Abstract
This work presents a flexible reconfigurable approach to a bioinspired spiking neuron. The main objective of this contribution is to evaluate the silicon cost of the implementation of lime-dependent conductances in spiking neurons. The design presented here has been defined using a high level Hardware Description Language (HDL). This facilitates the extraction of simulation results, and the easy change of the circuit. The paper discusses how different aspects of lime-dependent conductances can be particularized in the circuit, and Iheir hardware requirements.
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References
SpikeFORCE: Real-Time Spiking Networks for Robot Control. (EU Project: TST-2001-35271). http://www.spikeforce.org
Jahnke, A.; Schoenauer, T.; Roth, U.; Mohraz, K.; Klar, H.: Simulation of Spiking Neural Networks on Different Hardware Platforms. ICANN97, (Springer-Verlag), LCNS, pp. 1187–1192(1997)
Schoenauer, T.; Alasoy, S.; Mehrlash, N.; Klar, H.: NeuroPipe-Chip: A Digital Neuro-Processor for Spiking Neural Networks, TEEE Trans. Neu. Net., 13(1), pp. 205–213, (2002)
Hartmann, G.; Frank, G.; Schafer, M.; Wolff, C: SPIKE128K-An Accelerator for Dynamic Simulation of Large Pulse-Coded Networks, MicroNeuro97, (1997)
Opher, L; Horn, D.; Quenel, B.: Clustering with spiking neurons, TCANN’99, LCNS (Springer-Verlag), pp. 485–490, Edinburgh, (1999)
Hopfield, J.J.; Pattern recognition computation using action potential timing for stimulus representation, Nature, 376, pp. 33–36, (1995)
Rochel, O.; Martinez, D.; Hugues, E.; Sarry, F.: Slereo-olfaclion with a sniffing neuromorphic robot using spiking neurons, Eurosensors, (2002)
Delorme, A.; Thorpe, S.J.: Face identification using one spike per neuron: resistance to image degradations, Neural Networks, 14(6–7), pp. 795–804, (2001)
Smith, L.S.: A one-dimensional frequency map implemented using a network of integrale-and-fire neurons. TCANN98, (Springer-Verlag), LCNS pp. 991–996, (1998)
Pelayo, F.J.; Romero, S.; Morillas, C.A.; Martinez, A; Ros, E.; Fernandez, E.: Translating image sequences into spike patterns for cortical neuro-stimulation. To be pusblished at the proceeding of the CNS’2003, Alicante, Spain, (2003)
Ros, E.; Pelayo, F.J.; Rojas, L; Fernandez, F.J.; Prielo, A.: A VLSI approach for spike liming coding, LNCS, (Springer-Verlag), 1629, 610–620, (1999)
Christodoulou, C; Bugmann, G.; Clarkson, T.G.: A spiking neuron model: applications and learning, Neural Networks, 15, (7), pp. 891–908, (2002)
Gerstner, W., Kistler, W.: Spiking Neuron Models. Cambridge University Press, (2002)
Eckhorn, R.; Bauer, R.; Jordan, W.; Brosh, M.; Krase, W.; Munk, M.; Reitbock: Coherent oscillations: A mechanism of feature linking in the visual cortex? Biol. Cyber. 60, pp. 121–130,(1988)
Walts, L.: Event-driven simulation of networks of spiking neurons. Advances in Neural Information Processing Systems, Vol. 6, pp. 927–934, (1994)
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Ros, E., Agis, R., Carrillo, R.R., Ortigosa, E.M. (2003). Post-synaptic Time-dependent Conductances in Spiking Neurons: FPGA Implementation of a Flexible Cell Model. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_19
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DOI: https://doi.org/10.1007/3-540-44869-1_19
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