The Guide to Dendritic Spikes of the Mammalian Cortex In Vitro and In Vivo

Larkum, Matthew E.; Wu, Jiameng; Duverdin, Sarah A; Gidon, Albert

doi:10.1016/j.neuroscience.2022.02.009

Cited by 44 publications

(47 citation statements)

References 256 publications

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“…DLR is consistent with the experimental data of [Gambino et al, 2014, Stein et al, 2021] insofar as synaptic plasticity only occurs in the presence of an NMDA spike, i.e., y = 1. Furthermore, consistent with the results of [Stein et al, 2021], the decision between LTP and LTD depends on the presence of a signal that causes Ca 2+ -influx in temporal proximity, which can be caused in L2/3 pyramids by a backpropagating action potential, and in L5 pyramids by a Ca 2+ -spike that is triggered when significant depolarization in the apical tuft coincides with a somatic spike [Larkum, 2013, Larkum et al, 2022]. In addition, DLR predicts that the amplitude of LTP is scaled by the term q w ( x ) = σ ( β ( w · x – u 0 )), i.e., by the current excitability of the dendrite for NMDA spikes, reducing the amplitude of LTP if this excitability is already fairly high.…”

Section: Resultssupporting

confidence: 79%

“…There exists a staggering diversity of subtypes of pyramidal cells, especially in the human cortex [Berg et al, 2021]. In addition there exists substantial diversity among subtypes of NMDA receptors [Stein et al, 2021] and of NMDA spikes [Stuyt et al, 2021, Larkum et al, 2022]. Hence it would be quite optimistic to expect that all resulting plasticity processes in apical dendrites of pyramidal cells can be described by a simple formula, such as DLR.…”

Section: Discussionmentioning

confidence: 99%

“…It integrates two different inputs streams, a top-down stream that arrives in its apical dendrites (shown at the top), and a bottom-up stream that arrives in basal dendrites and influences the generation of somatic spikes. see [Larkum, 2013, Augusto and Gambino, 2019, Larkum et al, 2022]. Synaptic inputs from neurons in higher brain areas with firing rates x 1 , …, x N are integrated with synaptic weights w 1 , …, w N in the apical tuft.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental data clarify where topdown and bottom-up information meet in generic cortical microcircuits: in pyramidal cells on layers 2/3 and 5. More precisely, top-down inputs reach these cells primarily through synapses on their apical dendrites in superficial layers, especially in layer 1 [Larkum, 2022], whereas bottom-up information primarily targets basal dendrites. Hence we need to understand the plasticity processes which shape the impact and information content of this top-down synaptic input.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, local NMDA spikes in the apical tuft of pyramidal cells play an essential role for this plasticity, see e.g. [Augusto and Gambino, 2019, Stuyt et al, 2021, Larkum, 2022 for recent reviews. Related to the important role that NMDA spikes, which typically last 100ms and longer, play for synaptic plasticity in apical dendrites, unlike STDP for basal dendrites the precise temporal relation between these NMDA spikes and other salient events, appears to be less relevant for inducing synaptic plasticity.…”

Section: Introductionmentioning

confidence: 99%

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Self-supervised learning of probabilistic prediction through synaptic plasticity in apical dendrites: A normative model

Rao

Legenstein

Subramoney

et al. 2021

Preprint

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Predictive coding has been identified as a major driver of computation and learning in corticalmicrocircuits. But it has remained unknown which synaptic plasticity processes install and maintain predictive coding capability. Predictions are inherently uncertain, and learning rules that aim at discriminating linearly separable classes of inputs - such as the perceptron learning rule - are not suitable for learning to predict. We show that experimental data on synaptic plasticity in distal dendrites of pyramidal cells support a functionally more powerful learning rule that produces a spike-based approximation to logistic regression, i.e., to probabilistic prediction. We also show that experimentally found interactions between different dendritic branches support learning of predictions for more complex probability distributions. The resulting learning theory for top-down inputs to pyramidal cells provides a normative framework for evaluating experimental data, and suggests further experiments for tracking the emergence of predictive coding through synaptic plasticity in distal dendrites.

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