Homeostatic control of synaptic rewiring in recurrent networks induces the formation of stable memory engrams
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 c...
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description | 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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W.</contributor><creatorcontrib>Gallinaro, Júlia V ; Gašparović, Nebojša ; Rotter, Stefan ; van Rossum, Mark C. W.</creatorcontrib><description>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. 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This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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W.</contributor><creatorcontrib>Gallinaro, Júlia V</creatorcontrib><creatorcontrib>Gašparović, Nebojša</creatorcontrib><creatorcontrib>Rotter, Stefan</creatorcontrib><title>Homeostatic control of synaptic rewiring in recurrent networks induces the formation of stable memory engrams</title><title>PLoS computational biology</title><addtitle>PLoS Comput Biol</addtitle><description>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.</description><subject>Biology and Life Sciences</subject><subject>Brain</subject><subject>Computer and Information Sciences</subject><subject>Connectivity</subject><subject>Diffusion rate</subject><subject>Firing pattern</subject><subject>Firing rate</subject><subject>Functional plasticity</subject><subject>Hebbian plasticity</subject><subject>Homeostasis</subject><subject>Homeostasis - physiology</subject><subject>Homeostatic plasticity</subject><subject>Medicine and Health Sciences</subject><subject>Memory</subject><subject>Models, Neurological</subject><subject>Nerve Net - physiology</subject><subject>Neural circuitry</subject><subject>Neural networks</subject><subject>Neural plasticity</subject><subject>Neurological research</subject><subject>Neuronal Plasticity - physiology</subject><subject>Neurons</subject><subject>Neurons - physiology</subject><subject>Neuroplasticity</subject><subject>Physiological aspects</subject><subject>Plasticity</subject><subject>Rewiring</subject><subject>Stimulation</subject><subject>Structure-function relationships</subject><subject>Synapses</subject><subject>Synapses - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gallinaro, Júlia V</au><au>Gašparović, Nebojša</au><au>Rotter, Stefan</au><au>van Rossum, Mark C. W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Homeostatic control of synaptic rewiring in recurrent networks induces the formation of stable memory engrams</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2022-02-01</date><risdate>2022</risdate><volume>18</volume><issue>2</issue><spage>e1009836</spage><epage>e1009836</epage><pages>e1009836-e1009836</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>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. 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subjects | Biology and Life Sciences Brain Computer and Information Sciences Connectivity Diffusion rate Firing pattern Firing rate Functional plasticity Hebbian plasticity Homeostasis Homeostasis - physiology Homeostatic plasticity Medicine and Health Sciences Memory Models, Neurological Nerve Net - physiology Neural circuitry Neural networks Neural plasticity Neurological research Neuronal Plasticity - physiology Neurons Neurons - physiology Neuroplasticity Physiological aspects Plasticity Rewiring Stimulation Structure-function relationships Synapses Synapses - physiology Synaptic plasticity |
title | Homeostatic control of synaptic rewiring in recurrent networks induces the formation of stable memory engrams |
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