[CTN] CTN seminar: Stefan Mihalas (Allen Institute) 3:30 Tuesday Nov 27 E5 2004

[Bryan Tripp]

Hi everyone,

Here is the abstract for Tuesday's talk.

Bryan

Bio-inspired models of machine learning in vision
Stefan Mihalas, Allen Institute for Brain Science

Deep neural network have been inspired by biological networks.
Convolutional neural network, a frequently used form of deep networks, have
had great success in many real-world applications and have been used to
model visual processing in the brain. However, these networks require large
amounts of labeled data to train and are quite brittle: for example, small
changes in the input image can dramatically change the network's output
prediction. In contrast to what is known from biology, these networks rely
on feedforward connections, largely ignoring the influence of recurrent
connections.
In this study we construct deep neural networks which make use of knowledge
of local circuits, and test some predictions of the network against
observed data. For the local circuit, we used a model based on the
assumption that the lateral connections of neurons implement optimal
integration of context. The optimal computations require more complex
neurons, but they can be approximated by a standard artificial neuron. We
tested this hypothesis using natural scene statistics and mouse v1
recordings which allows us to construct a parameter-free model for lateral
connections. The optimal structure matches the observed structure
(like-to-like pyramidal connectivity and distance dependence of
connections) better than receptive field correlation models.
Subsequently we integrated these local circuits in traditional
convolutional neural networks. Models with optimal lateral connections are
more robust to noise and achieve better performance on noisy versions of
the MNIST and CIFAR-10 datasets. These models also reproduce salient
features of observed neuronal recordings: e.g. positive signal and noise
correlations. Our results demonstrate the usefulness of combining knowledge
of local circuits with machine learning techniques in real-world vision
tasks and studying cortical computations.


On Sat, Nov 24, 2018 at 10:49 PM Bryan Tripp <bptripp at gmail.com> wrote:

> Hi everyone,
>
> Please join us for the seminar on Tuesday. The speaker is Stefan Mihalas,
> from the Allen Institute for Brain Science (Seattle). The title is,
> "Bio-inspired models of machine learning in vision."
>
> Bryan
>
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