Learning invariance from transformation sequences
Neural Computation
Neural Networks
Topology representing networks
Neural Networks
Self-organizing maps
Dynamics and Topographic Organization of Recursive Self-Organizing Maps
Neural Computation
Temporal context as cortical spatial codes
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
On non-markovian topographic organization of receptive fields in recursive self-organizing map
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Spatio-Temporal organization map: a speech recognition application
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Residual activity in the neurons allows SOMs to learn temporal order
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Comparison between two spatio-temporal organization maps for speech recognition
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Learning and generating folk melodies using MPF-Inspired hierarchical self-organising maps
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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The new time-organized map (TOM) is presented for a better understanding of the self-organization and geometric structure of cortical signal representations. The algorithm extends the common self-organizing map (SOM) from the processing of purely spatial signals to the processing of spatiotemporal signals. The main additional idea of the TOM compared with the SOM is the functionally reasonable transfer of temporal signal distances into spatial signal distances in topographic neural representations. This is achieved by neural dynamics of propagating waves, allowing current and former signals to interact spatiotemporally in the neural network. Within a biologically plausible framework, the TOM algorithm (1) reveals how dynamic neural networks can self-organize to embed spatial signals in temporal context in order to realize functional meaningful invariances, (2) predicts time-organized representational structures in cortical areas representing signals with systematic temporal relation, and (3) suggests that the strength with which signals interact in the cortex determines the type of signal topology realized in topographic maps (e.g., spatially or temporally defined signal topology). Moreover, the TOM algorithm supports the explanation of topographic reorganizations based on time-to-space transformations (Wiemer, Spengler, Joublin, Stagge, & Wacquant, 2000).