Abstract
We investigated the effect of reciprocal connections in a network of modules of simulated spiking neurons. The neural activity is recorded by means of virtual electrodes and EEG-like signals, called electrochipograms (EChG), are analyzed by time- and frequency-domain methods. Bio-inspired processes in the circuits drive the build-up of auto-associative links within each module, which generate an areal activity, recorded by EChG, that reflect the changes in the corresponding functional connectivity within and between neuronal modules. We found that circuits with short inter-layer reciprocal projections exhibited enhanced response as to the stimulus, as to the inner-activity and long inter-layer projections make circuit exhibit non-coherent behavior. We show evidence that all networks of modules are able to process and maintain patterns of activity associated with the stimulus after its offset.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ahn, S.M., Freemen, W.J.: Neural dynamics under noise in the olfactory system. Biol. Cybern. 17(3), 165–168 (1975)
Blackman, R.B., Tukey, J.W.: The measurement of power spectra: From the Point of View of Communications Engineering, vol. 190. Dover Publications Inc., New York (1959)
Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Meth. 134(1), 9–21 (2004)
Gray, R.T., Robinson, P.A.: Stability and structural constraints of random brain networks with excitatory and inhibitory neural populations. J. Comput. Neurosci. 27(1), 81–101 (2009)
Hill, S., Villa, A.E.P.: Dynamic transitions in global network activity influenced by the balance of excitation and inhibtion. Network: Computational Neural Networks 8, 165–184 (1997)
Iglesias, J., Eriksson, J., Grize, F., Tomassini, M., Villa, A.E.P.: Dynamics of pruning in simulated large-scale spiking neural networks. BioSystems 79(1), 11–20 (2005)
Iglesias, J., Villa, A.E.P.: Recurrent spatiotemporal firing patterns in large spiking neural networks with ontogenetic and epigenetic processes. J. Physiol. Paris 104(3-4), 137–146 (2010)
Innocenti, G.M.: Exuberant development of connections, and its possible permissive role in cortical evolution. Trends in Neurosciences 18(9), 397–402 (1995)
Kaneko, K., Tsuda, I.: Chaotic itinerancy. Chaos: An Interdisciplinary Journal of Nonlinear Science 13(3), 926–936 (2003)
Lashley, K.S.: In search of engram. In: Anderson, J.A., Rosenfeld, E. (eds.) Neurocomputing: Foundations of Research, pp. 57–63. MIT Press, Cambridge (1988)
Rakic, P., Bourgeois, J., Eckenhoff, M.F., Zecevic, N., Goldman-Rakic, P.S.: Concurrent overproduction of synapses in diverse regions of the primate cerebral cortex. Science 232(4747), 232–235 (1986)
Shaposhnyk, V., Dutoit, P., Contreras-Lámus, V., Perrig, S., Villa, A.E.P.: A Framework for Simulation and Analysis of Dynamically Organized Distributed Neural Networks. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009, Part I. LNCS, vol. 5768, pp. 277–286. Springer, Heidelberg (2009)
Thiagarajan, T.C., Lebedev, M.A., Nicolelis, M.A., Plenz, D.: Coherence potentials: loss-less, all-or-none network events in the cortex. PLoS Biol. 8(1) (January 2010)
Wolters, G., Raffone, A.: Coherence and recurrency: maintenance, control and integration in working memory. Cogn. Process. 9(1), 1–17 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shaposhnyk, V., Villa, A.E.P. (2012). An Effect of Short and Long Reciprocal Projections on Evolution of Hierarchical Neural Networks. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-33269-2_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33268-5
Online ISBN: 978-3-642-33269-2
eBook Packages: Computer ScienceComputer Science (R0)