P321 Overcoming the space-clamp effect: reliable recovery of local and effective synaptic conductances of neurons
Ziling Wang1,2,3, David McLaughlin*4,5,6,7,8, Douglas Zhou*1,2,3, Songting Li*1,2,3
1School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
2Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
3Ministry of Education Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai 200240, China
4Courant Institute of Mathematical Sciences, New York University, New York, New York 10012
5Center for Neural Science, New York University, New York, New York 10012
6New York University Shanghai, Shanghai 200122, China
7NYU Tandon School of Engineering, New York University, Brooklyn, NY 11201
8Neuroscience Institute of NYU Langone Health, New York University, New York, NY 10016
*Email: david.mclaughlin@nyu.edu, zdz@sjtu.edu.cn, or songting@sjtu.edu.cn
Introduction
To understand the interplay between excitatory (E) and inhibitory (I) inputs in neuronal networks, it is necessary to separate and recover E from I inputs. Somatic recordings are more accessible than those from local dendrites, which poses challenges in recovering input characteristics and distinguishing E from I after dendritic filtering. Somatic voltage clamp methods [1,2] address these issues by assuming an iso-potential neuron. However, as shown in Fig. 1A, this assumption is debated, as the voltage is nonuniform across neurons due to complex morphology [3]. This nonuniform voltage, known collectively as the space clamp effect, leads to inaccurate conductance estimations and even yields erroneous negative conductances [4].
Methods
We study mathematical models of voltage clamping, beginning with an asymptotic analysis of an idealized cable neuron model with realistic time-varying synaptic inputs, and then extending the analysis to simulations of realistic model neurons with varying types, morphologies, and active ion channels. The asymptotic analysis describes in detail the response of the idealized neuron under somatic clamping, and thus captures the discrepancy between the local synaptic conductance on the dendrite, the effective conductances at the soma and the traditional voltage clamp approximation. This discrepancy arises primarily due to the traditional approach’s oversight of the space clamp effect.
Results
With this detailed quantitative understanding of neural response, we refine the traditional method to circumvent the space clamp effect, thus enabling accurate recovery of local and effective conductances from somatic measurements. Specifically, we develop a two-step clamp method that separately recovers the mean and time constants of local conductance on the dendrite when a neuron receives a single synaptic input. Besides, under in-vivo conditions of multiple inputs, we propose an intercept method to extract effective net E and I conductances. Both methods are grounded in perturbation analyses and validated using biologically detailed multi-compartment neuron models with active channels included, as shown in Fig. 1B-1D.
Discussion
Our methods consistently achieve high accuracy in estimating both local and effective conductances through simulations involving various realistic neuron models. Accuracy holds over a broad range of synaptic input strengths, input locations, ionic channels, and receptors. However, two factors can degrade accuracy: large EPSPs and active HCN channels. Large EPSPs, particularly at dendritic tips, require higher-order corrections beyond first-order perturbation theory. Besides, HCN channels also reduce accuracy, but blocking them restores precision. Our approach is robust across various neuron types, as demonstrated in simulations of mPFC fast-spiking neurons, cerebellar Purkinje neurons, and hippocampal pyramidal neurons.
Figure 1. Performance of our method for recovering local and effective conductances in a realistic neocortical layer 5 pyramidal neuron model. (A) Voltage distribution across the pyramidal neuron under somatic voltage clamp condition. (B–D) Our methods perform well in estimating local synaptic conductance features—the mean (B) and time constant (C), as well as the effective conductance at the soma (D).
Acknowledgements
This work was supported by Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project (No.2021ZD0200204 D.Z., S.L.); Science and Technology Commission of Shanghai Municipality (No.24JS2810400 D.Z.); National Natural Science Foundation of China (No.12225109, 12071287 D.Z.; 12271361, 12250710674 S.L.) and Student Innovation Center at SJTU (Z.W., D.Z. and S.L.).
References
[1].https://doi.org/10.1038/30735
[2].https://doi.org/10.1016/j.neuron.2011.12.013
[3].https://doi.org/10.1038/nrn2286
[4].https://doi.org/10.1038/nn.2137
