 |
||||
|
||||
|
||||
|
||||
Previous Article | Next Article
The Journal of Neuroscience, December 1, 2000, 20(23):8812-8821
Stable Hebbian Learning from Spike Timing-Dependent Plasticity
M. C. W. van Rossum1, G. Q. Bi2, and G. G. Turrigiano1 1 Brandeis University, Department of Biology, Waltham, Massachusetts 02454-9110, and 2 University of California at San Diego, Department of Biology, La Jolla, California 92093-0357
We explore a synaptic plasticity model that incorporates recent findings that potentiation and depression can be induced by precisely timed pairs of synaptic events and postsynaptic spikes. In addition we include the observation that strong synapses undergo relatively less potentiation than weak synapses, whereas depression is independent of synaptic strength. After random stimulation, the synaptic weights reach an equilibrium distribution which is stable, unimodal, and has positive skew. This weight distribution compares favorably to the distributions of quantal amplitudes and of receptor number observed experimentally in central neurons and contrasts to the distribution found in plasticity models without size-dependent potentiation. Also in contrast to those models, which show strong competition between the synapses, stable plasticity is achieved with little competition. Instead, competition can be introduced by including a separate mechanism that scales synaptic strengths multiplicatively as a function of postsynaptic activity. In this model, synaptic weights change in proportion to how correlated they are with other inputs onto the same postsynaptic neuron. These results indicate that stable correlation-based plasticity can be achieved without introducing competition, suggesting that plasticity and competition need not coexist in all circuits or at all developmental stages.
Key words: Hebbian plasticity; synaptic weights; synaptic competition; activity-dependent scaling; temporal learning; stochastic approaches
Copyright © 2000 Society for Neuroscience 0270-6474/00/20238812-10$05.00/0
This article has been cited by other articles:
T. Elliott
Temporal Dynamics of Rate-Based Synaptic Plasticity Rules in a Stochastic Model of Spike-Timing-Dependent Plasticity
Neural Comput., September 1, 2008; 20(9): 2253 - 2307.
[Abstract] [Full Text] [PDF]
P. J. Sjostrom, E. A. Rancz, A. Roth, and M. Hausser
Dendritic Excitability and Synaptic Plasticity
Physiol Rev, April 1, 2008; 88(2): 769 - 840.
[Abstract] [Full Text] [PDF]
H. Urakubo, M. Honda, R. C. Froemke, and S. Kuroda
Requirement of an Allosteric Kinetics of NMDA Receptors for Spike Timing-Dependent Plasticity
J. Neurosci., March 26, 2008; 28(13): 3310 - 3323.
[Abstract] [Full Text] [PDF]
M. A. Farries and A. L. Fairhall
Reinforcement Learning With Modulated Spike Timing Dependent Synaptic Plasticity
J Neurophysiol, December 1, 2007; 98(6): 3648 - 3665.
[Abstract] [Full Text] [PDF]
E. Muller, L. Buesing, J. Schemmel, and K. Meier
Spike-Frequency Adapting Neural Ensembles: Beyond Mean Adaptation and Renewal Theories
Neural Comput., November 1, 2007; 19(11): 2958 - 3010.
[Abstract] [Full Text] [PDF]
T. Aoki and T. Aoyagi
Synchrony-Induced Switching Behavior of Spike Pattern Attractors Created by Spike-Timing-Dependent Plasticity
Neural Comput., October 1, 2007; 19(10): 2720 - 2738.
[Abstract] [Full Text] [PDF]
A. Morrison, A. Aertsen, and M. Diesmann
Spike-Timing-Dependent Plasticity in Balanced Random Networks
Neural Comput., June 1, 2007; 19(6): 1437 - 1467.
[Abstract] [Full Text] [PDF]
P. A. Appleby and T. Elliott
Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity
Neural Comput., May 1, 2007; 19(5): 1362 - 1399.
[Abstract] [Full Text] [PDF]
T. Toyoizumi, J.-P. Pfister, K. Aihara, and W. Gerstner
Optimality Model of Unsupervised Spike-Timing-Dependent Plasticity: Synaptic Memory and Weight Distribution.
Neural Comput., March 1, 2007; 19(3): 639 - 671.
[Abstract] [Full Text] [PDF]
V. Jacob, D. J. Brasier, I. Erchova, D. Feldman, and D. E. Shulz
Spike Timing-Dependent Synaptic Depression in the In Vivo Barrel Cortex of the Rat
J. Neurosci., February 7, 2007; 27(6): 1271 - 1284.
[Abstract] [Full Text] [PDF]
S. M. Bohte and M. C. Mozer
Reducing the variability of neural responses: a computational theory of spike-timing-dependent plasticity.
Neural Comput., February 1, 2007; 19(2): 371 - 403.
[Abstract] [Full Text] [PDF]
I. Rabinowitch and I. Segev
The Endurance and Selectivity of Spatial Patterns of Long-Term Potentiation/Depression in Dendrites under Homeostatic Synaptic Plasticity
J. Neurosci., December 27, 2006; 26(52): 13474 - 13484.
[Abstract] [Full Text] [PDF]
C. C. Rumsey and L. F. Abbott
Synaptic Democracy in Active Dendrites
J Neurophysiol, November 1, 2006; 96(5): 2307 - 2318.
[Abstract] [Full Text] [PDF]
P. A. Appleby and T. Elliott
Stable Competitive Dynamics Emerge from Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity.
Neural Comput., October 1, 2006; 18(10): 2414 - 2464.
[Abstract] [Full Text] [PDF]
J.-P. Pfister and W. Gerstner
Triplets of spikes in a model of spike timing-dependent plasticity.
J. Neurosci., September 20, 2006; 26(38): 9673 - 9682.
[Abstract] [Full Text] [PDF]
I. Rabinowitch and I. Segev
The Interplay Between Homeostatic Synaptic Plasticity and Functional Dendritic Compartments
J Neurophysiol, July 1, 2006; 96(1): 276 - 283.
[Abstract] [Full Text] [PDF]
P. J. Drew and L. F. Abbott
Extending the effects of spike-timing-dependent plasticity to behavioral timescales
PNAS, June 6, 2006; 103(23): 8876 - 8881.
[Abstract] [Full Text] [PDF]
J.-P. Pfister, T. Toyoizumi, D. Barber, and W. Gerstner
Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning.
Neural Comput., June 1, 2006; 18(6): 1318 - 1348.
[Abstract] [Full Text] [PDF]
A. P. Davison and Y. Fregnac
Learning Cross-Modal Spatial Transformations through Spike Timing-Dependent Plasticity
J. Neurosci., May 24, 2006; 26(21): 5604 - 5615.
[Abstract] [Full Text] [PDF]
R. C. Froemke, I. A. Tsay, M. Raad, J. D. Long, and Y. Dan
Contribution of Individual Spikes in Burst-Induced Long-Term Synaptic Modification
J Neurophysiol, March 1, 2006; 95(3): 1620 - 1629.
[Abstract] [Full Text] [PDF]
P. A. Appleby and T. Elliott
Synaptic and Temporal Ensemble Interpretation of Spike-Timing-Dependent Plasticity
Neural Comput., November 1, 2005; 17(11): 2316 - 2336.
[Abstract] [Full Text] [PDF]
R. Legenstein, C. Naeger, and W. Maass
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Neural Comput., November 1, 2005; 17(11): 2337 - 2382.
[Abstract] [Full Text] [PDF]
D. V. Buonomano
A Learning Rule for the Emergence of Stable Dynamics and Timing in Recurrent Networks
J Neurophysiol, October 1, 2005; 94(4): 2275 - 2283.
[Abstract] [Full Text] [PDF]
W. Senn and S. Fusi
Learning Only When Necessary: Better Memories of Correlated Patterns in Networks with Bounded Synapses
Neural Comput., October 1, 2005; 17(10): 2106 - 2138.
[Abstract] [Full Text] [PDF]
R. Guyonneau, R. VanRullen, and S. J. Thorpe
Neurons Tune to the Earliest Spikes Through STDP
Neural Comput., April 1, 2005; 17(4): 859 - 879.
[Abstract] [Full Text] [PDF]
M. J. E. Richardson and W. Gerstner
Synaptic Shot Noise and Conductance Fluctuations Affect the Membrane Voltage with Equal Significance
Neural Comput., April 1, 2005; 17(4): 923 - 947.
[Abstract] [Full Text] [PDF]
F. Worgotter and B. Porr
Temporal Sequence Learning, Prediction, and Control: A Review of Different Models and Their Relation to Biological Mechanisms
Neural Comput., February 1, 2005; 17(2): 245 - 319.
[Abstract] [Full Text] [PDF]
E. M. Izhikevich, J. A. Gally, and G. M. Edelman
Spike-timing Dynamics of Neuronal Groups
Cereb Cortex, August 1, 2004; 14(8): 933 - 944.
[Abstract] [Full Text] [PDF]
A. N. Burkitt, H. Meffin, and D. B. Grayden
Spike-Timing-Dependent Plasticity: The Relationship to Rate-Based Learning for Models with Weight Dynamics Determined by a Stable Fixed Point
Neural Comput., May 1, 2004; 16(5): 885 - 940.
[Abstract] [Full Text] [PDF]
C. C. Rumsey and L. F. Abbott
Equalization of Synaptic Efficacy by Activity- and Timing-Dependent Synaptic Plasticity
J Neurophysiol, May 1, 2004; 91(5): 2273 - 2280.
[Abstract] [Full Text] [PDF]
K. Kitano and T. Fukai
Temporal Characteristics of the Predictive Synchronous Firing Modeled by Spike-Timing-Dependent Plasticity
Learn. Mem., May 1, 2004; 11(3): 267 - 276.
[Abstract] [Full Text] [PDF]
N. Masuda and K. Aihara
Self-Organizing Dual Coding Based on Spike-Time-Dependent Plasticity
Neural Comput., March 1, 2004; 16(3): 627 - 663.
[Abstract] [Full Text] [PDF]
T. Nowotny, V. P. Zhigulin, A. I. Selverston, H. D. I. Abarbanel, and M. I. Rabinovich
Enhancement of Synchronization in a Hybrid Neural Circuit by Spike-Timing Dependent Plasticity
J. Neurosci., October 29, 2003; 23(30): 9776 - 9785.
[Abstract] [Full Text] [PDF]
E. M. Izhikevich and N. S. Desai
Relating STDP to BCM
Neural Comput., July 1, 2003; 15(7): 1511 - 1523.
[Abstract] [Full Text] [PDF]
D. Durstewitz
Self-Organizing Neural Integrator Predicts Interval Times through Climbing Activity
J. Neurosci., June 15, 2003; 23(12): 5342 - 5353.
[Abstract] [Full Text] [PDF]
R. Gutig, R. Aharonov, S. Rotter, and H. Sompolinsky
Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity
J. Neurosci., May 1, 2003; 23(9): 3697 - 3714.
[Abstract] [Full Text] [PDF]
H. Cateau and T. Fukai
A Stochastic Method to Predict the Consequence of Arbitrary Forms of Spike-Timing-Dependent Plasticity
Neural Comput., March 1, 2003; 15(3): 597 - 620.
[Abstract] [Full Text] [PDF]
H. Z. Shouval, M. F. Bear, and L. N Cooper
A unified model of NMDA receptor-dependent bidirectional synaptic plasticity
PNAS, August 6, 2002; 99(16): 10831 - 10836.
[Abstract] [Full Text] [PDF]
H. D. I. Abarbanel, R. Huerta, and M. I. Rabinovich
Dynamical model of long-term synaptic plasticity
PNAS, July 23, 2002; 99(15): 10132 - 10137.
[Abstract] [Full Text] [PDF]
|
|
|
|
 |
|