통찰 - Computational Biology - # Modeling Neuron Dynamics from Electrophysiological Data

Estimating Biophysical Parameters of Neurons from Electrophysiological Recordings using a Deep Generative Model

Q: How could the EP-GAN architecture be extended to model neurons with spiking activity

To extend the EP-GAN architecture to model neurons with spiking activity, several modifications and additions can be made to the existing framework. One key aspect to consider is the incorporation of additional parameters and mechanisms that are specific to spiking neurons. This could involve including variables related to action potential generation, such as threshold potentials, action potential amplitude, and refractory periods. By expanding the parameter space to encompass these spiking-related features, the EP-GAN model can learn to generate parameters that accurately capture the spiking behavior of neurons. Furthermore, the training data used for EP-GAN can be augmented with recordings from neurons known to exhibit spiking activity. By including a diverse range of neuronal responses, including both graded potential and spiking responses, the model can learn to differentiate between the two types of activity and generate appropriate parameters for each. Additionally, the network architecture may need to be adjusted to accommodate the more complex dynamics of spiking neurons, potentially requiring additional layers or units to capture the intricacies of action potential generation and propagation. Overall, by expanding the parameter space, incorporating spiking-specific features, and diversifying the training data, the EP-GAN architecture can be extended to effectively model neurons with spiking activity.

Q: What other types of electrophysiological or anatomical data could be incorporated into the EP-GAN framework to improve the accuracy and generalization of the estimated neuron parameters

Incorporating additional types of electrophysiological or anatomical data into the EP-GAN framework can significantly enhance the accuracy and generalization of the estimated neuron parameters. Some potential data types that could be integrated include: Channel Activation Profiles: Including information about the activation and inactivation kinetics of ion channels can provide valuable insights into the dynamics of membrane potential changes. By incorporating data on channel activation profiles, EP-GAN can better capture the underlying mechanisms driving neuronal activity. Synaptic Connectivity: Information about the synaptic connections between neurons can help in modeling network interactions and dynamics. By including data on synaptic weights, connectivity patterns, and neurotransmitter release dynamics, EP-GAN can simulate more realistic neuronal responses within a network context. Morphological Data: Anatomical data such as neuronal morphology, dendritic arborization, and axonal projections can influence the electrical properties of neurons. By integrating morphological data into the model, EP-GAN can account for the structural characteristics that impact neuronal behavior. Neurotransmitter Receptor Distributions: Data on the distribution of neurotransmitter receptors on the neuron's membrane can affect its response to synaptic inputs. By incorporating information on receptor densities and subtypes, EP-GAN can better simulate the modulation of neuronal activity by neurotransmitters. By incorporating a diverse range of data types into the EP-GAN framework, the model can capture the complex interplay of factors influencing neuronal behavior and generate more accurate and generalizable neuron parameters.

Q: Given the potential for EP-GAN to scale to modeling large-scale neural networks, how could this approach be leveraged to study emergent properties of neuronal circuits beyond the individual neuron level

The scalability of EP-GAN to model large-scale neural networks presents exciting opportunities for studying emergent properties of neuronal circuits beyond the individual neuron level. To leverage this approach effectively, several strategies can be employed: Network Connectivity Analysis: By extending EP-GAN to incorporate data on synaptic connectivity and network architecture, the model can simulate the interactions between multiple neurons within a circuit. This can enable the study of network dynamics, information flow, and emergent behaviors arising from the collective activity of neurons. Dynamic Simulation of Network Activity: Using the estimated parameters from EP-GAN, dynamic simulations of neuronal network activity can be performed. By simulating the spatiotemporal dynamics of network activity, researchers can investigate phenomena such as synchronization, oscillations, and information processing within the circuit. Perturbation Studies: EP-GAN can be used to predict the effects of perturbations on network activity by altering parameters associated with specific neurons or synaptic connections. This can help in understanding how changes at the individual neuron level impact the overall network behavior and emergent properties. Integration with Experimental Data: Combining EP-GAN predictions with experimental data on network activity can validate the model's accuracy and provide insights into the underlying mechanisms of emergent properties. This iterative process of modeling and experimental validation can lead to a deeper understanding of complex neural circuits. By applying EP-GAN to study large-scale neuronal circuits, researchers can uncover novel insights into the emergent properties of neural networks and advance our understanding of brain function and information processing.

핵심 개념

A deep generative model called ElectroPhysiomeGAN (EP-GAN) can efficiently estimate the biophysical parameters of Hodgkin-Huxley neuron models from recorded electrophysiological responses of neurons.

초록

The paper introduces a novel deep generative model called ElectroPhysiomeGAN (EP-GAN) that can estimate the biophysical parameters of Hodgkin-Huxley (HH) neuron models from recorded electrophysiological data such as membrane potential responses and steady-state current profiles.

Key highlights:

EP-GAN combines a Generative Adversarial Network (GAN) architecture with a Recurrent Neural Network (RNN) Encoder to generate HH-model parameters given the input electrophysiological recordings.
The method can generate a large number of HH-model parameters (over 170) from the neuron's membrane potential responses and steady-state current profiles.
EP-GAN outperforms existing optimization-based methods like Differential Evolution and Genetic Algorithms in terms of accuracy and inference speed when modeling non-spiking neurons in the C. elegans nervous system.
EP-GAN preserves its performance even when provided with incomplete input data, such as up to 25% missing membrane potential responses or 75% missing steady-state current profiles.
Ablation studies show the importance of the reconstruction losses (for membrane potential and current) in aligning the Generator to produce accurate HH-model parameters.
EP-GAN can generalize to model novel neurons in C. elegans that were not previously characterized, achieving lower errors compared to other methods.
The method is currently limited to non-spiking neurons but could potentially be extended to model neurons with spiking activity in the future.

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biorxiv.org

통계

The HH-model used in this study has 15 ion channels and a total of 216 parameters, of which 176 are trainable.
EP-GAN was trained on 32,000 simulated HH-model neurons.
For the validation scenarios, EP-GAN achieved an average RMSE error of 5.84 mV and 5.81 mV for neurons sampled from the training and test data, respectively.
For the 3 experimentally recorded C. elegans neurons (RIM, AFD, AIY), EP-GAN achieved an overall RMSE error of 6.7 mV, which is 40% lower than the next best method (NSGA2 at 11.2 mV).
For the 6 additional C. elegans neurons (AWB, AWC, URX, RIS, DVC, HSN), EP-GAN achieved an even lower overall RMSE error of 5.35 mV.

인용구

"EP-GAN can learn a translation from electrophysiologically recorded responses and propose projections of them to parameter space."
"EP-GAN is currently limited to non-spiking neurons in C. elegans as it was designed and trained with HH-model describing the ion channels of these neurons."

핵심 통찰 요약

ElectroPhysiomeGAN: Generation of Biophysical Neuron Model Parameters from Recorded Electrophysiological Responses

by Kim,J., Liu,... 게시일 www.biorxiv.org 12-20-2023

https://www.biorxiv.org/content/10.1101/2023.12.19.572452v1

더 깊은 질문

How could the EP-GAN architecture be extended to model neurons with spiking activity

To extend the EP-GAN architecture to model neurons with spiking activity, several modifications and additions can be made to the existing framework. One key aspect to consider is the incorporation of additional parameters and mechanisms that are specific to spiking neurons. This could involve including variables related to action potential generation, such as threshold potentials, action potential amplitude, and refractory periods. By expanding the parameter space to encompass these spiking-related features, the EP-GAN model can learn to generate parameters that accurately capture the spiking behavior of neurons.
Furthermore, the training data used for EP-GAN can be augmented with recordings from neurons known to exhibit spiking activity. By including a diverse range of neuronal responses, including both graded potential and spiking responses, the model can learn to differentiate between the two types of activity and generate appropriate parameters for each. Additionally, the network architecture may need to be adjusted to accommodate the more complex dynamics of spiking neurons, potentially requiring additional layers or units to capture the intricacies of action potential generation and propagation.
Overall, by expanding the parameter space, incorporating spiking-specific features, and diversifying the training data, the EP-GAN architecture can be extended to effectively model neurons with spiking activity.

What other types of electrophysiological or anatomical data could be incorporated into the EP-GAN framework to improve the accuracy and generalization of the estimated neuron parameters

Incorporating additional types of electrophysiological or anatomical data into the EP-GAN framework can significantly enhance the accuracy and generalization of the estimated neuron parameters. Some potential data types that could be integrated include:

Channel Activation Profiles: Including information about the activation and inactivation kinetics of ion channels can provide valuable insights into the dynamics of membrane potential changes. By incorporating data on channel activation profiles, EP-GAN can better capture the underlying mechanisms driving neuronal activity.

Synaptic Connectivity: Information about the synaptic connections between neurons can help in modeling network interactions and dynamics. By including data on synaptic weights, connectivity patterns, and neurotransmitter release dynamics, EP-GAN can simulate more realistic neuronal responses within a network context.

Morphological Data: Anatomical data such as neuronal morphology, dendritic arborization, and axonal projections can influence the electrical properties of neurons. By integrating morphological data into the model, EP-GAN can account for the structural characteristics that impact neuronal behavior.

Neurotransmitter Receptor Distributions: Data on the distribution of neurotransmitter receptors on the neuron's membrane can affect its response to synaptic inputs. By incorporating information on receptor densities and subtypes, EP-GAN can better simulate the modulation of neuronal activity by neurotransmitters.

By incorporating a diverse range of data types into the EP-GAN framework, the model can capture the complex interplay of factors influencing neuronal behavior and generate more accurate and generalizable neuron parameters.

Given the potential for EP-GAN to scale to modeling large-scale neural networks, how could this approach be leveraged to study emergent properties of neuronal circuits beyond the individual neuron level

The scalability of EP-GAN to model large-scale neural networks presents exciting opportunities for studying emergent properties of neuronal circuits beyond the individual neuron level. To leverage this approach effectively, several strategies can be employed:

Network Connectivity Analysis: By extending EP-GAN to incorporate data on synaptic connectivity and network architecture, the model can simulate the interactions between multiple neurons within a circuit. This can enable the study of network dynamics, information flow, and emergent behaviors arising from the collective activity of neurons.

Dynamic Simulation of Network Activity: Using the estimated parameters from EP-GAN, dynamic simulations of neuronal network activity can be performed. By simulating the spatiotemporal dynamics of network activity, researchers can investigate phenomena such as synchronization, oscillations, and information processing within the circuit.

Perturbation Studies: EP-GAN can be used to predict the effects of perturbations on network activity by altering parameters associated with specific neurons or synaptic connections. This can help in understanding how changes at the individual neuron level impact the overall network behavior and emergent properties.

Integration with Experimental Data: Combining EP-GAN predictions with experimental data on network activity can validate the model's accuracy and provide insights into the underlying mechanisms of emergent properties. This iterative process of modeling and experimental validation can lead to a deeper understanding of complex neural circuits.

By applying EP-GAN to study large-scale neuronal circuits, researchers can uncover novel insights into the emergent properties of neural networks and advance our understanding of brain function and information processing.