Simulation of chaotic EEG patterns with a dynamic model of the olfactory system
Biological Cybernetics
Dynamic pattern recognition of coordinated biological motion
Neural Networks
Keeping the neural networks simple by minimizing the description length of the weights
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Neural Computation
Connecting cortical and behavioral dynamics: bimanual coordination
Neural Computation
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
A fast learning algorithm for deep belief nets
Neural Computation
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
Anubis: Artificial neuromodulation using a bayesian inference system
Neural Computation
Image categorization using a semantic hierarchy model with sparse set of salient regions
Frontiers of Computer Science: Selected Publications from Chinese Universities
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This paper assumes that cortical circuits have evolved to enable inference about the causes of sensory input received by the brain. This provides a principled specification of what neural circuits have to achieve. Here, we attempt to address how the brain makes inferences by casting inference as an optimisation problem. We look at how the ensuing recognition dynamics could be supported by directed connections and message-passing among neuronal populations, given our knowledge of intrinsic and extrinsic neuronal connections. We assume that the brain models the world as a dynamic system, which imposes causal structure on the sensorium. Perception is equated with the optimisation or inversion of this internal model, to explain sensory input. Given a model of how sensory data are generated, we use a generic variational approach to model inversion to furnish equations that prescribe recognition; i.e., the dynamics of neuronal activity that represents the causes of sensory input. Here, we focus on a model whose hierarchical and dynamical structure enables simulated brains to recognise and predict sequences of sensory states. We first review these models and their inversion under a variational free-energy formulation. We then show that the brain has the necessary infrastructure to implement this inversion and present stimulations using synthetic birds that generate and recognise birdsongs.