A Synaptic Framework for the Persistence of Memory Engrams

Rao-Ruiz, Priyanka; Visser, Esther; Mitrić, Miodrag; Smit, August B.; Oever, Michel C. van den

doi:10.3389/fnsyn.2021.661476

Cited by 35 publications

(34 citation statements)

References 144 publications

(195 reference statements)

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“…High turnover rates of synapses increase the volatility of network structure. This, in turn, poses a grand challenge to any synaptic theory of memory [ 11 ], and it is not yet clear how memories can at all persist in a system that is constantly rewiring [ 49 ]. In our model, the desired relative stability of memories is achieved by storing them with the help of a slow manifold mechanism.…”

Section: Discussionmentioning

confidence: 99%

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

2022

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Brain networks store new memories using functional and structural synaptic plasticity. Memory formation is generally attributed to Hebbian plasticity, while homeostatic plasticity is thought to have an ancillary role in stabilizing network dynamics. Here we report that homeostatic plasticity alone can also lead to the formation of stable memories. We analyze this phenomenon using a new theory of network remodeling, combined with numerical simulations of recurrent spiking neural networks that exhibit structural plasticity based on firing rate homeostasis. These networks are able to store repeatedly presented patterns and recall them upon the presentation of incomplete cues. Storage is fast, governed by the homeostatic drift. In contrast, forgetting is slow, driven by a diffusion process. Joint stimulation of neurons induces the growth of associative connections between them, leading to the formation of memory engrams. These memories are stored in a distributed fashion throughout connectivity matrix, and individual synaptic connections have only a small influence. Although memory-specific connections are increased in number, the total number of inputs and outputs of neurons undergo only small changes during stimulation. We find that homeostatic structural plasticity induces a specific type of “silent memories”, different from conventional attractor states.

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Section: Discussionmentioning

confidence: 99%

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

2022

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“… 241 Extrapolated to the scale of entire circuits, this theory provided the basis for the idea that learning leads to the strengthening of synaptic transmission between groups of neurons that have all been activated by specific patterns of incoming activity. 242 Such groups of neurons could be easily reactivated if the learned pattern or stimulus is presented again after learning, possibly speeding up the neural coding of stimulus properties. These co-activated groups of neurons are thought to participate in the formation of what is sometimes defined as an “engram,” a signature circuit for a memory 243 (see also Sec.…”

Section: Metastability Learning and Neural Network Functionmentioning

confidence: 99%

Metastable dynamics of neural circuits and networks

Brinkman

Yan

Maffei

et al. 2022

Applied Physics Reviews

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Cortical neurons emit seemingly erratic trains of action potentials or “spikes,” and neural network dynamics emerge from the coordinated spiking activity within neural circuits. These rich dynamics manifest themselves in a variety of patterns, which emerge spontaneously or in response to incoming activity produced by sensory inputs. In this Review, we focus on neural dynamics that is best understood as a sequence of repeated activations of a number of discrete hidden states. These transiently occupied states are termed “metastable” and have been linked to important sensory and cognitive functions. In the rodent gustatory cortex, for instance, metastable dynamics have been associated with stimulus coding, with states of expectation, and with decision making. In frontal, parietal, and motor areas of macaques, metastable activity has been related to behavioral performance, choice behavior, task difficulty, and attention. In this article, we review the experimental evidence for neural metastable dynamics together with theoretical approaches to the study of metastable activity in neural circuits. These approaches include (i) a theoretical framework based on non-equilibrium statistical physics for network dynamics; (ii) statistical approaches to extract information about metastable states from a variety of neural signals; and (iii) recent neural network approaches, informed by experimental results, to model the emergence of metastable dynamics. By discussing these topics, we aim to provide a cohesive view of how transitions between different states of activity may provide the neural underpinnings for essential functions such as perception, memory, expectation, or decision making, and more generally, how the study of metastable neural activity may advance our understanding of neural circuit function in health and disease.

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“…High turnover rates of synapses increase the volatility of network structure. This, in turn, poses a grand challenge to any synaptic theory of memory [42], and it is not yet clear how memories can at all persist in a system that is constantly rewiring [48]. In our model, the desired relative stability of memories is achieved by storing them with the help of a slow manifold mechanism.…”

Section: Discussionmentioning

confidence: 99%

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

Gallinaro

Gasparovic

Rotter

2020

Preprint

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Brain networks store new memories using functional and structural synaptic plasticity. Memory formation is generally attributed to Hebbian plasticity, while homeostatic plasticity is thought to have an ancillary role in stabilizing network dynamics. Here we report that homeostatic plasticity alone can also lead to the formation of stable memories. We analyze this phenomenon using a new theory of network remodeling, combined with numerical simulations of recurrent spiking neural networks that exhibit structural plasticity based on firing rate homeostasis. These networks are able to store repeatedly presented patterns and recall them upon the presentation of incomplete cues. Storing is fast, governed by the homeostatic drift. In contrast, forgetting is slow, driven by a diffusion process. Joint stimulation of neurons induces the growth of associative connections between them, leading to the formation of memory engrams. In conclusion, homeostatic structural plasticity induces a specific type of "silent memories", different from conventional attractor states.

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A Synaptic Framework for the Persistence of Memory Engrams

Cited by 35 publications

References 144 publications

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

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

Metastable dynamics of neural circuits and networks

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

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