Towards a Dynamic Causal Modeling approach by using Bayesian Convolutional Networks and Stochastic Variational Inference
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2021-05-11
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en
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Abstract
The identification of the neural dependencies and hierarchical representations in the
brain constitutes one of the principal goals of neuroscience. We propose a Dynamic
Causal Modeling (DCM) approach that utilizes convolutional neural networks and
stochastic variational inference. We employ the Neural Information Flow (NIF)
that describes the visual cortex areas’ hierarchy and neuronal activity through
convolutional layers and an observation model, respectively. We convert the model
into Bayesian NIF by introducing probability distributions over all the learnable
parameters and estimate the latent parameters’ posterior densities by maximizing
the model’s evidence lower bound (ELBO) via stochastic variational inference. All
the Bayesian NIF parameters have biophysical interpretation. Stochastic variational
inference supports the application of complex Bayesian models to large datasets,
whereas the estimated ELBO can be used for Bayesian model comparison like in
DCM. Therefore, our proposed approach points to a better explained DCM that
can manage large-scale fMRI datasets. We demonstrate our method’s capacity to
predict accurate neural responses by applying a large-scale functional magnetic
resonance dataset over a simple model architecture representing the early visual
cortex. Our results imply that the Bayesian NIF model can predict brain-like
responses while successfully capturing the observational model’s receptive field
representations. Further, the ELBO converges and thus can be used for Bayesian
model comparison with different model architectures.
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Faculteit der Sociale Wetenschappen