Synaptic Transmission Optimization Predicts Expression Loci of Long-Term Plasticity

Costa, Rui Ponte; Padamsey, Zahid; D'Amour, James A; Emptage, Nigel J.; Froemke, Robert C.; Vogels, Tim P.

doi:10.1016/j.neuron.2017.09.021

Cited by 41 publications

(60 citation statements)

References 89 publications

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“…We assumed that pre-and postsynaptic changes would match each other over time and ignored the dynamics of this process. This choice was motivated in part by the lack of experimental constraints and in part by the results of recent theoretical studies (Costa et al, 2017). We obtained only moderated levels of LTD compared to in vitro experiments.…”

Section: Discussionmentioning

confidence: 99%

“…However, more recent studies found also a prominent postsynaptic component (Sjöström et al, 2007), mostly based on alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionate receptor (AM-PAR) plasticity, as commonly observed in the hippocampus. Some recent studies (Costa et al, 2017) have investigated the theoretical implications of bi-lateral plasticity and concluded that synapses strive for extreme states (highconductance high-release probability, low-conductance lowrelease probability). LTP/LTD are by definition persistent changes, at least in the order of minutes to hours, but presumably longer.…”

Section: Methodsmentioning

confidence: 99%

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A calcium-based plasticity model predicts long-term potentiation and depression in the neocortex

Chindemi

Abdellah

Amsalem

et al. 2020

Preprint

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Long-term potentiation (LTP) and long-term depression (LTD) of pyramidal cell connections are among the key mechanisms underlying learning and memory in the brain. Despite their important role, only a few of these connections have been characterized in terms of LTP/LTD dynamics, such as the one between layer 5 thick-tufted pyramidal cells (L5-TTPCs).Comparing the available evidence on different pyramidal connection types reveals a large variability of experimental outcomes, possibly indicating the presence of connection-type-specific mechanisms. Here, we show that a calcium-based plasticity rule regulating L5-TTPC synapses holds also for several other pyramidalto-pyramidal connections in a digital model of neocortical tissue. In particular, we show that synaptic physiology, cell morphology and innervation patterns jointly determine LTP/LTD dynamics without requiring a different model or parameter set for each connection type. We therefore propose that a similar set of plasticity mechanisms is shared by seemingly very different neocortical connections and that only a small number of targeted experiments is required for generating a complete map of synaptic plasticity dynamics in the neocortex. computational neuroscience | synaptic plasticity |

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A calcium-based plasticity model predicts long-term potentiation and depression in the neocortex

Chindemi

Abdellah

Amsalem

et al. 2020

Preprint

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“…section 4), data itself may guide the choice of metric. Surprisingly, the available and appropriately normalized experimental data is consistent with the Euclidean metric in P rel − q space [19], but there is probably not sufficient data to discard a metric based on metabolic cost. Gradient descent has been popular as an approach to postulate synaptic plasticity rules [7][8][9][11][12][13][14][15][16][17].…”

Section: Gradient Descent In Neurosciencementioning

confidence: 98%

“…In some cases, cost as performance of a biological system is measured in comparison to the absolute physical minimum [5] or an information theoretic optimum [1][2][3] without addressing the question of how a solution at or close to the minimum can be found. In other cases, cost is used to derive algorithms that move the system closer to the minimum [6][7][8][9][10][11][12][13][14][15][16][17][18][19]. In the second case, predictions entail update rules of neuronal quantities or differential equations for the time evolution of synaptic weights.…”

Section: Introductionmentioning

confidence: 99%

On the choice of metric in gradient-based theories of brain function

et al. 2020

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The idea that the brain functions so as to minimize certain costs pervades theoretical neuroscience. Since a cost function by itself does not predict how the brain finds its minima, additional assumptions about the optimization method need to be made to predict the dynamics of physiological quantities. In this context, steepest descent (also called gradient descent) is often suggested as an algorithmic principle of optimization potentially implemented by the brain. In practice, researchers often consider the vector of partial derivatives as the gradient. However, the definition of the gradient and the notion of a steepest direction depend on the choice of a metric. Since the choice of the metric involves a large number of degrees of freedom, the predictive power of models that are based on gradient descent must be called into question, unless there are strong constraints on the choice of the metric. Here we provide a didactic review of the mathematics of gradient descent, illustrate common pitfalls of using gradient descent as a principle of brain function with examples from the literature and propose ways forward to constrain the metric.

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“…Previous modeling approaches of STP have used a system of 84 nonlinear ordinary differential equations to capture µ(t) separated in a number of 85 dynamic factors [4,11,23,24]. Our main result is that we propose a linear-nonlinear 86 approach inspired from the engineering of systems identification [33,[40][41][42][43][44][45][46][47] and the 87 Spike Response Model (SRM) for cellular dynamics [34,48,49]. Here, the efficacies are 88 modeled as a nonlinear readout, f , of a linear filtering operation:…”

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confidence: 99%

Linear-Nonlinear Cascades Capture Synaptic Dynamics

Rossbroich

Trotter

Beninger

et al. 2020

Preprint

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Synaptic dynamics differ markedly across connections and strongly regulate how action potentials are being communicated. To model the range of synaptic dynamics observed in experiments, we develop a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks. Author summaryUnderstanding how information is transmitted relies heavily on knowledge of the underlying regulatory synaptic dynamics. Existing computational models for capturing such dynamics are often either very complex or too restrictive. As a result, effectively capturing the different types of dynamics observed experimentally remains a challenging problem. Here, we propose a mathematically flexible linear-nonlinear model that is capable of efficiently characterizing synaptic dynamics. We demonstrate the ability of this model to capture different features of experimentally observed data.The nervous system has evolved a communication system largely based on temporal 2 sequences of action potentials. A central feature of this communication is that action 3 potentials are communicated with variable efficacy on short (10 ms -10 s) time 4 scales [1-6]. The dynamics of synaptic efficacy at short time scales, or short-term 5 plasticity (STP), can be a powerful determinant of the flow of information, allowing the 6 same axon to communicate independent messages to different post-synaptic 7 targets [7, 8]. Properties of STP vary markedly across projections [9-11], leading to the 8idea that connections can be conceived as belonging to distinct classes [12,13] and that 9 these distinct classes shape information transmission in vivo [14][15][16]. Thus, to 10 May 29, 2020 1/25 understand the flow of information in neuronal networks, the connectome must be 11 indexed with an accurate description of STP properties. 12One approach to characterizing synaptic dynamics is to perform targeted 13 experiments and extract a summary feature, most commonly the paired-pulse 14 ratio [5,[17][18][19], whereby a synapse can be classified as short-term depressing (STD) or 15 short-term facilitating (STF). However, a single summary feature is insufficient to 16 capture the full extent of STP diversity. Longer or more complex stimulation patterns 17 are required to describe delayed facilitation onset [6], biphasic STP [20, 21] or the 18 distinction between supra-and sub-linear facilitation [22]. Such atypical STP dynamics 19 challenge the traditional dichotomy of STF and STD and suggest that more complex 20 phenotypes can exist and contribute to network function in...

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Synaptic Transmission Optimization Predicts Expression Loci of Long-Term Plasticity

Cited by 41 publications

References 89 publications

A calcium-based plasticity model predicts long-term potentiation and depression in the neocortex

A calcium-based plasticity model predicts long-term potentiation and depression in the neocortex

On the choice of metric in gradient-based theories of brain function

Linear-Nonlinear Cascades Capture Synaptic Dynamics

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