Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Slow feature analysis: unsupervised learning of invariances
Neural Computation
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
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
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The hippocampal formation is believed to play a central role in forming long lasting representation of events. However, in contrast to the continuous nature of sensory signal flow, events are spatially and temporally bounded processes. In this paper we are interested in the kind of representation that allows for detecting and/or predicting events. Based on new results on the identification problem of linear hidden processes, we propose a general signal encoding model that can represent causal relationships used to define events. We translate the model into a connectionist structure in which parameter learning follows biologically plausible rules. We also speculate on the resemblance of the resulting structure to the connection system of the hippocampal formation. When our signal encoding model is applied on spatially anchored inputs, its different parts feature spatially localized and periodic neural activity similar to those found in the hippocampus and in the entorhinal cortex, respectively. These emergent forms of spatial activity differentiates our model from other computational models of (spatial) memory as the model has not been explicitly designed to deal with spatial information. We speculate that our model may describe the core function of the hippocampal region in forming episodic memory and supporting spatial navigation.