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Characterizing the Firing Properties of an Adaptive Analog VLSI Neuron

  • Daniel Ben Dayan Rubin
  • Elisabetta Chicca
  • Giacomo Indiveri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3141)

Abstract

We describe the response properties of a compact, low power, analog circuit that implements a model of a leaky–Integrate & Fire (I&F) neuron, with spike-frequency adaptation, refractory period and voltage threshold modulation properties. We investigate the statistics of the circuit’s output response by modulating its operating parameters, like refractory period and adaptation level and by changing the statistics of the input current. The results show a clear match with theoretical prediction and neurophysiological data in a given range of the parameter space. This analysis defines the chip’s parameter working range and predicts its behavior in case of integration into large massively parallel very–large–scale–integration (VLSI) networks.

Keywords

Refractory Period Input Current Firing Property Subthreshold Behavior Neocortical Pyramidal Cell 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Dayan, P., Abbott, L.F.: Theoretical Neuroscience. The MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  2. 2.
    Chicca, E., Indiveri, G., Douglas, R.: An adaptive silicon synapse. In: Proc. IEEE International Symposium on Circuits and Systems, IEEE, Los Alamitos (2003)Google Scholar
  3. 3.
    D’Andreagiovanni, M., Dante, V., Del Giudice, P., Mattia, M., Salina, G.: Emergent asynchronous, irregular firing in a deterministic analog VLSI recurrent network. In: Rattay, F. (ed.) Proceedings of the World Congress on Neuroinformatics. ARGESIM Reports, Vienna, pp. 468–477. ARGESIM/ASIM Verlag (2001)Google Scholar
  4. 4.
    Mead, C.: Analog VLSI and Neural Systems. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  5. 5.
    Schultz, S.R., Jabri, M.A.: Analogue VLSI ’integrate-and-fire’ neuron with frequency adaptation. Electronic Letters 31, 1357–1358 (1995)CrossRefGoogle Scholar
  6. 6.
    van Schaik, A.: Building blocks for electronic spiking neural networks. Neural Networks 14, 617–628 (2001)CrossRefGoogle Scholar
  7. 7.
    Boahen, K.: Communicating neuronal ensembles between neuromorphic chips. In: Lande, T.S. (ed.) Neuromorphic Systems Engineering, pp. 229–259. Kluwer Academic, Norwell (1998)CrossRefGoogle Scholar
  8. 8.
    Culurciello, E., Etienne-Cummings, R., Boahen, K.: Arbitrated address-event representation digital image sensor. Electronics Letters 37, 1443–1445 (2001)CrossRefGoogle Scholar
  9. 9.
    Indiveri, G.: Neuromorphic selective attention systems. In: Proc. IEEE International Symposium on Circuits and Systems, IEEE, Los Alamitos (2003)Google Scholar
  10. 10.
    Liu, S.C., Kramer, J., Indiveri, G., Delbrück, T., Douglas, R.: Analog VLSI:Circuits and Principles. MIT Press, Cambridge (2002)Google Scholar
  11. 11.
    Ermentrout, B.: Linearization of F-I curves by adaptation. Neural Comput. 10, 1721–1729 (1998)CrossRefGoogle Scholar
  12. 12.
    Liu, Y., Wang, X.: Spike-Frequency Adaptation of a Generalized Leaky Integrate-and-Fire Model Neuron. J. Comput. Neurosc. 10, 25–45 (2001)CrossRefGoogle Scholar
  13. 13.
    Fuhrmann, G., Markram, H., Tsodyks, M.: Spike-Frequency Adaptation and neocortical rhythms. J. Neurophysiology 88, 761–770 (2002)Google Scholar
  14. 14.
    Rauch, A., Camera, G.L., Luescher, H.R., Senn, W., Fusi, S.: Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents. J. Neurophysiol. 79 (2003)Google Scholar
  15. 15.
    Wang, X.: Calcium coding and adaptive temporal computation in cortical pyramidal neurons. J. Neurophysiol. 79, 1549–1566 (1998)Google Scholar
  16. 16.
    Koch, C., Segev, I.: Methods in Neural Modeling, 2nd edn. MIT Press, Cambridge (2001)Google Scholar
  17. 17.
    Tuckwell, H.: Introduction to Theoretical Neurobiology, vol. 2. Cambridge University Press, Cambridge (1988)CrossRefGoogle Scholar
  18. 18.
    Softky, W., Koch, C.: The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci. 13, 334–350 (1993)Google Scholar
  19. 19.
    Fusi, S., Mattia, M.: Collective behaviour of networks with linear (VLSI) integrate-and-fire neurons. Neural Computation 11, 633–652 (1999)CrossRefGoogle Scholar
  20. 20.
    Ahmed, B., Anderson, J., Douglas, R., Martin, K., Whitteridge, D.: Estimates of the net excitatory currents evoked by visual stimulation of identified neurons in cat visual cortex. Cereb. Cortex 8, 462–476 (1998)CrossRefGoogle Scholar
  21. 21.
    Chicca, E., Badoni, D., Dante, V., D’Andreagiovanni, M., Salina, G., Fusi, S., Del Giudice, P.: A VLSI recurrent network of integrate–and–fire neurons connected by plastic synapses with long term memory. IEEE Trans. Neural Net. 14, 1297–1307 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Daniel Ben Dayan Rubin
    • 1
    • 2
  • Elisabetta Chicca
    • 2
  • Giacomo Indiveri
    • 2
  1. 1.Department of BioengineeringPolitecnico di MilanoMilanItaly
  2. 2.INI – Institute of NeuroinformaticsUNI/ETH ZurichZurichSwitzerland

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