Abstract
Spiking neural networks have a limited memory capacity, such that a stimulus arriving at time t would vanish over a timescale of 200-300 milliseconds [1]. Therefore, only neural computations that require history dependencies within this short range can be accomplished. In this paper, the limited memory capacity of a spiking neural network is extended by coupling it to an delayed-dynamical system. This presents the possibility of information exchange between spiking neurons and continuous delayed systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Maass, W., Natschläger, T., Markram, H.: Fading memory and kernel properties of generic cortical microcircuit models. Journal of Physiology 98, 315–330 (2004)
Buonomano, D.V., Maass, W.: State-dependent computations: spatiotemporal processing in cortical networks. Nature Reviews Neuroscience 10, 113–125 (2009)
Durstewitz, D., Seamans, J.K., Sejnowski, T.J.: Neurocomputational models of working memory. Nature Neuroscience 3(suppl.), 1184–1191 (2000)
Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Computation 14, 2531–2560 (2002)
Jäger, H.: The echo state approach to analysing and training recurrent neural networks. GMD Report 147 (2001)
Mayor, J., Gerstner, W.: Signal buffering in random networks of spiking neurons: Microscopic versus macroscopic phenomena. Physical Review E 72, 15 (2005)
Körding, K.P., Wolpert, D.M.: Bayesian integration in sensorimotor learning. Nature 427, 244–247 (2004)
Churchland, M.M., et al.: Neural population dynamics during reaching. Nature 487, 51–56 (2012)
Maass, W., Joshi, P., Sontag, E.D.: Computational aspects of feedback in neural circuits. PLoS Comput. Biol. 3, e165 (2007)
Pascanu, R., Jäger, H.: A Neurodynamical Model for Working Memory. Neural Networks 1, 123 (2010)
Forde, J.E.: Delay Differential Equation Models in Mathematical Biology. PhD Thesis
Natschläger, T., Markram, H., Maass, W.: Computer Models and Analysis Tools for Neural Microcircuits. Neuro- Science Databases. A Practical Guide, 121–136 (2003)
Mackey, M.C., Glass, L.: Oscillation and Chaos in Phisiological Control Systems. Science (1977)
Appeltant, L., et al.: Information processing using a single dynamical node as complex system. Nature Communications 2, 468 (2011)
Ganguli, S., Huh, D., Sompolinsky, H.: Memory traces in dynamical systems. PNAS 105, 18970–18975 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Castellano, M., Pipa, G. (2013). Memory Trace in Spiking Neural Networks. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-40728-4_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40727-7
Online ISBN: 978-3-642-40728-4
eBook Packages: Computer ScienceComputer Science (R0)