Dendritic computations captured by an effective point neuron model
| Autor: | David W. McLaughlin, Songting Li, Nan Liu, Xiaohui Zhang, Douglas Zhou, David Cai |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Current (mathematics)
Computer science dendritic computation Biological neuron model Voltage-Gated Sodium Channels Inhibitory postsynaptic potential Rats Sprague-Dawley synaptic integration medicine Animals synaptic current CA1 Region Hippocampal Neurons Multidisciplinary Quantitative Biology::Neurons and Cognition single-neuron dynamics Excitatory Postsynaptic Potentials Biological Sciences Rats Electrophysiology medicine.anatomical_structure nervous system PNAS Plus Potassium Channels Voltage-Gated Synapses Excitatory postsynaptic potential Soma Neural Networks Computer Neuron point neuron model Neuroscience Coincidence detection in neurobiology |
| Zdroj: | Proceedings of the National Academy of Sciences of the United States of America |
| ISSN: | 1091-6490 0027-8424 |
| DOI: | 10.1073/pnas.1904463116 |
| Popis: | Significance Modeling single-neuron dynamics is the first step to quantitatively understand brain computation. Yet, the existing point neuron models fail to capture dendritic effects, which are crucial for neuronal information processing. We derive an effective point neuron model, which incorporates an additional synaptic integration current arising from the nonlinear interaction between synaptic currents across spatial dendrites. Our model captures the somatic voltage response of a neuron with complex dendrites and is capable of performing rich dendritic computations. Besides its computational efficiency in simulations, our model suggests reexamination of previous studies involving the decomposition of excitatory and inhibitory synaptic inputs based on the existing point neuron framework, e.g., the inhibition is often underestimated in experiment. Complex dendrites in general present formidable challenges to understanding neuronal information processing. To circumvent the difficulty, a prevalent viewpoint simplifies the neuronal morphology as a point representing the soma, and the excitatory and inhibitory synaptic currents originated from the dendrites are treated as linearly summed at the soma. Despite its extensive applications, the validity of the synaptic current description remains unclear, and the existing point neuron framework fails to characterize the spatiotemporal aspects of dendritic integration supporting specific computations. Using electrophysiological experiments, realistic neuronal simulations, and theoretical analyses, we demonstrate that the traditional assumption of linear summation of synaptic currents is oversimplified and underestimates the inhibition effect. We then derive a form of synaptic integration current within the point neuron framework to capture dendritic effects. In the derived form, the interaction between each pair of synaptic inputs on the dendrites can be reliably parameterized by a single coefficient, suggesting the inherent low-dimensional structure of dendritic integration. We further generalize the form of synaptic integration current to capture the spatiotemporal interactions among multiple synaptic inputs and show that a point neuron model with the synaptic integration current incorporated possesses the computational ability of a spatial neuron with dendrites, including direction selectivity, coincidence detection, logical operation, and a bilinear dendritic integration rule discovered in experiment. Our work amends the modeling of synaptic inputs and improves the computational power of a modeling neuron within the point neuron framework. |
| Databáze: | OpenAIRE |
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