actually happens in the brain,
but that is currently on the--
like, in the future.
Right now, we're
just making sure
that we can model this kind
of behavior in a computer.
DAVID ROLNICK: In
some sense, it's
an engineering task or a
theoretical task followed
by an engineering task.
Understanding what can be
done in a system like this
and then simply building it.
We built it and now we have to--
ISHITA DASGUPTA: Yes.
DAVID ROLNICK: --see how
it works in practice.
ISHITA DASGUPTA:
It becomes kind of
like an experimental
science, we're just
changing parameters and
seeing how things change,
because these are not
entirely clear and predicable.
You can't just say that
because you built it,
you should know how it works.
There are too many
degrees of freedom,
so there are a lot of
things to be tested
to see how well it
performs in different--
under different environments.
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