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Balancing the critical period of spiking neurons with attractor-less STDP

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Attractor-based ("multiplicative") STDP has been called a biologically more realistic form of spike timing dependent plasticity than attractor-less ("additive") variants [14], as it produces unimodal distributions of synaptic weights [5, 6] when given poisson-distributed random input spike trains [1, 2, 4, 79]. While unimodal weight distributions have been observed as an outcome in some in vitro experiments [5], the actual biochemical process that changes synaptic connection strengths has yet to be fully understood.

Unfortunately, attractor-based STDP has been repeatedly shown to be computationally less powerful than attractor-less STDP [9] and successful implementations of attractor-based STDP used attractors that were either very weak [8] or very close to some minimum weight [2, 3], causing a more "additive-like" [2, 8] behaviour of the plasticity rule. We therefore examined possible biological interpretations of attractor-less STDP rules while keeping in mind that any STDP rule is just an abstraction from the hidden biophysical reality.

We show how unimodal weight distributions can reliably result from attractor-less STDP when negative synaptic drift is combined with activity-independent synaptic growth. A bimodal distribution is then only formed when non-random (polychromous [10]) poisson-distributed inputs are presented to a neuron [11]. In practice, this produces a plasticity rule that keeps the postsynaptic neuron unselective and responsive to a broad range of inputs while receiving only random spikes, but quickly allows the neuron's receptive field to become highly selective as soon as some inputs start repeating a non-random ordering of spikes.

This stabilization procedure preserves STDP's sensitivity to temporally shifted correlations in input spike data, which in turn gives us several beneficial features for biologically more realistic and computationally more powerful [12] implementations of plasticity in spiking neural networks.


  1. 1.

    Rubin J, Lee D, Sompolinsky H: Equilibrium Properties of Temporally Asymmetric Hebbian Plasticity. Phys Rev Lett. 2001, 86 (2): 364-367. doi:10.1103/PhysRevLett.86.364

  2. 2.

    Gütig R, Aharonov R, Rotter S, Sompolinsky H: Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity. J Neurosci. 2003, 23 (9): 3697-3714.

  3. 3.

    Morrison A, Aertsen A, Diesmann M: Spike-timing-dependent plasticity in balanced random networks. Neural Comput. 2007, 19 (6): 1437-1467. doi:10.1162/neco.2007.19.6.1437

  4. 4.

    van Rossum MCW, Bi GQ, Turrigiano GG: Stable Hebbian learning from spike timing-dependent plasticity. J Neurosci. 2000, 20 (23): 8812-8821.

  5. 5.

    Turrigiano GG, Leslie KR, Desai NS, Rutherford LC, Nelson SB: Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature. 1998, 391 (6670): 892-896. doi:10.1038/36103

  6. 6.

    Turrigiano GG, Nelson SB: Homeostatic plasticity in the developing nervous system. Nat Rev Neurosci. 2004, 5 (2): 97-107. doi:10.1038/nrn1327

  7. 7.

    Sjöström PJ, Turrigiano GG, Nelson SB: Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron. 2001, 32 (6): 1149-1164.

  8. 8.

    Gilson M, Burkitt AN, Grayden DB, Thomas Da, van Hemmen JL: Emergence of network structure due to [STDP] in recurrent neuronal networks V: self-organization schemes and weight dependence. Biol Cybern. 2010, 103 (5): 365-386.

  9. 9.

    Billings G, van Rossum MCW: Memory retention and spike-timing-dependent plasticity. J Neurophysiol. 2009, 101 (6): 2775-2788. doi:10.1152/jn.91007.2008

  10. 10.

    Izhikevich EM: Polychronization: computation with spikes. Neural Comput. 2006, 18 (2): 245-282.

  11. 11.

    Guyonneau R, VanRullen R, Thorpe SJ: Neurons tune to the earliest spikes through STDP. Neural Comput. 2005, 17 (4): 859-879. doi:10.1162/0899766053429390

  12. 12.

    Olshausen BA, Field DJ: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature. 1996, 381 (6583): 607-609. doi:10.1038/381607a0

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Correspondence to Simon M Vogt.

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  • Receptive Field
  • Spike Train
  • Synaptic Weight
  • Postsynaptic Neuron
  • Random Input