Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensation

Edited by Terrence J. Sejnowski, Salk Institute for Biological Studies, La Jolla, CA, and approved October 1, 2021 (received for review November 30, 2020)
November 12, 2021
118 (46) e2023832118

Significance

Associative memories are thought to be represented by neuronal assemblies, ensembles of nerve cells with strong synaptic interconnectivity. Experiments have, however, shown that synapses can change spontaneously. Motivated by this and by experimentally observed changes of representations, we propose that assemblies that drift freely in the brain, due to noisy network activity and spontaneous synaptic changes, are the basis of associative memory. How can memories and behaviors persist despite these changes? We find that simple, teacher-free synaptic plasticity compensates the drift and thereby solves the conundrum. When such plasticity is incorporated, networks with drifting assemblies can maintain stable memories and basic computations. The mechanisms underlying drift and compensation may apply to different kinds of drifting neural representations.

Abstract

Change is ubiquitous in living beings. In particular, the connectome and neural representations can change. Nevertheless, behaviors and memories often persist over long times. In a standard model, associative memories are represented by assemblies of strongly interconnected neurons. For faithful storage these assemblies are assumed to consist of the same neurons over time. Here we propose a contrasting memory model with complete temporal remodeling of assemblies, based on experimentally observed changes of synapses and neural representations. The assemblies drift freely as noisy autonomous network activity and spontaneous synaptic turnover induce neuron exchange. The gradual exchange allows activity-dependent and homeostatic plasticity to conserve the representational structure and keep inputs, outputs, and assemblies consistent. This leads to persistent memory. Our findings explain recent experimental results on temporal evolution of fear memory representations and suggest that memory systems need to be understood in their completeness as individual parts may constantly change.

Continue Reading

Data Availability

Code to reproduce the main results of this article have been deposited in GitHub at https://github.com/fkalle/drifting-assemblies.

Acknowledgments

We thank Paul Züge for fruitful discussions; Abigail Morrison for comments on the manuscript; Hans Günter Memmesheimer, Katharina Hack, and Jonas Nietzsche for help with the graphical illustrations; and the German Federal Ministry of Education and Research for support via the Bernstein Network (Bernstein Award 2014, 01GQ1710).

Supporting Information

Appendix 01 (PDF)

References

1
S. Rumpel, J. Triesch, The dynamic connectome. e-Neuroforum 22, 48–53 (2016).
2
Y. Humeau, D. Choquet, The next generation of approaches to investigate the link between synaptic plasticity and learning. Nat. Neurosci. 22, 1536–1543 (2019).
3
N. Yasumatsu, M. Matsuzaki, T. Miyazaki, J. Noguchi, H. Kasai, Principles of long-term dynamics of dendritic spines. J. Neurosci. 28, 13592–13608 (2008).
4
A. Rubinski, N. E. Ziv, Remodeling and tenacity of inhibitory synapses: Relationships with network activity and neighboring excitatory synapses. PLOS Comput. Biol. 11, e1004632 (2015).
5
R. Dvorkin, N. E. Ziv, Relative contributions of specific activity histories and spontaneous processes to size remodeling of glutamatergic synapses. PLoS Biol. 14, e1002572 (2016).
6
K. P. Berry, E. Nedivi, Spine dynamics: Are they all the same? Neuron 96, 43–55 (2017).
7
N. E. Ziv, N. Brenner, Synaptic tenacity or lack thereof: Spontaneous remodeling of synapses. Trends Neurosci. 41, 89–99 (2018).
8
L. A. DeNardo et al., Temporal evolution of cortical ensembles promoting remote memory retrieval. Nat. Neurosci. 22, 460–469 (2019).
9
C. Clopath, T. Bonhoeffer, M. Hübener, T. Rose, Variance and invariance of neuronal long-term representations. Philos. Trans. R. Soc. Lond. B Biol. Sci. 372, 20160161 (2017).
10
M. E. Rule, T. O’Leary, C. D. Harvey, Causes and consequences of representational drift. Curr. Opin. Neurobiol. 58, 141–147 (2019).
11
G. Buzsáki, Neural syntax: Cell assemblies, synapsembles, and readers. Neuron 68, 362–385 (2010).
12
W. Gerstner, W. M. Kistler, R. Naud, L. Paninski, Neuronal Dynamics–From Single Neurons to Networks and Models of Cognition (Cambridge University Press, Cambridge, UK, 2014).
13
G. Mongillo, S. Rumpel, Y. Loewenstein, Intrinsic volatility of synaptic connections - A challenge to the synaptic trace theory of memory. Curr. Opin. Neurobiol. 46, 7–13 (2017).
14
A. Scott, Neuroscience: A Mathematical Primer (Springer, New York, 2002).
15
T. P. Vogels, H. Sprekeler, F. Zenke, C. Clopath, W. Gerstner, Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks. Science 334, 1569–1573 (2011).
16
A. Litwin-Kumar, B. Doiron, Formation and maintenance of neuronal assemblies through synaptic plasticity. Nat. Commun. 5, 5319 (2014).
17
F. Zenke, E. J. Agnes, W. Gerstner, Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks. Nat. Commun. 6, 6922 (2015).
18
N. Ravid Tannenbaum, Y. Burak, Shaping neural circuits by high order synaptic interactions. PLOS Comput. Biol. 12, e1005056 (2016).
19
G. K. Ocker, B. Doiron, Training and spontaneous reinforcement of neuronal assemblies by spike timing plasticity. Cereb. Cortex 29, 937–951 (2019).
20
J. Herpich, C. Tetzlaff, Principles underlying the input-dependent formation and organization of memories. Netw. Neurosci. 3, 606–634 (2019).
21
J. Humble, K. Hiratsuka, H. Kasai, T. Toyoizumi, Intrinsic spine dynamics are critical for recurrent network learning in models with and without autism spectrum disorder. Front. Comput. Neurosci. 13, 38 (2019).
22
L. Montangie, C. Miehl, J. Gjorgjieva, Autonomous emergence of connectivity assemblies via spike triplet interactions. PLOS Comput. Biol. 16, e1007835 (2020).
23
M. J. Fauth, M. C. van Rossum, Self-organized reactivation maintains and reinforces memories despite synaptic turnover. eLife 8, e43717 (2019).
24
L. Wittgenstein, Philosophische Untersuchungen/Philosophical Investigations (Wiley-Blackwell, Oxford, UK, 2009).
25
T. Hainmueller, M. Bartos, Parallel emergence of stable and dynamic memory engrams in the hippocampus. Nature 558, 292–296 (2018).
26
R. K. Mishra, S. Kim, S. J. Guzman, P. Jonas, Symmetric spike timing-dependent plasticity at CA3-CA3 synapses optimizes storage and recall in autoassociative networks. Nat. Commun. 7, 11552 (2016).
27
E. L. Bienenstock, L. N. Cooper, P. W. Munro, Theory for the development of neuron selectivity: Orientation specificity and binocular interaction in visual cortex. J. Neurosci. 2, 32–48 (1982).
28
I. R. Fiete, W. Senn, C. Z. H. Wang, R. H. R. Hahnloser, Spike-time-dependent plasticity and heterosynaptic competition organize networks to produce long scale-free sequences of neural activity. Neuron 65, 563–576 (2010).
29
A. Lazar, G. Pipa, J. Triesch, SORN: A self-organizing recurrent neural network. Front. Comput. Neurosci. 3, 23 (2009).
30
R. Quian Quiroga, Plugging in to human memory: Advantages, challenges, and insights from human single-neuron recordings. Cell 179, 1015–1032 (2019).
31
C. Gardiner, Handbook of Stochastic Methods (Springer, Berlin, Germany, 2002).
32
W. Horsthemke, R. Lefever, Noise-Induced Transitions (Springer, Berlin, Germany, 1984).
33
D. B. Headley, V. Kanta, P. Kyriazi, D. Paré, Embracing complexity in defensive networks. Neuron 103, 189–201 (2019).
34
N. Karalis et al., 4-Hz oscillations synchronize prefrontal-amygdala circuits during fear behavior. Nat. Neurosci. 19, 605–612 (2016).
35
A. Attardo et al., Long-term consolidation of ensemble neural plasticity patterns in hippocampal area CA1. Cell Rep. 25, 640–650.e2 (2018).
36
A. U. Sugden et al., Cortical reactivations of recent sensory experiences predict bidirectional network changes during learning. Nat. Neurosci. 23, 981–991 (2020).
37
R. Kempter, W. Gerstner, J. L. Van Hemmen, Hebbian learning and spiking neurons. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics 59, 4498 (1999).
38
V. Pernice, B. Staude, S. Cardanobile, S. Rotter, How structure determines correlations in neuronal networks. PLOS Comput. Biol. 7, e1002059 (2011).
39
F. Y. Kalle Kossio, S. Goedeke, B. van den Akker, B. Ibarz, R. M. Memmesheimer, Growing critical: Self-organized criticality in a developing neural system. Phys. Rev. Lett. 121, 058301 (2018).
40
D. V. Buonomano, A learning rule for the emergence of stable dynamics and timing in recurrent networks. J. Neurophysiol. 94, 2275–2283 (2005).
41
C. Clopath, L. Büsing, E. Vasilaki, W. Gerstner, Connectivity reflects coding: A model of voltage-based STDP with homeostasis. Nat. Neurosci. 13, 344–352 (2010).
42
A. Renart et al., The asynchronous state in cortical circuits. Science 327, 587–590 (2010).
43
A. R. Chambers, S. Rumpel, A stable brain from unstable components: Emerging concepts and implications for neural computation. Neuroscience 357, 172–184 (2017).
44
U. Rokni, A. G. Richardson, E. Bizzi, H. S. Seung, Motor learning with unstable neural representations. Neuron 54, 653–666 (2007).
45
G. Mongillo, S. Rumpel, Y. Loewenstein, Inhibitory connectivity defines the realm of excitatory plasticity. Nat. Neurosci. 21, 1463–1470 (2018).
46
L. Susman, N. Brenner, O. Barak, Stable memory with unstable synapses. Nat. Commun. 10, 4441 (2019).
47
M. Gillett, U. Pereira, N. Brunel, Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning. Proc. Natl. Acad. Sci. U.S.A. 117, 29948–29958 (2019).
48
R. Ajemian, A. D’Ausilio, H. Moorman, E. Bizzi, A theory for how sensorimotor skills are learned and retained in noisy and nonstationary neural circuits. Proc. Natl. Acad. Sci. U.S.A. 110, E5078–E5087 (2013).
49
D. Kappel, R. Legenstein, S. Habenschuss, M. Hsieh, W. Maass, A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning. eneuro 5, 0301–17.2018 (2018).
50
D. Acker, S. Paradis, P. Miller, Stable memory and computation in randomly rewiring neural networks. J. Neurophysiol. 122, 66–80 (2019).
51
M. E. Rule et al., Stable task information from an unstable neural population. eLife 9, e51121 (2020).
52
M. A. Triplett, L. Avitan, G. J. Goodhill, Emergence of spontaneous assembly activity in developing neural networks without afferent input. PLOS Comput. Biol. 14, e1006421 (2018).
53
N. Hiratani, T. Fukai, Interplay between short- and long-term plasticity in cell-assembly formation. PLoS One 9, e101535 (2014).
54
T. Pietri et al., The emergence of the spatial structure of tectal spontaneous activity is independent of visual inputs. Cell Rep. 19, 939–948 (2017).
55
S. Cheng, The CRISP theory of hippocampal function in episodic memory. Front. Neural Circuits 7, 88 (2013).
56
S. Royer, D. Paré, Conservation of total synaptic weight through balanced synaptic depression and potentiation. Nature 422, 518–522 (2003).
57
G. Turrigiano, Homeostatic synaptic plasticity: Local and global mechanisms for stabilizing neuronal function. Cold Spring Harb. Perspect. Biol. 4, a005736 (2012).
58
M. Letellier, F. Levet, O. Thoumine, Y. Goda, Differential role of pre- and postsynaptic neurons in the activity-dependent control of synaptic strengths across dendrites. PLoS Biol. 17, e2006223 (2019).
59
H. G. Rey et al., Single neuron coding of identity in the human hippocampal formation. Curr. Biol. 30, 1152–1159.e3 (2020).
60
V. D. Blondel, J. L. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, P10008 (2008).
61
R. LaPlante et al., bctpy v0.5.2: Brain connectivity toolbox for Python. https://github.com/aestrivex/bctpy. Accessed 29 October 2021.

Information & Authors

Information

Published in

Go to Proceedings of the National Academy of Sciences
Go to Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences
Vol. 118 | No. 46
November 16, 2021
PubMed: 34772802

Classifications

Data Availability

Code to reproduce the main results of this article have been deposited in GitHub at https://github.com/fkalle/drifting-assemblies.

Submission history

Accepted: September 11, 2021
Published online: November 12, 2021
Published in issue: November 16, 2021

Keywords

  1. associative memory
  2. cell assemblies
  3. neural representations
  4. representational drift
  5. synaptic remodeling

Acknowledgments

We thank Paul Züge for fruitful discussions; Abigail Morrison for comments on the manuscript; Hans Günter Memmesheimer, Katharina Hack, and Jonas Nietzsche for help with the graphical illustrations; and the German Federal Ministry of Education and Research for support via the Bernstein Network (Bernstein Award 2014, 01GQ1710).

Notes

This article is a PNAS Direct Submission.
Published under the PNAS license.

Authors

Affiliations

Yaroslav Felipe Kalle Kossio https://orcid.org/0000-0003-0696-6079
Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, 53115 Bonn, Germany
Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, 53115 Bonn, Germany
Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, 53115 Bonn, Germany
Raoul-Martin Memmesheimer2 [email protected]
Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, 53115 Bonn, Germany

Notes

2
To whom correspondence may be addressed. Email: [email protected].
Author contributions: Y.F.K.K., S.G., C.K., and R.-M.M. designed research, performed research, analyzed data, and wrote the paper.
1
S.G. and C.K. contributed equally to this work.

Competing Interests

The authors declare no competing interest.

Metrics & Citations

Metrics

Note: The article usage is presented with a three- to four-day delay and will update daily once available. Due to ths delay, usage data will not appear immediately following publication. Citation information is sourced from Crossref Cited-by service.


Citation statements

Altmetrics

Citations

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

    Loading...

    View Options

    View options

    PDF format

    Download this article as a PDF file

    DOWNLOAD PDF

    Get Access

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Personal login Institutional Login

    Recommend to a librarian

    Recommend PNAS to a Librarian

    Purchase options

    Purchase this article to get full access to it.

    Single Article Purchase

    Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensation
    Proceedings of the National Academy of Sciences
    • Vol. 118
    • No. 46

    Media

    Figures

    Tables

    Other

    Share

    Share

    Share article link

    Share on social media