Anisotropic connectivity implements motion-based prediction in a spiking neural network
2013 (English)In: Frontiers in Computational Neuroscience, ISSN 1662-5188, Vol. 7, UNSP 112- p.Article in journal (Refereed) Published
Predictive coding hypothesizes that the brain explicitly infers upcoming sensory input to establish a coherent representation of the world. Although it is becoming generally accepted, it is not clear on which level spiking neural networks may implement predictive coding and what function their connectivity may have. We present a network model of conductance-based integrate-and-fire neurons inspired by the architecture of retinotopic cortical areas that assumes predictive coding is implemented through network connectivity, namely in the connection delays and in selectiveness for the tuning properties of source and target cells. We show that the applied connection pattern leads to motion-based prediction in an experiment tracking a moving dot. In contrast to our proposed model, a network with random or isotropic connectivity fails to predict the path when the moving dot disappears. Furthermore, we show that a simple linear decoding approach is sufficient to transform neuronal spiking activity into a probabilistic estimate for reading out the target trajectory.
Place, publisher, year, edition, pages
2013. Vol. 7, UNSP 112- p.
motion detection, motion extrapolation, probabilistic representation, predictive coding, network of spiking neurons, large-scale neuromorphic systems
Neurosciences Bioinformatics (Computational Biology)
IdentifiersURN: urn:nbn:se:su:diva-95083DOI: 10.3389/fncom.2013.00112ISI: 000324633400001OAI: oai:DiVA.org:su-95083DiVA: diva2:658679