Abstract
In humans and primates, the sequential structure of complex actions is apparently learned at an abstract “cognitive” level in several regions of the frontal cortex, independent of the control of the immediate effectors by the motor system. At this level, actions are represented in terms of kinematic parameters – especially direction of end effector movement – and encoded using population codes. Muscle force signals are generated from this representation by downstream systems in the motor cortex and the spinal cord.
In this paper, we consider the problem of learning population-coded kinematic sequences in an abstract neural network model of the medial frontal cortex. For concreteness, the sequences are represented as line drawings in a two-dimensional workspace. Learning such sequences presents several challenges because of the internal complexity of the individual sequences and extensive overlap between sequences. We show that, by using a simple module-selection mechanism, our model is capable of learning multiple sequences with complex structure and very high cross-sequence similarity.
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Byadarhaly, K.V., Perdoor, M., Vasa, S., Fernandez, E., Minai, A.A. (2009). Learning Complex Population-Coded Sequences. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_31
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DOI: https://doi.org/10.1007/978-3-642-04274-4_31
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