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BILL LOTTER: Hi,
I'm Bill Lotter.
I have a PhD in
biophysics from Harvard,
where I was jointly advised by
Gabriel Kreiman and David Cox.
And I was a member of CBMM.
A common goal in neuroscience
is to build computational models
that can reproduce and explain
neural phenomena as this can
help us gain a
better understanding
of the computational
principles in the brain.
Here we took a
deep neural network
that we had previously
developed called the PredNet
and tested whether it could
reproduce various aspects that
are observed in actual neurons.
There have been a
number of recent works,
for instance, that have shown
that deep neural networks can
be useful in predicting the
responses of actual neurons
to sets of images.
Many of these networks, however,
have been purely feed forward,
meaning that they lack top-down
and lateral recurrence, which
we know are prevalent
in the brain.
Additionally, these
networks are often
trained in a purely supervised
sense using large numbers
of label-training examples.
We know that this
level of supervision
is also different
from how humans learn.
The PredNet model,
on the other hand,
is a neural network that has
both top-down and lateral
recurrent connections,
and is additionally
trained in a purely unsupervised
or self-supervised manner.
The network is trained to
make next frame predictions
in videos.
That is, given a
series of video frames,
it's trained to predict the
next frame in the sequence.
Here, like the original paper,
we trained the PredNet model
on car-mounted camera videos.
So these are videos
of cars driving around
with cameras attached.
We then took the
network and tested it
with a number of
artificial stimuli
that are similar to
those commonly used
in neuroscience experiments
to see if it could reproduce
various phenomena.
We saw that it indeed could
reproducing aspects ranging
from single unit
response properties
to responses to
visual illusions.
For instance, we saw
that it exhibited
the temporal and
spatial response
properties that resembled
visual neuron responses.
The model also showed
sequence learning
aspects that are similar
to those observed
in primate visual cortex.
Finally, it was able
to reproduce aspects
of visual illusions, such
as those like the Kanizsa
triangle, where the
model's response resembled
responses observed in neurons.
It additionally showed
correlates of the flash lag
illusion.
So a model that was
inspired by neuroscience
and trained on
real-world stimuli
could reproduce various aspects
observed in biological neurons
even though it wasn't
explicitly trained to do so.
These results thus suggest
potentially deep connections
between recurrent predictive
neural networks and the brain.
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