Abstract
We investigate the influence of spike timing dependent plasticity (STDP) on the prediction properties of recurrent microcircuits. We use sparsely connected networks in which the synaptic modifications introduced by STDP are complemented by two homeostatic plasticity mechanisms: synaptic normalization and intrinsic plasticity. In the presence of structured external input, STDP changes the connectivity matrix of the network such that the recurrent connections capture the particularities of the input stimuli, allowing the network to anticipate future inputs. Network activation patterns reflect different input expectations and can be separated by an unsupervised clustering technique.
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Lazar, A., Pipa, G., Triesch, J. (2008). Predictive Coding in Cortical Microcircuits. In: Kůrková, V., Neruda, R., KoutnÃk, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_40
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DOI: https://doi.org/10.1007/978-3-540-87559-8_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87558-1
Online ISBN: 978-3-540-87559-8
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