Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning

Gillett, Maxwell; Pereira, Ulises; Brunel, Nicolas

doi:10.1073/pnas.1918674117

Cited by 36 publications

(77 citation statements)

References 75 publications

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“…A sequence imprinted in recurrent synaptic weights can be replayed during rest or sleep (27, 59–62), which was also observed in network-simulation studies (25, 26, 63). Replay could thus be a possible readout of the temporal-order learning mechanism.…”

Section: Discussionmentioning

confidence: 54%

Synaptic learning rules for sequence learning

Reifenstein

Kempter

2020

Preprint

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Remembering the temporal order of a sequence of events is a task easily performed by humans in everyday life, but the underlying neuronal mechanisms are unclear. This problem is particularly intriguing as human behavior often proceeds on a time scale of seconds, which is in stark contrast to the much faster millisecond time-scale of neuronal processing in our brains. One long-held hypothesis in sequence learning suggests that a particular temporal fine-structure of neuronal activity-termed "phase precession"-enables the compression of slow behavioral sequences down to the fast time scale of the induction of synaptic plasticity. Using mathematical analysis and computer simulations, we find that phase precession can improve sequence learning tremendously and that the asymmetric part of the synaptic learning window is essential for temporal-order learning. To test these predictions, we suggest experiments that selectively alter phase precession or the learning window and evaluate memory of temporal order.

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Section: Discussionmentioning

confidence: 54%

Synaptic learning rules for sequence learning

Reifenstein

Kempter

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…A sequence imprinted in recurrent synaptic weights can be replayed during rest or sleep (27,(59)(60)(61)(62), which was also observed in network-simulation studies (25,26,63). Replay could thus be a possible readout of the temporal-order learning mechanism.…”

Section: Replay Of Sequences and Storage Of Multiple And Overlapping mentioning

confidence: 60%

“…As a result, when the first event is encountered and/or the first neuron is activated, the neuron representing the second event is activated. Consequently, the behavioral sequence could be replayed (as illustrated by simulations e.g., in 14,[20][21][22][23][24][25][26] and the memory of the temporal order of events is recalled (27,28). We note, however, that in what follows we do not simulate such a replay of sequences, which would depend also on a vast number of parameters that define the network; instead, we rather focus on the underlying change in connectivity, which is the very basis of replay, and draw connections to "replay" in the Discussion.…”

Section: Resultsmentioning

confidence: 99%

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Synaptic learning rules for sequence learning

Reifenstein

Khalid

Kempter

2021

eLife

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Remembering the temporal order of a sequence of events is a task easily performed by humans in everyday life, but the underlying neuronal mechanisms are unclear. This problem is particularly intriguing as human behavior often proceeds on a time scale of seconds, which is in stark contrast to the much faster millisecond time-scale of neuronal processing in our brains. One long-held hypothesis in sequence learning suggests that a particular temporal fine-structure of neuronal activity - termed 'phase precession' - enables the compression of slow behavioral sequences down to the fast time scale of the induction of synaptic plasticity. Using mathematical analysis and computer simulations, we find that - for short enough synaptic learning windows - phase precession can improve temporal-order learning tremendously and that the asymmetric part of the synaptic learning window is essential for temporal-order learning. To test these predictions, we suggest experiments that selectively alter phase precession or the learning window and evaluate memory of temporal order.

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“…Therefore, applying temporally asymmetric Hebbian learning for SRNN to model neural sequences represents another important future research direction [46,47]…”

Section: Discussionmentioning

confidence: 99%

Spiking recurrent neural networks represent task-relevant neural sequences in rule-dependent computation

Xue

Halassa

Chen

2021

Preprint

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Prefrontal cortical neurons play in important roles in performing rule-dependent tasks and working memory-based decision making. Motivated by experimental data, we develop an excitatory-inhibitory spiking recurrent neural network (SRNN) to perform a rule-dependent two-alternative forced choice (2AFC) task. We imposed several important biological constraints onto the SRNN, and adapted the spike frequency adaptation (SFA) and SuperSpike gradient methods to update the network parameters. These proposed strategies enabled us to train the SRNN efficiently and overcome the vanishing gradient problem during error back propagation through time. The trained SRNN produced rule-specific tuning in single-unit representations, showing rule-dependent population dynamics that strongly resemble experimentally observed data in rodent and monkey. Under varying test conditions, we further manipulated the parameters or configuration in computer simulation setups and investigated the impacts of rule-coding error, delay duration, weight connectivity and sparsity, and excitation/inhibition (E/I) balance on both task performance and neural representations. Overall, our modeling study provides a computational framework to understand neuronal representations at a fine timescale during working memory and cognitive control.Author SummaryWorking memory and decision making are fundamental cognitive functions of the brain, but the circuit mechanisms of these brain functions remain incompletely understood. Neuroscientists have trained animals (rodents or monkeys) to perform various cognitive tasks while simultaneously recording the neural activity from specific neural circuits. To complement the experimental investigations, computational modeling may provide an alternative way to examine the neural representations of neuronal assemblies during task behaviors. Here we develop and train a spiking recurrent neural network (SRNN) consisting of balanced excitatory and inhibitory neurons to perform the rule-dependent working memory tasks Our computer simulations produce qualitatively similar results as the experimental findings. Moreover, the imposed biological constraints on the trained network provide additional channel to investigate cell type-specific population responses, cortical connectivity and robustness. Our work provides a computational platform to investigate neural representations and dynamics of cortical circuits a fine timescale during complex cognitive tasks.

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Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning

Cited by 36 publications

References 75 publications

Synaptic learning rules for sequence learning

Synaptic learning rules for sequence learning

Synaptic learning rules for sequence learning

Spiking recurrent neural networks represent task-relevant neural sequences in rule-dependent computation

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