Abstract
This chapter introduces a new temporal learning rule, namely the Precise-Spike-Driven (PSD) Synaptic Plasticity, for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff (WH) rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity, 1st edn. Cambridge University Press, Cambridge (2002)
Ghosh-Dastidar, S., Adeli, H.: Spiking neural networks. Int. J. Neural Syst. 19(04), 295–308 (2009)
Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10(9), 1659–1671 (1997)
Shadlen, M.N., Movshon, J.A.: Synchrony unbound: review a critical evaluation of the temporal binding hypothesis. Neuron 24, 67–77 (1999)
Gütig, R., Sompolinsky, H.: The tempotron: a neuron that learns spike timing-based decisions. Nat. Neurosci. 9(3), 420–428 (2006)
Widrow, B., Lehr, M.: 30 years of adaptive neural networks: perceptron, madaline, and backpropagation. Proc. IEEE 78(9), 1415–1442 (1990)
Knudsen, E.I.: Supervised learning in the brain. J. Neurosci. 14(7), 3985–3997 (1994)
Thach, W.T.: On the specific role of the cerebellum in motor learning and cognition: clues from PET activation and lesion studies in man. Behav. Brain Sci. 19(3), 411–431 (1996)
Ito, M.: Mechanisms of motor learning in the cerebellum. Brain Res. 886(1–2), 237–245 (2000)
Carey, M.R., Medina, J.F., Lisberger, S.G.: Instructive signals for motor learning from visual cortical area MT. Nat. Neurosci. 8(6), 813–819 (2005)
Brader, J.M., Senn, W., Fusi, S.: Learning real-world stimuli in a neural network with spike-driven synaptic dynamics. Neural Comput. 19(11), 2881–2912 (2007)
Bohte, S.M., Kok, J.N., Poutré, J.A.L.: Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48(1–4), 17–37 (2002)
Ponulak, F.: ReSuMe-new supervised learning method for spiking neural networks. Institute of Control and Information Engineering, Poznoń University of Technology, Technical report (2005)
Florian, R.V.: The chronotron: a neuron that learns to fire temporally precise spike patterns. PLoS One 7(8), e40,233 (2012)
Mohemmed, A., Schliebs, S., Matsuda, S., Kasabov, N.: SPAN: spike pattern association neuron for learning spatio-temporal spike patterns. Int. J. Neural Syst. 22(04), 1250,012 (2012)
Yu, Q., Tang, H., Tan, K.C., Li, H.: Rapid feedforward computation by temporal encoding and learning with spiking neurons. IEEE Trans. Neural Netw. Learn. Syst. 24(10), 1539–1552 (2013)
Ponulak, F., Kasinski, A.: Supervised learning in spiking neural networks with resume: sequence learning, classification, and spike shifting. Neural Comput. 22(2), 467–510 (2010)
Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Netw. 14(6), 1569–1572 (2003)
Rossum, M.: A novel spike distance. Neural Comput. 13(4), 751–763 (2001)
Rieke, F., Warland, D., van Steveninck, R.D., Bialek, W.: Spikes: Exploring the Neural Code, 1st edn. MIT Press, Cambridge (1997)
Hu, J., Tang, H., Tan, K.C., Li, H., Shi, L.: A spike-timing-based integrated model for pattern recognition. Neural Comput. 25(2), 450–472 (2013)
Gardner, E.: The space of interactions in neural networks models. J. Phys. A21, 257–270 (1988)
Foehring, R.C., Lorenzon, N.M.: Neuromodulation, development and synaptic plasticity. Can. J. Exp. Psychol./Rev. Canadienne de Psychologie Expérimentale 53(1), 45–61 (1999)
Seamans, J.K., Yang, C.R., et al.: The principal features and mechanisms of dopamine modulation in the prefrontal cortex. Prog. Neurobiol. 74(1), 1–57 (2004)
Artola, A., Bröcher, S., Singer, W.: Different voltage-dependent thresholds for inducing long-term depressiona and long-term potentiation in slices of rat visual cortex. Nature 347, 69–72 (1990)
Ngezahayo, A., Schachner, M., Artola, A.: Synaptic activity modulates the induction of bidirectional synaptic changes in adult mouse hippocampus. J. Neurosci. 20(7), 2451–2458 (2000)
Lisman, J., Spruston, N.: Postsynaptic depolarization requirements for LTP and LTD: a critique of spike timing-dependent plasticity. Nat. Neurosci. 8(7), 839–841 (2005)
Froemke, R.C., Poo, M.M., Dan, Y.: Spike-timing-dependent synaptic plasticity depends on dendritic location. Nature 434(7030), 221–225 (2005)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Yu, Q., Tang, H., Hu, J., Tan, K. (2017). Precise-Spike-Driven Synaptic Plasticity for Hetero Association of Spatiotemporal Spike Patterns. In: Neuromorphic Cognitive Systems. Intelligent Systems Reference Library, vol 126. Springer, Cham. https://doi.org/10.1007/978-3-319-55310-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-55310-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55308-5
Online ISBN: 978-3-319-55310-8
eBook Packages: EngineeringEngineering (R0)