A Computational Model of Working Memory Based on Spike-Timing-Dependent Plasticity

Huang, Qiu-Sheng; Wei, Hui

doi:10.3389/fncom.2021.630999

Cited by 8 publications

(3 citation statements)

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“…Hebbian changes in synaptic strengths can form memory associations in the brain that are transient and, importantly, can be overwritten in a use-dependent manner ( 21 ). Accordingly, a number of computational models of WM implement Hebbian synaptic plasticity to simulate a range of cognitive memory effects ( 22 – 25 ). To date, little direct neural evidence has supported these rapidly associative neural codes, partly because methods have not yet been developed to detect and model them.…”

mentioning

confidence: 99%

Neural signature of flexible coding in prefrontal cortex

Bocincova

Buschman

Stokes

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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The ability of prefrontal cortex to quickly encode novel associations is crucial for adaptive behavior and central to working memory. Fast Hebbian changes in synaptic strength permit forming new associations, but neuronal signatures of this have been elusive. We devised a trialwise index of pattern similarity to look for rapid changes in population codes. Based on a computational model of working memory, we hypothesized that synaptic strength—and consequently, the tuning of neurons—could change if features of a subsequent stimulus need to be “reassociated,” i.e., if bindings between features need to be broken to encode the new item. As a result, identical stimuli might elicit different neural responses. As predicted, neural response similarity dropped following rebinding, but only in prefrontal cortex. The history-dependent changes were expressed on top of traditional, fixed selectivity and were not explainable by carryover of previous firing into the current trial or by neural adaptation.

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mentioning

confidence: 99%

Neural signature of flexible coding in prefrontal cortex

Bocincova

Buschman

Stokes

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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“…Moreover, this is an important property when the network needs to learn an entirely new input or when the network needs to recover from a partial lesion. Computational models with synaptic plasticity avoid this problem either by all-to-all connectivity (Rolls et al, 2013;Huang and Wei, 2021)-which is computationally demanding on the one hand and not comparable to the brain where connectivity is only sparse on the other hand-or they require the creation of a fixed number of synapses between random neurons (Szatmáry and Izhikevich, 2010;Savin and Triesch, 2014;Fiebig and Lansner, 2017) at the beginning, limiting the ability to form memories between neuron pairs drastically. Our approach overcomes this limitation, allowing the forming of connections between all neurons and influencing the range over which neurons connect by changing our Gaussian probability parameter σ .…”

Section: Comparison Between Homeostatic Engram Formation and Synaptic...mentioning

confidence: 99%

Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism

Kaster,

Czappa,

Butz-Ostendorf

et al. 2024

Front. Neuroinform.

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Memory formation is usually associated with Hebbian learning and synaptic plasticity, which changes the synaptic strengths but omits structural changes. A recent study suggests that structural plasticity can also lead to silent memory engrams, reproducing a conditioned learning paradigm with neuron ensembles. However, this study is limited by its way of synapse formation, enabling the formation of only one memory engram. Overcoming this, our model allows the formation of many engrams simultaneously while retaining high neurophysiological accuracy, e.g., as found in cortical columns. We achieve this by substituting the random synapse formation with the Model of Structural Plasticity. As a homeostatic model, neurons regulate their activity by growing and pruning synaptic elements based on their current activity. Utilizing synapse formation based on the Euclidean distance between the neurons with a scalable algorithm allows us to easily simulate 4 million neurons with 343 memory engrams. These engrams do not interfere with one another by default, yet we can change the simulation parameters to form long-reaching associations. Our model's analysis shows that homeostatic engram formation requires a certain spatiotemporal order of events. It predicts that synaptic pruning precedes and enables synaptic engram formation and that it does not occur as a mere compensatory response to enduring synapse potentiation as in Hebbian plasticity with synaptic scaling. Our model paves the way for simulations addressing further inquiries, ranging from memory chains and hierarchies to complex memory systems comprising areas with different learning mechanisms.

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“…Moreover, this is an important property when the network needs to learn an entirely new input or when the network needs to recover from a partial lesion. Computational models with synaptic plasticity avoid this problem either by all-to-all connectivity (Rolls et al, 2013, Huang and Wei, 2021)—which is computationally demanding on the one hand and not comparable to the brain where connectivity is only sparse on the other hand— or they require the creation of a fixed number of synapses between random neurons (Fiebig and Lansner, 2017, Szatmáary and Izhikevich, 2010, Savin and Triesch, 2014) at the beginning, limiting the ability to form memories between neuron pairs drastically. Our approach overcomes this limitation, allowing the forming of connections between all neurons and influencing the range over which neurons connect by changing our Gaussian probability parameter σ .…”

Section: Discussionmentioning

confidence: 99%

Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism

Kaster,

Czappa,

Butz-Ostendorf

et al. 2023

Preprint

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Memory formation is usually associated with Hebbian learning, using synaptic plasticity to change the synaptic strengths but omitting structural changes. Recent work suggests that structural plasticity can also lead to silent memory engrams, reproducing a conditioned learning paradigm with neuron ensembles. However, this work is limited by its way of synapse formation, enabling the formation of only one memory engram. Overcoming this, our model allows the formation of many engrams simultaneously while retaining high neurophysiological accuracy, e.g., as found in cortical columns. We achieve this by substituting the random synapse formation with the Model of Structural Plasticity. As a homeostatic model, neurons regulate their own activity by growing and pruning of synaptic elements based on their current activity. Utilizing synapse formation with respect to the Euclidean distance between the neurons with a scalable algorithm allows us to easily simulate 4 million neurons with 343 memory engrams. These engrams do not interfere with one another by default, yet we can change the simulation parameters to form long-reaching associations. Our model paves the way for simulations addressing further inquiries, ranging from memory chains and hierarchies to complex memory systems comprising areas with different learning mechanisms.Author summaryMemory is usually explained by the strengthening or weakening of synapses that fire closely after each other. This limits the forming of memory to already connected neurons. Recent work showed that memory engrams can be formed with structural plasticity on a homeostatic base. In this model, the synapses of neurons have a fixed weight, and the model forms new synapses to have sufficient synaptic input to maintain a steady fire rate. However, this model connects neurons uniformly at random, limiting the parallel formation of memories. We extend this work by using a more biologically accurate model that connects neurons in a distant-dependent manner enabling us the parallel formation of memories. The scalability of our model enables us to easily simulate 4 million neurons with 343 memories in parallel without any unwanted inference. Our model increases biological accuracy by modeling brain-like structures such as cortical columns and paving the way for large-scale experiments with memory.

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A Computational Model of Working Memory Based on Spike-Timing-Dependent Plasticity

Cited by 8 publications

References 50 publications

Neural signature of flexible coding in prefrontal cortex

Neural signature of flexible coding in prefrontal cortex

Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism

Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism

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