Neuroscience
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In this paper, we discuss the nonlinear computational power provided by dendrites in biological and artificial neurons. We start by briefly presenting biological evidence about the type of dendritic nonlinearities, respective plasticity rules and their effect on biological learning as assessed by computational models. ⋯ The works are categorized according to the three primary methods of plasticity used-structural plasticity, weight plasticity, or plasticity of synaptic delays. Finally, we show the recent trend of confluence between concepts of deep learning and dendritic computations and highlight some future research directions.
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Much of our understanding of dendritic and synaptic physiology comes from in vitro experimentation, where the afforded mechanical stability and convenience of applying drugs allowed patch-clamping based recording techniques to investigate ion channel distributions, their gating kinetics, and to uncover dendritic integrative and synaptic plasticity rules. However, with current efforts to study these questions in vivo, there is a great need to translate existing knowledge between in vitro and in vivo experimental conditions. ⋯ Here, we argue that under physiological in vivo ionic conditions, dendrites are expected to be more excitable and the threshold for synaptic plasticity induction to be lowered. Consequently, the plasticity rules described in vitro vary significantly from those implemented in vivo.
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Computations on the dendritic trees of neurons have important constraints. Voltage dependent conductances in dendrites are not similar to arbitrary direct-current generation, they are the basis for dendritic nonlinearities and they do not allow converting positive currents into negative currents. ⋯ We find that dendritic model performance on interesting machine learning tasks is not hurt by these constraints but may benefit from them. Our results suggest that single real dendritic trees may be able to learn a surprisingly broad range of tasks.
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Review
Cholinergic Modulation of Dendritic Signaling in Hippocampal GABAergic Inhibitory Interneurons.
Dendrites represent the "reception hub" of the neuron as they collect thousands of different inputs and send a coherent response to the cell body. A considerable portion of these signals, especially in vivo, arises from neuromodulatory sources, which affect dendritic computations and cellular activity. In this context, acetylcholine (ACh) exerts a coordinating role of different brain structures, contributing to goal-driven behaviors and sleep-wake cycles. ⋯ We consider the distribution of cholinergic receptors on these interneurons, including information about their specific somatodendritic location, and discuss how the action of these receptors can modulate dendritic Ca2+ signaling and activity of interneurons. The implications of ACh-dependent Ca2+ signaling for dendritic plasticity are also discussed. We propose that cholinergic modulation can shape the dendritic integration and plasticity in interneurons in a cell type-specific manner, and the elucidation of these mechanisms will be required to understand the contribution of each cell type to large-scale network activity.
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Decades of experimental and theoretical work support a now well-established theory that active dendritic processing contributes to the computational power of individual neurons. This theory is based on the high degree of electrical compartmentalization observed in the dendrites of single neurons in ex vivo preparations. ⋯ In this review, we contextualize these new findings and discuss their impact on the future of the field. Specifically, we consider how highly coordinated, and thus less compartmentalized, activity in soma and dendrites can contribute to cortical computations including nonlinear mixed selectivity, prediction/expectation, multiplexing, and credit assignment.