Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Sparse coding in the primate cortex
The handbook of brain theory and neural networks
Slow feature analysis: unsupervised learning of invariances
Neural Computation
Predictability, Complexity, and Learning
Neural Computation
Learning spatial concepts from RatSLAM representations
Robotics and Autonomous Systems
Simple conditions for forming triangular grids
Neurocomputing
Undercomplete Blind Subspace Deconvolution
The Journal of Machine Learning Research
A principle for learning egocentric-allocentric transformation
Neural Computation
Solving the problem of negative synaptic weights in cortical models
Neural Computation
Bayesian Inference and Optimal Design for the Sparse Linear Model
The Journal of Machine Learning Research
Undercomplete Blind Subspace Deconvolution Via Linear Prediction
ECML '07 Proceedings of the 18th European conference on Machine Learning
Autoregressive model of the hippocampal representation of events
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Independent process analysis without a priori dimensional information
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Post nonlinear independent subspace analysis
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Learning the states: a brain inspired neural model
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
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The hippocampal formation is believed to play a central role in memory functions related to the representation of events. Events are usually considered as temporally bounded processes, in contrast to the continuous nature of sensory signal flow they originate from. Events are then organized and stored according to behavioral relevance and are used to facilitate prediction of similar events. In this paper we are interested in the kind of representation of sensory signals that allows for detecting and/or predicting events. Based on new results on the identification problem of linear hidden processes, we propose a connectionist network with biologically sound parameter tuning that can represent causal relationships and define events. Interestingly, the wiring diagram of our architecture not only resembles the gross anatomy of the hippocampal formation (including the entorhinal cortex), but it also features similar spatial distribution functions of activity (localized and periodic, 'grid-like' patterns) as found in the different parts of the hippocampal formation. We shortly discuss how our model corresponds to different theories on the role of the hippocampal formation in forming episodic memories or supporting spatial navigation. We speculate that our approach may constitute a step toward a unified theory about the functional role of the hippocampus and the structure of memory representations.