Dendritic Learning as a Paradigm Shift in Brain Learning

Sardi, Shira; Vardi, Roni; Goldental, Amir; Tugendhaft, Yael; Uzan, Herut; Kanter, Ido

doi:10.1021/acschemneuro.8b00204

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…This concept of spike-timing dependent plasticity (STDP) means that the learning scheme is global, strengthening the synapses that the backpropagating signal can reach. However, Sardi et al [1] proposed a paradigm shift in learning, that is, by dendritic learning instead of the slow synaptic plasticity. Recent experimental studies in CA3 pyramidal neurons prove that a single dendritic branch induces branch-constrained synaptic plasticity through NMDA spikes and Ca 2+ transients [72] .…”

Section: Discussionmentioning

confidence: 99%

“…During simultaneous synaptic activation, the driving force of an input signal combines with the driving forces of its neighboring synaptic inputs. The contribution of a signal depends on several factors: its distance from the points of entry of other inputs, the diameter and length of its dendritic compartment, the morphology of the dendrites, the distribution of active channels, and the somatic dynamics, as well [1] – [5] . If the sum of the driving forces reaching the somatic compartment is above the spiking threshold, an action potential (AP) is generated.…”

Section: Introductionmentioning

confidence: 99%

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A multilayer-multiplexer network processing scheme based on the dendritic integration in a single neuron

Lorenzo¹,

Binczak²,

Jacquir³

2022

AIMSN

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<abstract> <p>Advances in neuronal studies suggest that a single neuron can perform integration functions previously associated only with neuronal networks. Here, we proposed a dendritic abstraction employing a dynamic thresholding function that models the spatiotemporal dendritic integration process of a CA3 pyramidal neuron. First, we developed an input-output quantification process that considers the natural neuronal response and the full range of dendritic dynamics. We analyzed the IO curves and demonstrated that dendritic integration is branch-specific and dynamic rather than the commonly employed static nonlinearity. Second, we completed the integration model by creating a dendritic abstraction incorporating the spatiotemporal characteristics of the dendrites. Furthermore, we predicted the dendritic activity in each dendritic layer and the corresponding somatic firing activity by employing the dendritic abstraction in a multilayer-multiplexer information processing scheme comparable to a neuronal network. The subthreshold activity influences the suprathreshold regions via its dynamic threshold, a parameter that is dependent not only on the driving force but also on the number of activated synapses along the dendritic branch. An individual dendritic branch performs multiple integration modes by shifting from supralinear to linear then to sublinear. The abstraction includes synaptic input location-dependent voltage delay and decay, time-dependent linear summation, and dynamic thresholding function. The proposed dendritic abstraction can be used to create multilayer-multiplexer neurons that consider the spatiotemporal properties of the dendrites and with greater computational capacity than the conventional schemes.</p> </abstract>

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multilayer-multiplexer network processing scheme based on the dendritic integration in a single neuron

Lorenzo¹,

Binczak²,

Jacquir³

2022

AIMSN

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“…Dendritic spines change shape as they mature, progressing from thin, to stubby to mushroom [95][96][97]. These changes in dendritic spine morphology correspond to their function of the brain and influence plasticity of the neuron, and ultimately the brain as a whole [98,99]. We have previously shown that COX-2 --KI male mice were less likely and COX-2 --KI females were more likely to have mature spines compare to the Saline control counterparts [39,56].…”

Section: Dendritic Spine Morphologymentioning

confidence: 99%

Maternal exposure to prostaglandin e2 results in abnormal dendritic morphology in the cerebellum and related motor behaviour in mouse offspring

Kissoondoyal,

Ho,

Wong

et al. 2023

Preprint

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The lipid signalling molecule prostaglandin E2 (PGE2) is important in healthy brain development. Abnormal PGE2 levels during prenatal development, which can be influenced by genetic causes and exposure to various environmental risk factors, have been linked to increased prevalence of Autism Spectrum Disorders (ASDs). Growing research in animal models aims to provide evidence for the mechanisms by which increased or reduced PGE2 levels influence brain development. In this study, we show that maternal exposure to PGE2 in mice at gestational day 11 (G11) results in molecular changes within the cerebellum and associated behaviours in offspring. We observed a decrease in cerebellar cell density originating at G11 (in males and females) and at G16 (in females only). In Golgi-COX-stained cerebellar slices from PGE2-exposed offspring at the postnatal day 30 (PN30), we found an increase in dendritic arborization, the odds of observing dendritic loops, dendritic spine density, and the odds of observing mature (mushroom-shaped) spines. We also observed a decrease in the expression level of the cytoskeletal protein beta-actin, the actin associated protein spinophilin, and the cell adhesion protein N-Cadherin. In addition, we found that specifically PGE2-exposed male offspring exhibited abnormal cerebellar related motor function. This study adds further evidence that changes in the PGE2 levels during critical times may impact the developing brain differently in males and females. These findings also emphasize the importance of examining sex differences in research relevant to neurodevelopmental disorders.

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“…This ontogenic process is functionally analogous to the evolution of structural and positional diversity of dendrites as they have adapted to a spectrum of functional roles [5], e.g. the implementation of deep learning via synaptic plasticity [6,7]. Neurons are not, therefore, static structures, but rather can be regarded as undergoing continuous development throughout the life cycle.…”

Section: Introductionmentioning

confidence: 99%

Neurons as hierarchies of quantum reference frames

Fields,

Glazebrook,

Levin

2022

Preprint

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Conceptual and mathematical models of neurons have lagged behind empirical understanding for decades. Here we extend previous work in modeling biological systems with fully scale-independent quantum information-theoretic tools to develop a uniform, scalable representation of synapses, dendritic and axonal processes, neurons, and local networks of neurons. In this representation, hierarchies of quantum reference frames act as hierarchical active-inference systems. The resulting model enables specific predictions of correlations between synaptic activity, dendritic remodeling, and trophic reward. We summarize how the model may be generalized to nonneural cells and tissues in developmental and regenerative contexts.

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Dendritic Learning as a Paradigm Shift in Brain Learning

Cited by 6 publications

References 4 publications

A multilayer-multiplexer network processing scheme based on the dendritic integration in a single neuron

A multilayer-multiplexer network processing scheme based on the dendritic integration in a single neuron

Maternal exposure to prostaglandin e2 results in abnormal dendritic morphology in the cerebellum and related motor behaviour in mouse offspring

Neurons as hierarchies of quantum reference frames

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