An Evolutionary Framework for Replicating Neurophysiological Data with Spiking Neural Networks

  • Emily L. RoundsEmail author
  • Eric O. Scott
  • Andrew S. Alexander
  • Kenneth A. De Jong
  • Douglas A. Nitz
  • Jeffrey L. Krichmar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9921)


Here we present a framework for the automatic tuning of spiking neural networks (SNNs) that utilizes an evolutionary algorithm featuring indirect encoding to achieve a drastic reduction in the dimensionality of the parameter space, combined with a GPU-accelerated SNN simulator that results in a considerable decrease in the time needed for fitness evaluation, despite the need for both a training and a testing phase. We tuned the parameters governing a learning rule called spike-timing-dependent plasticity (STDP), which was used to alter the synaptic weights of the network. We validated this framework by applying it to a case study in which synthetic neuronal firing rates were matched to electrophysiologically recorded neuronal firing rates in order to evolve network functionality. Our framework was not only able to match their firing rates, but also captured functional and behavioral aspects of the biological neuronal population, in roughly 50 generations.


Spiking neural networks Evolutionary algorithms Indirect encoding Neurophysiological recordings Plasticity Data matching Parallel computing 



Supported by the National Science Foundation (Award IIS-1302125).


  1. 1.
    Alexander, A.S., Nitz, D.A.: Retrosplenial cortex maps the conjunction of internal and external spaces. Nat. Neurosci. 18(8), 1143–1151 (2015)CrossRefGoogle Scholar
  2. 2.
    Asher, D.E., Krichmar, J.L., Oros, N.: Evolution of biologically plausible neural networks performing a visually guided reaching task. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation (GECCO 2014), pp. 145–152. ACM, New York (2014)Google Scholar
  3. 3.
    Beyeler, M., Carlson, K.D., Chou, T.-S., Dutt, N., Krichmar, J.L.: CARLsim 3: a user-friendly and highly optimized library for thecreation of neurobiologically detailed spiking neural networks. In: 2015 International Joint Conference on Neural Networks (IJCNN 2015), pp. 1–8. IEEE (2015)Google Scholar
  4. 4.
    Bi, G.-Q., Poo, M.-M.: Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18(24), 10464–10472 (1998)Google Scholar
  5. 5.
    Carlson, K.D., Nageswaran, J.M., Dutt, N., Krichmar, J.L.: An efficient automated parameter tuning framework for spiking neuralnetworks. Front. Neurosci. 8 (2014)Google Scholar
  6. 6.
    Carlson, K.D., Richert, M., Dutt, N., Krichmar, J.L.: Biologically plausible models of homeostasis and STDP: stabilityand learning in spiking neural networks. In: The 2013 International Joint Conference on Neural Networks (IJCNN 2013), pp. 1–8. IEEE (2013)Google Scholar
  7. 7.
    Carnevale, F., deLafuente, V., Romo, R., Barak, O., Parga, N.: Dynamic control of response criterion in premotor cortex during perceptual detection under temporal uncertainty. Neuron 86(4), 1067–1077 (2015)CrossRefGoogle Scholar
  8. 8.
    Fountas, Z., Shanahan, M.: GPU-based fast parameter optimization for phenomenological spikingneural models. In: 2015 International Joint Conference on Neural Networks (IJCNN 2015), pp. 1–8, July 2015Google Scholar
  9. 9.
    Hu, M., Li, H., Chen, Y., Wu, Q., Rose, G.S., Linderman, R.W.: Memristor crossbar-based neuromorphic computing system: a case study. IEEE Trans. Neural Networks Learn. Syst. 25(10), 1864–1878 (2014)CrossRefGoogle Scholar
  10. 10.
    Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Networks 14(6), 1569–1572 (2003)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Izhikevich, E.M., Desai, N.S.: Relating STDP to BCM. Neural Comput. 15(7), 1511–1523 (2003)CrossRefzbMATHGoogle Scholar
  12. 12.
    Krichmar, J.L., Coussy, P., Dutt, N.: Large-scale spiking neural networks using neuromorphic hardwarecompatible models. ACM J. Emerging Technol. Comput. Syst. (JETC), 11(4) (2015). Article no. 36Google Scholar
  13. 13.
    Mante, V., Sussillo, D., Shenoy, K.V., Newsome, W.T.: Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503(7474), 78–84 (2013)CrossRefGoogle Scholar
  14. 14.
    Prinz, A.A., Billimoria, C.P., Marder, E.: Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J. Neurophysiol. 90(6), 3998–4015 (2003)CrossRefGoogle Scholar
  15. 15.
    Prinz, A.A., Bucher, D., Marder, E.: Similar network activity from disparate circuit parameters. Nat. Neurosci. 7(12), 1345–1352 (2004)CrossRefGoogle Scholar
  16. 16.
    Rossant, C., Goodman, D.F.M., Fontaine, B., Platkiewicz, J., Magnusson, A.K., Brette, R.: Fitting neuron models to spike trains. Front. Neurosci. 5(9) (2011)Google Scholar
  17. 17.
    Song, H.F., Yang, G.R., Wang, X.J.: Training excitatory-inhibitory recurrent neural networks forcognitive tasks: a simple and flexible framework. PLoS Comput. Biol. 12(2), e1004792 (2016)CrossRefGoogle Scholar
  18. 18.
    Stanley, K.O., D’Ambrosio, D.B., Gauci, J.: A hypercube-based encoding for evolving large-scale neural networks. Artif. Life 15(2), 185–212 (2009)CrossRefGoogle Scholar
  19. 19.
    Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)CrossRefGoogle Scholar
  20. 20.
    Tripathy, S.J., Savitskaya, J., Burton, S.D., Urban, N.N., Gerkin, R.C.: Neuroelectro: a window to the world’s neuron electrophysiology data. Front. Neuroinf. 8 (2014)Google Scholar
  21. 21.
    White, D.R.: Software review: the ECJ toolkit. Genet. Program. Evolvable Mach. 13(1), 65–67 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Emily L. Rounds
    • 1
    Email author
  • Eric O. Scott
    • 2
  • Andrew S. Alexander
    • 3
  • Kenneth A. De Jong
    • 2
  • Douglas A. Nitz
    • 3
  • Jeffrey L. Krichmar
    • 1
  1. 1.Department of Cognitive SciencesUniversity of California, IrvineIrvineUSA
  2. 2.Department of Computer ScienceGeorge Mason UniversityFairfaxUSA
  3. 3.Department of Cognitive ScienceUniversity of California, San DiegoLa JollaUSA

Personalised recommendations