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- C. Weber and
K. Obermayer. Emergence of Modularity within One Sheet of Neurons: a
Model Comparison.
.
In Emergent Neural Computational Architectures Based on
Neuroscience, pages 53-67. Springer, 2001.
We investigate how structured information processing within a
neural net can emerge as a result of unsupervised learning from data. The
model consists of input neurons and hidden neurons which are recurrently
connected. On the basis of a maximum likelihood framework the task is to
reconstruct given input data using the code of the hidden units. Hidden
neurons are fully connected and they may code on different hierarchical
levels. The hidden neurons are seperated into two groups by their intrinsic
parameters witch control their firing properties. These differential
properties encourage the two groups to code on two different hierarchical
levels. We train the net using data which are either generated by two linear
models acting in parallel or by a hierarchical process. As a result of
training the net captures the structure of the data generation process.
Simulations were performed with two different neural network models, both
trained to be maximum likelihood predictors of the training data. A (non
linear) hierarchical Kalman filter model and a Helmholtz machine. Here we
compare both models to the neural circuitry in the cortex. The results imply
that the division of the cortex into laterally and hierarchically organized
areas can evolve to a certain degree as an adaptation to the
environment.
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