Neuronal synaptic conductance reconstruction method

Abstract

A method for reconstructing neuronal synaptic conductance involves measuring the synaptic current at the cell body by varying the amplitude of the cell body voltage clamp and calculating the average synaptic charge at the cell body. The average local synaptic conductance is then obtained through function fitting. The synaptic current is then obtained by varying the start time of the cell body voltage clamp. After integrating the synaptic charge, an estimate of the time constant of the synaptic conductance is obtained through fitting. This estimate is then combined with the average local synaptic conductance to obtain the complete local synaptic conductance. This method, based on the spatial structural complexity of neurons, overcomes the spatial effects of neuronal voltage clamping. By using the cell body voltage clamp method, the local synaptic conductance of neurons (including the average local synaptic input strength and time constant) is restored, significantly improving measurement accuracy.

Description

Technical Field

[0001] The present invention relates to a technology in the field of neuron signal measurement, in particular to a neuron synaptic conductance reconstruction method. Background Art

[0002] Existing techniques for measuring synaptic conductance fail to fully account for the spatial complexity of dendritic structure. They theoretically assume that voltage clamping at the cell body can fully control the voltage at all locations within the neuron, treating them as equipotential. However, due to the complexity of dendrites, conductance measurements in some regions, particularly at the distal ends of dendrites, deviate significantly from actual results, and theoretical calculations may not align with reality. Summary of the Invention

[0003] In response to the above-mentioned deficiencies in the prior art, the present invention proposes a method for reconstructing neuronal synaptic conductance. Based on the spatial structural complexity of neurons, this method overcomes the spatial effect of neuronal voltage clamping and restores the local synaptic conductance of neurons (including the average local synaptic conductance and time constant) through the cell body voltage clamp method, significantly improving the measurement accuracy.

[0004] The present invention is achieved through the following technical solutions:

[0005] The present invention relates to a method for reconstructing neuronal synaptic conductance. The method comprises the following steps: measuring the synaptic current at the cell body by changing the amplitude of the cell body voltage clamp, calculating the average synaptic charge at the cell body, and then obtaining the average local synaptic conductance by function fitting; then obtaining the synaptic current by changing the start time of the cell body voltage clamp, integrating the synaptic charge, and obtaining the time constant estimate of the synaptic conductance by fitting. The complete local synaptic conductance is obtained by combining the average local synaptic conductance.

[0006] Technical Effects

[0007] The present invention is based on cable theory and asymptotic analysis, combined with the dynamic characteristics of neurons with complex spatial structures when receiving synaptic input, and significantly improves the accuracy of synaptic conductance measurement by establishing a relationship model between synaptic conductance and synaptic current measured at the cell body. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 Flowchart of the present invention;

[0009] Figure 2 Schematic diagram comparing the dependence of the average local synaptic conductance estimation error on the input intensity for the passive pyramidal neuron model;

[0010] In the figure: a is for excitatory (E) input, b is for inhibitory (I) input;

[0011] Figure 3Schematic diagram comparing the position dependence of the error in the estimation of the average local synaptic conductance;

[0012] Figure: A is a passive pyramidal neuron model, showing the spatial dependence of the relative error in measuring the average local synaptic conductance using the present invention (left) and the prior art (right), with the E input strength at each location fixed; B is similar to A but includes active ion channels; C is similar to B but blocks hyperpolarization-activated cyclic nucleotide-gated (HCN) channels;

[0013] Figure 4 Schematic diagram comparing the recovery error of the average local synaptic conductance of fast-firing neurons in the prefrontal cortex;

[0014] Figure 2 shows the spatial dependence of the relative error in measuring the average local synaptic conductance using the present invention (left) and the prior art (right) when the synaptic input strength at each position is fixed. A is the E synaptic input in a passive fast-firing neuron; B is the E synaptic input in an active fast-firing neuron; C is the I synaptic input in a passive fast-firing neuron; and D is the I synaptic input in an active fast-firing neuron.

[0015] Figure 5 Schematic diagram of the comparison of the recovery error of the average local synaptic conductance of cerebellar Purkinje neurons;

[0016] In the figure: AD, when the synaptic input strength at each position is fixed, the spatial dependence of the relative error in measuring the average local synaptic conductance by the present invention (left) and the prior art (right), A is the E synaptic input in a passive cerebellar Purkinje neuron; B is the E synaptic input in an active cerebellar Purkinje neuron; C is the I synaptic input in a passive cerebellar Purkinje neuron; D is the I synaptic input in an active cerebellar Purkinje neuron;

[0017] Figure 6 Schematic diagram of the comparison of the recovery error of the average local synaptic conductance of hippocampal pyramidal neurons;

[0018] In the figure: AD, when the synaptic input strength at each position is fixed, the spatial dependence of the relative error in measuring the average local synaptic conductance by the present invention (left) and the prior art (right), A is the E synaptic input in a passive hippocampal pyramidal neuron; B is the E synaptic input in an active hippocampal pyramidal neuron; C is the I synaptic input in a passive hippocampal pyramidal neuron; D is the I synaptic input in an active hippocampal pyramidal neuron;

[0019] Figure 7 Schematic diagram comparing the estimated time constants of local synaptic conductance in passive pyramidal neurons;

[0020] In the figure: the left side is for E input, and the right side is for I input;

[0021] Figure 8 Schematic diagram comparing the position dependence of the estimated error of the time constant of local synaptic conductance in pyramidal neurons;

[0022] In the figure: the left figure is the synaptic conductance rise time constant τ r The error on the right is the synaptic conductance decrease time constant τ d Error; A is a passive pyramidal neuron; B is an active pyramidal neuron; C is an active pyramidal neuron but with the HCN channel deleted;

[0023] Figure 9 Schematic diagram comparing the recovery error of the time constant of local synaptic conductance of fast-firing neurons in the prefrontal cortex;

[0024] In the figure: A is a passive fast-discharging neuron with E input; B is an active fast-discharging neuron with E input; C is a passive fast-discharging neuron with I input; D is an active fast-discharging neuron with I input;

[0025] Figure 10 Schematic diagram of the comparison of the recovery error of the time constant of the local synaptic conductance of cerebellar Purkinje neurons;

[0026] In the figure: A: Passive Purkinje neuron, E input; B: Active Purkinje neuron, E input; C: Passive Purkinje neuron, I input; D: Active Purkinje neuron, I input;

[0027] Figure 11 Schematic diagram of the comparison of the recovery error of the time constant of the local synaptic conductance of hippocampal pyramidal neurons;

[0028] In the figure: A: passive hippocampal pyramidal neuron, E input; B: active hippocampal pyramidal neuron, E input; C: passive hippocampal pyramidal neuron, I input; D: active hippocampal pyramidal neuron, I input. DETAILED DESCRIPTION

[0029] like Figure 1 As shown in FIG, a neuron synaptic conductance reconstruction method involved in this embodiment includes:

[0030] Step 1: Use the cell body voltage clamp to control the cell body voltage to remain constant and collect the injection current difference of the voltage clamp under the conditions of synaptic input and no synaptic input to obtain the synaptic current I at the cell body. syn The average synaptic current is obtained by averaging multiple acquisitions. The average synaptic charge was obtained by integration, and the average synaptic charge at the cell body corresponding to different voltages was obtained by further changing the clamping voltage of the cell body voltage clamp and repeatedly collecting data.

[0031] Step 2: According to the average synaptic charge at the cell body obtained in step 1, the corresponding curve is fitted with the corresponding clamping voltage to obtain the average local synaptic conductance, specifically: y = kx + b, where: x, y are the standardized cell body clamping voltage (i.e., cell body clamping voltage minus resting voltage) and the corresponding average synaptic charge at the cell body, respectively, to obtain the average local synaptic conductance Where: ε is the reversal voltage of the synapse;

[0032] Step 3: At time t>0, change the clamping voltage of the cell body voltage clamp, monitor and record the difference in the voltage clamp injection current with and without synaptic input, obtain the corresponding synaptic current, and then integrate it to obtain the synaptic charge. Change the time t to obtain different synaptic charges.

[0033] Step 4: Based on the synaptic charge obtained in step 3, the time constant of synaptic conductance is estimated by fitting, specifically: in: and are the estimated values of the time constant of synaptic conductance, t and y are the changing moments of cell body clamping voltage and the corresponding synaptic charge, respectively;

[0034] Step 5: Based on the average local synaptic conductance obtained in step 3 and the time constant of the synaptic conductance obtained in step 4, the synaptic conductance strength f and parameter n are estimated, and then the complete local synaptic conductance is calculated. in:

[0035] Preferably, if the neuron contains a hyperpolarization-activated cyclic nucleotide-gated (HCN) channel, the channel can be blocked in advance by drugs before step 1, which can further improve the accuracy of the reconstructed synaptic conductance signal of the present invention.

[0036] After specific experiments, taking the fifth layer pyramidal neurons of the cerebral cortex as an example, the above method can accurately restore the average local synaptic conductance of neurons. Specifically, consider an E synaptic input located on a dendritic trunk 300 microns away from the cell body, and the input starts at t = 0. At the same time, by applying a voltage clamp to the cell body, the clamping amplitude of the cell body voltage can be changed while keeping the local synaptic input unchanged to measure the synaptic current under different conditions. Through the relationship between the average local synaptic conductance and the slope and intercept, the average local synaptic conductance can be accurately restored in the range of synaptic input strength: so that the excitatory postsynaptic voltage (EPSP) is from 0.07 mV to 3.5 mV, such as Figure 2 shown.

[0037] like Figure 3As shown, for E synaptic inputs located at different dendritic positions, the average error of the present invention remains around 10%. In contrast, the average relative error of the prior art can reach 26%. Moreover, when the E synaptic input is far from the cell body, the estimation error of the prior art can even exceed 60%.

[0038] Further simulations were performed using cortical layer V pyramidal neurons with various voltage-gated conductances. The addition of active conductances resulted in a slight decrease in accuracy (average relative error of the present invention: 14% for E input and 10% for I input). This decrease is mainly attributed to the distortion of synaptic currents by active conductances, such as Figure 3 Nonetheless, it is noteworthy that under these harsher conditions, the accuracy of the present invention still reliably exceeds that of existing methods at most synaptic locations.

[0039] like Figure 3 As shown, blocking the HCN current restores high accuracy of the estimation: average relative errors of the present invention are 9% for E input and 10% for I input, compared with average relative errors of the prior art: 26% for E input and 32% for I input.

[0040] The local synaptic time constant recovery method is numerically validated using a multicompartmental model of cortical layer V pyramidal neurons. This method accurately extracts the rise and decay time constants of local synaptic conductances over a wide range of excitatory and inhibitory synaptic input strengths, such as Figure 7 As shown (estimated rise time constants: 1.0-1.2 ms for E synapses and 1.0-1.8 ms for I synapses, true value 1.0 ms; estimated decay time constants: 5.0-5.2 ms for E synapses and 5.0-5.5 ms for I synapses, true value 5.0 ms). These inputs were located on dendritic trunks 300 μm from the cell body, with EPSPs ranging from 0.07 mV to 3.5 mV and inhibitory postsynaptic voltages (IPSPs) ranging from 0.01 mV to 1.2 mV.

[0041] Furthermore, the present invention enables accurate estimation of synaptic time constants when the synaptic input is far from the cell body, e.g. Figure 8 Figure A. Furthermore, the present invention demonstrated high efficiency even in the presence of many active ion channels, but accuracy decreased significantly for synapses located on distal apical dendrites. Notably, blocking HCN channels significantly improved the estimation, suggesting that while the presence of most voltage-dependent ion channels has little effect on synaptic time constant estimation, certain specific ion channels, such as HCN currents, have a significant impact on the estimation.

[0042] Secondly, whether it is the fast-discharging neurons in the prefrontal cortex, the Purkinje cells in the cerebellum, or the pyramidal cells in the hippocampus, the present invention can more accurately restore the local synaptic conductance of these neurons, such as Figure 4-Figure 6 as well as Figures 9-11 This universality makes the present invention have important application value in neuroscience research and helps to reveal the functions and mechanisms of various neurons in different brain regions.

[0043] The above-mentioned specific implementation can be partially adjusted in different ways by those skilled in the art without departing from the principles and purpose of the present invention. The scope of protection of the present invention shall be based on the claims and shall not be limited by the above-mentioned specific implementation. All implementation schemes within its scope shall be subject to the constraints of the present invention.

Claims

1. A method for reconstructing neuronal synaptic conductance, characterized in that: By changing the amplitude of the cell body voltage clamp, the synaptic current at the cell body is measured and the average synaptic charge at the cell body is calculated, and then the average local synaptic conductance is obtained by function fitting; then, by changing the start time of the cell body voltage clamp, the synaptic current is obtained, and after integrating the synaptic charge, the time constant estimate of the synaptic conductance is obtained by fitting, and combined with the average local synaptic conductance, the complete local synaptic conductance is obtained.

2. The neuron synaptic conductance reconstruction method according to claim 1, characterized in that: The average local synaptic conductance is obtained by controlling the cell body voltage to remain constant through the cell body voltage clamp and collecting the injection current difference of the voltage clamp under the conditions of synaptic input and no synaptic input to obtain the synaptic current at the cell body. The average synaptic current is obtained by averaging multiple acquisitions. , the average synaptic charge is obtained by integration, and the average synaptic charge at the cell body corresponding to different voltages is obtained by further changing the clamping voltage of the cell body voltage clamp and repeatedly collecting data. The corresponding curve is fitted based on the average synaptic charge at the cell body and the corresponding clamping voltage, and then the average local synaptic conductance is obtained, which is specifically: , where x and y are the standardized cell body clamp voltage and the corresponding average synaptic charge at the cell body, respectively, and the average local synaptic conductance is obtained. ,in: is the reversal voltage of the synapse; The normalized cell body clamp voltage is the cell body clamp voltage minus the resting voltage.

3. The neuron synaptic conductance reconstruction method according to claim 2, characterized in that: The time constant of the synaptic conductance is estimated by The clamping voltage of the cell body voltage clamp is constantly changed, and the difference of the injected current of the voltage clamp with and without synaptic input is monitored and recorded. After obtaining the synaptic current at the corresponding moment, the corresponding synaptic charge is obtained by integration. The corresponding synaptic electrical charge is obtained, and the time constant of synaptic conductance is estimated by fitting, specifically: ,in: and are the estimated time constants of synaptic conductance, and t is the moment of change of the cell body clamping voltage.

4. The neuron synaptic conductance reconstruction method according to claim 3, characterized in that: The complete local synaptic conductance is estimated by back-calculating the synaptic conductance strength based on the average local synaptic conductance and the time constant. as well as , and then calculate the complete local synaptic conductance .

5. The neuron synaptic conductance reconstruction method according to claim 1, wherein: By blocking the current of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, that is, when neurons contain hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, the channels are pre-blocked by drugs before detecting the average synaptic current to improve the accuracy of the reconstructed synaptic conductance signal.

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