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Tang Y, Zhang X, An L, Yu Z, Liu JK. Diverse role of NMDA receptors for dendritic integration of neural dynamics. PLoS Comput Biol 2023; 19:e1011019. [PMID: 37036844 PMCID: PMC10085026 DOI: 10.1371/journal.pcbi.1011019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 03/09/2023] [Indexed: 04/11/2023] Open
Abstract
Neurons, represented as a tree structure of morphology, have various distinguished branches of dendrites. Different types of synaptic receptors distributed over dendrites are responsible for receiving inputs from other neurons. NMDA receptors (NMDARs) are expressed as excitatory units, and play a key physiological role in synaptic function. Although NMDARs are widely expressed in most types of neurons, they play a different role in the cerebellar Purkinje cells (PCs). Utilizing a computational PC model with detailed dendritic morphology, we explored the role of NMDARs at different parts of dendritic branches and regions. We found somatic responses can switch from silent, to simple spikes and complex spikes, depending on specific dendritic branches. Detailed examination of the dendrites regarding their diameters and distance to soma revealed diverse response patterns, yet explain two firing modes, simple and complex spike. Taken together, these results suggest that NMDARs play an important role in controlling excitability sensitivity while taking into account the factor of dendritic properties. Given the complexity of neural morphology varying in cell types, our work suggests that the functional role of NMDARs is not stereotyped but highly interwoven with local properties of neuronal structure.
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Affiliation(s)
- Yuanhong Tang
- Institute for Artificial Intelligence, Department of Computer Science and Technology, Peking University, Beijing, China
| | - Xingyu Zhang
- Guangzhou Institute of Technology, Xidian University, Guangzhou, China
| | - Lingling An
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Zhaofei Yu
- Institute for Artificial Intelligence, Department of Computer Science and Technology, Peking University, Beijing, China
| | - Jian K Liu
- School of Computing, University of Leeds, Leeds, United Kingdom
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Shang Z, Huang J, Liu N, Zhang X. Bi-directional Control of Synaptic Input Summation and Spike Generation by GABAergic Inputs at the Axon Initial Segment. Neurosci Bull 2023; 39:1-13. [PMID: 35639277 PMCID: PMC9849666 DOI: 10.1007/s12264-022-00887-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 03/03/2022] [Indexed: 01/22/2023] Open
Abstract
Differing from other subtypes of inhibitory interneuron, chandelier or axo-axonic cells form depolarizing GABAergic synapses exclusively onto the axon initial segment (AIS) of targeted pyramidal cells (PCs). However, the debate whether these AIS-GABAergic inputs produce excitation or inhibition in neuronal processing is not resolved. Using realistic NEURON modeling and electrophysiological recording of cortical layer-5 PCs, we quantitatively demonstrate that the onset-timing of AIS-GABAergic input, relative to dendritic excitatory glutamatergic inputs, determines its bi-directional regulation of the efficacy of synaptic integration and spike generation in a PC. More specifically, AIS-GABAergic inputs promote the boosting effect of voltage-activated Na+ channels on summed synaptic excitation when they precede glutamatergic inputs by >15 ms, while for nearly concurrent excitatory inputs, they primarily produce a shunting inhibition at the AIS. Thus, our findings offer an integrative mechanism by which AIS-targeting interneurons exert sophisticated regulation of the input-output function in targeted PCs.
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Affiliation(s)
- Ziwei Shang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Junhao Huang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Nan Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Xiaohui Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
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Abstract
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.
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Li S, Liu N, Yao L, Zhang X, Zhou D, Cai D. Determination of effective synaptic conductances using somatic voltage clamp. PLoS Comput Biol 2019; 15:e1006871. [PMID: 30835719 PMCID: PMC6420044 DOI: 10.1371/journal.pcbi.1006871] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 03/15/2019] [Accepted: 02/14/2019] [Indexed: 11/20/2022] Open
Abstract
The interplay between excitatory and inhibitory neurons imparts rich functions of the brain. To understand the synaptic mechanisms underlying neuronal computations, a fundamental approach is to study the dynamics of excitatory and inhibitory synaptic inputs of each neuron. The traditional method of determining input conductance, which has been applied for decades, employs the synaptic current-voltage (I-V) relation obtained via voltage clamp. Due to the space clamp effect, the measured conductance is different from the local conductance on the dendrites. Therefore, the interpretation of the measured conductance remains to be clarified. Using theoretical analysis, electrophysiological experiments, and realistic neuron simulations, here we demonstrate that there does not exist a transform between the local conductance and the conductance measured by the traditional method, due to the neglect of a nonlinear interaction between the clamp current and the synaptic current in the traditional method. Consequently, the conductance determined by the traditional method may not correlate with the local conductance on the dendrites, and its value could be unphysically negative as observed in experiment. To circumvent the challenge of the space clamp effect and elucidate synaptic impact on neuronal information processing, we propose the concept of effective conductance which is proportional to the local conductance on the dendrite and reflects directly the functional influence of synaptic inputs on somatic membrane potential dynamics, and we further develop a framework to determine the effective conductance accurately. Our work suggests re-examination of previous studies involving conductance measurement and provides a reliable approach to assess synaptic influence on neuronal computation. To understand synaptic mechanisms underlying neuronal computations, a fundamental approach is to use voltage clamp to measure the dynamics of excitatory and inhibitory input conductances. Due to the space clamp effect, the measured conductance in general deviates from the local input conductance on the dendrites, hence its biological interpretation is questionable, as we demonstrate in this work. We further propose the concept of effective conductance that is proportional to the local input conductance on the dendrites and reflects directly the synaptic impact on spike generation, and develop a framework to determine the effective conductance reliably. Our work provides a biologically plausible metric for elucidating synaptic influence on neuronal computation under the constraint of the space clamp effect.
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Affiliation(s)
- Songting Li
- School of Mathematical Sciences, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - Nan Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Li Yao
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiaohui Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- * E-mail: (XZ); (DZ)
| | - Douglas Zhou
- School of Mathematical Sciences, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China
- * E-mail: (XZ); (DZ)
| | - David Cai
- School of Mathematical Sciences, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China
- Courant Institute of Mathematical Sciences and Center for Neural Science, New York University, New York, New York, United States of America
- NYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
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Li S, Xu J, Chen G, Lin L, Zhou D, Cai D. The characterization of hippocampal theta-driving neurons - a time-delayed mutual information approach. Sci Rep 2017; 7:5637. [PMID: 28717183 PMCID: PMC5514076 DOI: 10.1038/s41598-017-05527-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 05/30/2017] [Indexed: 11/23/2022] Open
Abstract
Interneurons are important for computation in the brain, in particular, in the information processing involving the generation of theta oscillations in the hippocampus. Yet the functional role of interneurons in the theta generation remains to be elucidated. Here we use time-delayed mutual information to investigate information flow related to a special class of interneurons—theta-driving neurons in the hippocampal CA1 region of the mouse—to characterize the interactions between theta-driving neurons and theta oscillations. For freely behaving mice, our results show that information flows from the activity of theta-driving neurons to the theta wave, and the firing activity of theta-driving neurons shares a substantial amount of information with the theta wave regardless of behavioral states. Via realistic simulations of a CA1 pyramidal neuron, we further demonstrate that theta-driving neurons possess the characteristics of the cholecystokinin-expressing basket cells (CCK-BC). Our results suggest that it is important to take into account the role of CCK-BC in the generation and information processing of theta oscillations.
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Affiliation(s)
- Songting Li
- Courant Institute of Mathematical Sciences and Center for Neural Science, New York University, New York, NY, United States of America
| | - Jiamin Xu
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Life Science and the Collaborative Innovation Center for Brain Science, Institute of Brain Functional Genomics, East China Normal University, Shanghai, China
| | - Guifen Chen
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Life Science and the Collaborative Innovation Center for Brain Science, Institute of Brain Functional Genomics, East China Normal University, Shanghai, China
| | - Longnian Lin
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Life Science and the Collaborative Innovation Center for Brain Science, Institute of Brain Functional Genomics, East China Normal University, Shanghai, China.
| | - Douglas Zhou
- School of Mathematical Sciences, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China.
| | - David Cai
- Courant Institute of Mathematical Sciences and Center for Neural Science, New York University, New York, NY, United States of America. .,School of Mathematical Sciences, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China. .,NYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
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