Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

Friedemann Zenke; Everton J. Agnes; Wulfram Gerstner

doi:10.1038/ncomms7922

journal article Open Access Apr 21, 2015

Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

Friedemann Zenke

Everton J. Agnes

Wulfram Gerstner

Nature Communications Vol. 6 No. 1 · Springer Science and Business Media LLC

View at Publisher Save 10.1038/ncomms7922

Abstract

AbstractSynaptic plasticity, the putative basis of learning and memory formation, manifests in various forms and across different timescales. Here we show that the interaction of Hebbian homosynaptic plasticity with rapid non-Hebbian heterosynaptic plasticity is, when complemented with slower homeostatic changes and consolidation, sufficient for assembly formation and memory recall in a spiking recurrent network model of excitatory and inhibitory neurons. In the model, assemblies were formed during repeated sensory stimulation and characterized by strong recurrent excitatory connections. Even days after formation, and despite ongoing network activity and synaptic plasticity, memories could be recalled through selective delay activity following the brief stimulation of a subset of assembly neurons. Blocking any component of plasticity prevented stable functioning as a memory network. Our modelling results suggest that the diversity of plasticity phenomena in the brain is orchestrated towards achieving common functional goals.

Topics

No keywords indexed for this article. Browse by subject →

References

67

[1]

Hebb, D. O. The Organization of Behavior: A Neuropsychological Theory Wiley & Sons New York (1949).

[2]

Markram, H., Gerstner, W. & Sjöström, P. J. A history of spike-timing-dependent plasticity. Front. Synaptic Neurosci. 3, 4 (2011). 10.3389/fnsyn.2011.00004

[3]

Fuster, J. M. & Jervey, J. P. Neuronal firing in the inferotemporal cortex of the monkey in a visual memory task. J. Neurosci. 2, 361–375 (1982). 10.1523/jneurosci.02-03-00361.1982

[4]

Goldman-Rakic, P. S. Cellular basis of working memory. Neuron 14, 477–485 (1995). 10.1016/0896-6273(95)90304-6

[5]

Gelbard-Sagiv, H., Mukamel, R., Harel, M., Malach, R. & Fried, I. Internally generated reactivation of single neurons in human hippocampus during free recall. Science 322, 96–101 (2008). 10.1126/science.1164685

[6]

Niessing, J. & Friedrich, R. W. Olfactory pattern classification by discrete neuronal network states. Nature 465, (2010). 10.1038/nature08961

[7]

Bathellier, B., Ushakova, L. & Rumpel, S. Discrete neocortical dynamics predict behavioral categorization of sounds. Neuron 76, 435–449 (2012). 10.1016/j.neuron.2012.07.008

[8]

Neural networks and physical systems with emergent collective computational abilities.

J J Hopfield

Proceedings of the National Academy of Sciences 1982 10.1073/pnas.79.8.2554

[9]

Gerstner, W. & van Hemmen, J. Associative memory in a network of ‘spiking’ neurons. Network. 3, 139–164 (1992). 10.1088/0954-898x_3_2_004

[10]

Mongillo, G., Barak, O. & Tsodyks, M. Synaptic theory of working memory. Science 319, 1543–1546 (2008). 10.1126/science.1150769

[11]

Amit, D. J. & Brunel, N. Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cereb. Cortex 7, 237–252 (1997). 10.1093/cercor/7.3.237

[12]

Rochester, N., Holland, J., Haibt, L. & Duda, W. Tests on a cell assembly theory of the action of the brain, using a large digital computer. IEEE Trans. Inf. Theory 2, 80–93 (1956). 10.1109/tit.1956.1056810

[13]

Fusi, S. Hebbian spike-driven synaptic plasticity for learning patterns of mean firing rates. Biol. Cybern. 87, 459–470 (2002). 10.1007/s00422-002-0356-8

[14]

Mongillo, G., Curti, E., Romani, S. & Amit, D. J. Learning in realistic networks of spiking neurons and spike-driven plastic synapses. Eur. J. Neurosci. 21, 3143–3160 (2005). 10.1111/j.1460-9568.2005.04087.x

[15]

Abbott, L. F. & Nelson, S. B. Synaptic plasticity: taming the beast. Nat. Neurosci. 3, 1178–1183 (2000). 10.1038/81453

[16]

Bliss, T. V. P. & Lømo, T. Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J. Physiol. 232, 331–356 (1973). 10.1113/jphysiol.1973.sp010273

[17]

Artola, A., Bröcher, S. & Singer, W. Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex. Nature 347, 69–72 (1990). 10.1038/347069a0

[18]

Markram, H., Lübke, J., Frotscher, M. & Sakmann, B. Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275, 213–215 (1997). 10.1126/science.275.5297.213

[19]

Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type

Guo-qiang Bi, Mu-ming Poo

The Journal of Neuroscience 1998 10.1523/jneurosci.18-24-10464.1998

[20]

Sjöström, P. J., Turrigiano, G. G. & Nelson, S. B. Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron 32, 1149–1164 (2001). 10.1016/s0896-6273(01)00542-6

[21]

Chistiakova, M., Bannon, N. M., Bazhenov, M. & Volgushev, M. Heterosynaptic plasticity multiple mechanisms and multiple roles. Neuroscientist 20, 483–498 (2014). 10.1177/1073858414529829

[22]

Lynch, G. S., Dunwiddie, T. & Gribkoff, V. Heterosynaptic depression: a postsynaptic correlate of long-term potentiation. Nature 266, 737–739 (1977). 10.1038/266737a0

[23]

Lisman, J. E. A mechanism for memory storage insensitive to molecular turnover: a bistable autophosphorylating kinase. Proc. Natl Acad. Sci. USA 82, 3055–3057 (1985). 10.1073/pnas.82.9.3055

[24]

Frey, U. & Morris, R. G. M. Synaptic tagging and long-term potentiation. Nature 385, 533–536 (1997). 10.1038/385533a0

[25]

Stepanyants, A., Hof, P. R. & Chklovskii, D. B. Geometry and structural plasticity of synaptic connectivity. Neuron 34, 275–288 (2002). 10.1016/s0896-6273(02)00652-9

[26]

Trachtenberg, J. T. et al. Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex. Nature 420, 788–794 (2002). 10.1038/nature01273

[27]

Abbott, L. F., Varela, J. A., Sen, K. & Nelson, S. B. Synaptic depression and cortical gain control. Science 275, 220–224 (1997). 10.1126/science.275.5297.221

[28]

Markram, H., Wang, Y. & Tsodyks, M. Differential signaling via the same axon of neocortical pyramidal neurons. Proc. Natl Acad. Sci. USA 95, 5323–5328 (1998). 10.1073/pnas.95.9.5323

[29]

Turrigiano, G. G. & Nelson, S. B. Hebb and homeostasis in neuronal plasticity. Curr. Opin. Neurobiol. 10, 358–364 (2000). 10.1016/s0959-4388(00)00091-x

[30]

Pfister, J.-P. & Gerstner, W. Triplets of spikes in a model of spike timing-dependent plasticity. J. Neurosci. 26, 9673–9682 (2006). 10.1523/jneurosci.1425-06.2006

[31]

Crow, T. J. Cortical synapses and reinforcement: a hypothesis. Nature 219, 736–737 (1968). 10.1038/219736a0

[32]

Izhikevich, E. M. Solving the distal reward problem through linkage of STDP and dopamine signaling. Cereb. Cortex 17, 2443–2452 (2007). 10.1093/cercor/bhl152

[33]

Pawlak, V., Wickens, J. R., Kirkwood, A. & Kerr, J. N. D. Timing is not everything: neuromodulation opens the STDP gate. Front. Synaptic Neurosci. 2, 146 (2010). 10.3389/fnsyn.2010.00146

[34]

A Neural Substrate of Prediction and Reward

Wolfram Schultz, Peter Dayan, P. Read Montague

Science 1997 10.1126/science.275.5306.1593

[35]

Turrigiano, G. G. Homeostatic synaptic plasticity: local and global mechanisms for stabilizing neuronal function. Cold Spring Harb. Perspect. Biol. 4, a005736 (2012). 10.1101/cshperspect.a005736

[36]

Treves, A. Mean-field analysis of neuronal spike dynamics. Network 4, 259–284 (1993). 10.1088/0954-898x_4_3_002

[37]

Song, S., Miller, K. D. & Abbott, L. F. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3, 919–926 (2000). 10.1038/78829

[38]

Senn, W., Markram, H. & Tsodyks, M. An algorithm for modifying neurotransmitter release probability based on pre- and postsynaptic spike timing. Neural. Comput. 13, 35–67 (2001). 10.1162/089976601300014628

[39]

Shouval, H. Z., Bear, M. F. & Cooper, L. N. A unified model of NMDA receptor-dependent bidirectional synaptic plasticity. Proc. Natl Acad. Sci. USA 99, 10831–10836 (2002). 10.1073/pnas.152343099

[40]

Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex

EL Bienenstock, LN Cooper, PW Munro

The Journal of Neuroscience 1982 10.1523/jneurosci.02-01-00032.1982

[41]

Clopath, C., Büsing, L., Vasilaki, E. & Gerstner, W. Connectivity reflects coding: a model of voltage-based STDP with homeostasis. Nat. Neurosci. 13, 344–352 (2010). 10.1038/nn.2479

[42]

Zenke, F., Hennequin, G. & Gerstner, W. Synaptic plasticity in neural networks needs homeostasis with a fast rate detector. PLoS Comput. Biol. 9, e1003330 (2013). 10.1371/journal.pcbi.1003330

[43]

Mongillo, G., Hansel, D. & van Vreeswijk, C. Bistability and spatiotemporal irregularity in neuronal networks with nonlinear synaptic transmission. Phys. Rev. Lett. 108, 158101 (2012). 10.1103/physrevlett.108.158101

[44]

Hansel, D. & Mato, G. Short-term plasticity explains irregular persistent activity in working memory tasks. J. Neurosci. 33, 133–149 (2013). 10.1523/jneurosci.3455-12.2013

[45]

Wang, X.-J. Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. J. Neurosci. 19, 9587–9603 (1999). 10.1523/jneurosci.19-21-09587.1999

[46]

Ermentrout, B. Linearization of F-I curves by adaptation. Neural. Comput. 10, 1721–1729 (1998). 10.1162/089976698300017106

[47]

Benda, J., Maler, L. & Longtin, A. Linear versus nonlinear signal transmission in neuron models with adaptation currents or dynamic thresholds. J. Neurophysiol. 104, 2806–2820 (2010). 10.1152/jn.00240.2010

[48]

Gerstner, W. & Kistler, W. M. Spiking Neuron Models: Single Neurons, Populations, Plasticity 1st edn Cambridge University Press (2002). 10.1017/cbo9780511815706

[49]

Chen, J.-Y. et al. Heterosynaptic plasticity prevents runaway synaptic dynamics. J. Neurosci. 33, 15915–15929 (2013). 10.1523/jneurosci.5088-12.2013

[50]

Woodin, M. A., Ganguly, K. & Poo, M.-M. Coincident pre- and postsynaptic activity modifies GABAergic synapses by postsynaptic changes in Cl- transporter activity. Neuron 39, 807–820 (2003). 10.1016/s0896-6273(03)00507-5

Showing 50 of 67 references

Cited By

343

Burst firing creates an attractor in synaptic weight dynamics

Kathleen Jacquerie, Danil Tyulmankov · 2026

PLOS Computational Biology

Adaptive Synaptic Scaling in Spiking Networks for Continual Learning and Enhanced Robustness

Mingkun Xu, Faqiang Liu · 2025

IEEE Transactions on Neural Network...

Taming the chaos gently: a predictive alignment learning rule in recurrent neural networks

Toshitake Asabuki, Claudia Clopath · 2025

Nature Communications

Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks

Everton J. Agnes, Tim P. Vogels · 2024

Nature Neuroscience

Inhibitory neurons control the consolidation of neural assemblies via adaptation to selective stimuli

Raphaël Bergoin, Alessandro Torcini · 2023

Scientific Reports

Homeostatic control of synaptic rewiring in recurrent networks induces the formation of stable memory engrams

Júlia V. Gallinaro, Nebojša Gašparović · 2022

PLOS Computational Biology

Quantum superposition inspired spiking neural network

Yinqian Sun, Yi Zeng · 2021

iScience

Hebbian plasticity in parallel synaptic pathways: A circuit mechanism for systems memory consolidation

Michiel W. H. Remme, Urs Bergmann · 2021

PLOS Computational Biology

Neural mechanisms of attending to items in working memory

Sanjay G. Manohar, Nahid Zokaei · 2019

Neuroscience & Biobehavioral Re...

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks

Friedemann Zenke, Surya Ganguli · 2018

Neural Computation

A Hippocampal Model for Behavioral Time Acquisition and Fast Bidirectional Replay of Spatio-Temporal Memory Sequences

Marcelo Matheus Gauy, Johannes Lengler · 2018

Frontiers in Neuroscience

The temporal paradox of Hebbian learning and homeostatic plasticity

Friedemann Zenke, Wulfram Gerstner · 2017

Current Opinion in Neurobiology

The Human Brain Project: Creating a European Research Infrastructure to Decode the Human Brain

Katrin Amunts, Christoph Ebell · 2016

Neuron

Metrics

343

Citations

67

References

Details

Published: Apr 21, 2015
Vol/Issue: 6(1)
License: View

Authors

Cite This Article

Friedemann Zenke, Everton J. Agnes, Wulfram Gerstner (2015). Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks. Nature Communications, 6(1). https://doi.org/10.1038/ncomms7922

Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

You May Also Like