Nonspecific synaptic plasticity improves the recognition of sparse patterns degraded by local noise

Safaryan, Karen; Maex, Reinoud; Davey, Neil; Adams, Roderick; Steuber, Volker

doi:10.1038/srep46550

Cited by 5 publications

(4 citation statements)

References 70 publications

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“…The focus on excitatory synapses also enables our work to be directly compared to studies of excitatory perceptron-like learning done on Purkinje cells-which have been classically conceived of as perceptrons (Marr, 1969;Albus, 1971)-, such as the work of Brunel et al (2004), Steuber et al (2007), and Safaryan et al (2017). These studies demonstrated that detailed models of Purkinje cells can learn to discriminate between different patterns of input from the parallel fibers (PF) via a perceptronlike usage of long-term depression (LTD), which is known to occur in PF-Purkinje synapses.…”

Section: Relationship To Models Of Learning In Purkinje Cellsmentioning

confidence: 99%

Perceptron Learning and Classification in a Modeled Cortical Pyramidal Cell

Moldwin

Segev

2020

Front. Comput. Neurosci.

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The perceptron learning algorithm and its multiple-layer extension, the backpropagation algorithm, are the foundations of the present-day machine learning revolution. However, these algorithms utilize a highly simplified mathematical abstraction of a neuron; it is not clear to what extent real biophysical neurons with morphologically-extended non-linear dendritic trees and conductance-based synapses can realize perceptron-like learning. Here we implemented the perceptron learning algorithm in a realistic biophysical model of a layer 5 cortical pyramidal cell with a full complement of non-linear dendritic channels. We tested this biophysical perceptron (BP) on a classification task, where it needed to correctly binarily classify 100, 1,000, or 2,000 patterns, and a generalization task, where it was required to discriminate between two "noisy" patterns. We show that the BP performs these tasks with an accuracy comparable to that of the original perceptron, though the classification capacity of the apical tuft is somewhat limited. We concluded that cortical pyramidal neurons can act as powerful classification devices.

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Section: Relationship To Models Of Learning In Purkinje Cellsmentioning

confidence: 99%

Perceptron Learning and Classification in a Modeled Cortical Pyramidal Cell

Moldwin

Segev

2020

Front. Comput. Neurosci.

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“…This work was important, as it provided a biologically relevant association between LTD and synapse elimination. In addition, studies also began to show that LTD of a synapse could also provide a meaningful way to increase storage capacity in neurons (Safaryan et al 2017), and increase the signal to noise ratio between synapses, without synapse elimination.…”

mentioning

confidence: 99%

Endocannabinoid receptors contribute significantly to multiple forms of long-term depression in the rat dentate gyrus

et al. 2020

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Cannabinoid receptors are widely expressed throughout the hippocampal formation, but are particularly dense in the dentate gyrus (DG) subregion. We, and others, have shown in mice that cannabinoid type 1 receptors (CB1Rs) are involved in a long-term depression (LTD) that can be induced by prolonged 10 Hz stimulation of the medial perforant path (MPP)-granule cell synaptic input to the DG. Here, we extend this work to examine the involvement of CB1Rs in other common forms of LTD in the hippocampus of juvenile male and female Sprague–Dawley rats (Rattus norvegicus). We found, as in mice, that prolonged 10 Hz stimulation (6000 pulses) could reliably induce a form of LTD that was dependent upon CB1R activation. In addition, we also discovered a role for both CB1R and mGluR proteins in LTD induced with 1 Hz low-frequency stimulation (1 Hz-LTD; 900 pulses) and in LTD induced by bath application of the group I mGluR agonist (RS)-3,5-Dihydroxyphenylglycine (DHPG; DHPG-LTD). This study elucidates an essential role for endocannabinoid receptors in a number of forms of LTD in the rat DG, and identifies a novel role for CB1Rs as potential therapeutic targets for conditions that involve impaired LTD in the DG.

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“…However, metaplasticity was appointed as a role for NO only in a more recent study [Safaryan et al, 2017], where it was proposed as an appropriate candidate for implementing a type of plasticity that is non-specific and outperforms the standard plasticity model of pf -PC synapses. The model is based on the experimental evidence that plasticity requires the action of a diffusive and volatile second messenger able to induce plasticity in neighbouring non-active synapses, such as the NO.…”

Section: Modelling No Diffusive Plasticity In the Cerebellummentioning

confidence: 99%

Modeling Nitric Oxide Diffusion and Plasticity Modulation in Cerebellar Learning

Trapani,

Sartori,

Gambosi

et al. 2024

Preprint

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Nitric Oxide (NO) is a versatile signalling molecule with significant roles in various physiological processes, including synaptic plasticity and memory formation. In the cerebellum, NO is produced by neural NO Synthase and diffuses to influence synaptic changes, particularly at parallel fiber - Purkinje cell synapses. This study aims to investigate NO’s role in cerebellar learning mechanisms using a biologically realistic simulation-based approach. We developed the NO Diffusion Simulator (NODS), a Python module designed to model NO production and diffusion within a cerebellar spiking neural network framework. Our simulations focus on the Eye-Blink Classical Conditioning protocol to assess the impact of NO modulation on long-term potentiation and depression at parallel fiber - Purkinje cell synapses. The results demonstrate that NO diffusion significantly affects synaptic plasticity, dynamically adjusting learning rates based on synaptic activity patterns. This metaplasticity mechanism enhances the cerebellum’s capacity to prioritize relevant inputs and mitigate learning interference selectively modulating synaptic efficacy. Our findings align with theoretical models suggesting that NO serves as a contextual indicator, optimizing learning rates for effective motor control and adaptation to new tasks. The NODS implementation provides an efficient tool for large-scale simulations, facilitating future studies on NO dynamics in various brain regions and neurovascular coupling scenarios. By bridging the gap between molecular processes and network-level learning, this work underscores the critical role of NO in cerebellar function and offers a robust framework for exploring NO-dependent plasticity in computational neuroscience.

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Nonspecific synaptic plasticity improves the recognition of sparse patterns degraded by local noise

Cited by 5 publications

References 70 publications

Perceptron Learning and Classification in a Modeled Cortical Pyramidal Cell

Perceptron Learning and Classification in a Modeled Cortical Pyramidal Cell

Endocannabinoid receptors contribute significantly to multiple forms of long-term depression in the rat dentate gyrus

Modeling Nitric Oxide Diffusion and Plasticity Modulation in Cerebellar Learning

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