Biology subjects

Shlizerman, E.

Publications and source records attributed to Shlizerman, E..

6 recordsLinked to original sources

bioRxiv2023

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

Recent advances in connectomics, biophysics, and neuronal electrophysiology warrant modeling of neurons with further details in both network interaction and cellular dynamics. Such models may be referred to as ElectroPhysiome, as they incorporate the connectome and individual neuron electrophysiology to simulate neuronal activities. The nervous system of C. elegans is considered a viable framework for such ElectroPhysiome studies due to advances in connectomics of its somatic nervous system and electrophysiological recordings of neuron responses. In order to achieve a simulated ElectroPhysiome, the set of parameters involved in modeling individual neurons need to be estimated from electrophysiological recordings. Here, we address this challenge by developing a deep generative estimation method called ElectroPhysiomeGAN (EP-GAN), which once trained, can instantly generate parameters associated with the Hodgkin-Huxley neuron model (HH-model) for multiple neurons with graded potential response. The method combines Generative Adversarial Network (GAN) architecture with Recurrent Neural Network (RNN) Encoder and can generate an extensive number of parameters (>170) given the neurons membrane potential responses and steady-state current profiles. We validate our method by estimating HH-model parameters for 200 simulated neurons with graded membrane potential followed by 9 experimentally recorded neurons (where 6 of them newly recorded) in the nervous system of C. elegans. Comparison of EP-GAN with existing estimation methods shows EP-GAN advantage in the accuracy of estimated parameters and inference speed for both small and large number of parameters being inferred. In addition, the architecture of EP-GAN permits input with arbitrary clamping protocols, allowing inference of parameters even when partial membrane potential and steady-state currents profile are given as inputs. EP-GAN is designed to leverage the generative capability of GAN to align with the dynamical structure of HH-model, and thus able to achieve such performance.

neuroscience↗

bioRxiv2022

OpenLabCluster: Active Learning Based Clustering and Classification of Animal Behaviors in Videos Based on Automatically Extracted Kinematic Body Keypoints

Quantifying natural behavior from video recordings is a key component in ethological studies. Markerless pose estimation methods have provided an important step toward that goal by automatically inferring kinematic body keypoints. Such methodologies warrant efficient organization and interpretation of keypoints sequences into behavioral categories. Existing approaches for behavioral interpretation often overlook the importance of representative samples in learning behavioral classifiers. Consequently, they either require extensive human annotations to train a classifier or rely on a limited set of annotations, resulting in suboptimal performance. In this work, we introduce a general toolset which reduces the required human annotations and is applicable to various animal species. In particular, we introduce OpenLabCluster, which clusters temporal keypoint segments into clusters in the latent space, and then employ an Active Learning (AL) approach that refines the clusters and classifies them into behavioral states. The AL approach selects representative examples of segments to be annotated such that the annotation informs clustering and classification of all temporal segments. With these methodologies, OpenLabCluster contributes to faster and more accurate organization of behavioral segments with only a sparse number of them being annotated. We demonstrate OpenLabCluster performance on four different datasets, which include different animal species exhibiting natural behaviors, and show that it boosts clustering and classification compared to existing methods, even when all segments have been annotated. OpenLabCluster has been developed as an open-source interactive graphic interface which includes all necessary functions to perform clustering and classification, informs the scientist of the outcomes in each step, and incorporates the choices made by the scientist in further steps.

animal behavior and cognition↗

bioRxiv2022

Multi-block RNN Autoencoders Enable Broadband ECoG Signal Reconstruction

ObjectiveNeural dynamical models reconstruct neural data using dynamical systems. These models enable direct reconstruction and estimation of neural time-series data as well as estimation of neural latent states. Nonlinear neural dynamical models using recurrent neural networks in an encoder-decoder architecture have recently enabled accurate single-trial reconstructions of neural activity for neuronal spiking data. While these models have been applied to neural field potential data, they have only so far been applied to signal feature reconstruction (e.g. frequency band power), and have not yet produced direct reconstructions of broadband time-series data preserving signal phase and temporal resolution. ApproachHere we present two encoder-decoder model architectures - the RNN autoencoder (RAE) and multi-block RAE (MRAE) for direct time-series reconstruction of broadband neural data. We trained and tested models on multi-channel micro-Electricorticography (ECoG) recordings from non-human primate motor corticies during unconstrained behavior. Main ResultsWe show that RAE reconstructs micro-electrocorticography recordings, but has reconstruction accuracy that is band-limited to model scale. The MRAE architecture overcomes these time-bandwidth restrictions, yielding broadband (0-100 Hz), accurate reconstructions of ECoG data. SignificanceRAE and MRAE reconstruct broadband ECoG data through multiblock dynamical modeling. The MRAE overcomes time-bandwitdh restrictions to provide improved accuracy for long time duration signals. The reconstruction capabilities provided by these models for broadband neural signals like ECoG may enable the development of improved tools and analysis for basic scientific research and applications like brain-computer interfaces.

neuroscience↗

bioRxiv2019

Whole integration of neural connectomics, dynamics and bio-mechanics for identification of behavioral sensorimotor pathways in Caenorhabditis elegans

Computational approaches which emulate in-vivo nervous system are needed to investigate mechanisms of the brain to orchestrate behavior. Such approaches must integrate a series of biophysical models encompassing the nervous system, muscles, biomechanics to allow observing the system in its entirety while supporting incorporations of different model variations. Here we develop modWorm: a modeling framework for the nematode Caenorhabditis elegans using modular integration approach. modWorm allows for construction of a model as an integrated series of configurable, exchangeable modules each describing specific biophysical processes across different modalities (e.g., nervous system, muscles, body). Utilizing modWorm, we propose a base neuro-mechanical model for C. elegans built upon the complete connectome. The model integrates a series of 7 modules: i) intra-cellular dynamics, ii) electrical and iii) chemical extra-cellular neural dynamics, iv) translation of neural activity to muscle calcium dynamics, v) muscle calcium dynamics to muscle forces, vi) muscle forces to body postures and vii) proprioceptive feedback. We validate the base model by in-silico injection of constant currents into sensory and inter-neurons known to be associated with locomotion behaviors and by applying external forces to the body. Applications of in-silico neural stimuli experimentally known to modulate locomotion show that the model can recapitulate natural behavioral responses such as forward and backward locomotion as well as mid-locomotion stimuli induced responses such as avoidance and turns. Furthermore, through in-silico ablation surveys, the model can infer novel neural circuits involved in sensorimotor behaviors. To further dissect mechanisms of locomotion, we utilize modWorm to introduce empirical based variations of intra and extra-cellular dynamics as well as model optimizations on associated parameters to elucidate their effects on simulated locomotion dynamics compared to experimental findings. Our results show that the proposed framework can be utilized to identify neural circuits which control, mediate and generate natural behavior.

neuroscience↗

bioRxiv2017

Neural Interactome: Interactive Simulation of a Neuronal System

Both connectivity and biophysical processes determine the functionality of neuronal networks. We, therefore, develop a real-time framework, called Neural Interactome1, to simultaneously visualize and interact with the structure and dynamics of such networks. Neural Interactome is a cross-platform framework, which combines graph visualization with the simulation of neural dynamics, or experimentally recorded multi neural time series, to allow application of stimuli to neurons to examine network responses. In addition, Neural Interactome supports structural changes, such as disconnection of neurons from the network (ablation feature), as typically done in experiments. Neural dynamics can be explored on a single neuron level (using a zoom feature), back in time (using a review feature) and recorded (using presets feature). We implement the framework using a model of the nervous system of Caenorhabditis elegans (C. elegans) nematode, a model organism for which full connectome and neural dynamics have been resolved. We show that Neural Interactome assists in studying neural response patterns associated with locomotion and other stimuli. In particular, we demonstrate how stimulation and ablation help in identifying neurons that shape particular dynamics. We examine scenarios that were experimentally studied, such as touch response circuit, and explore new scenarios that did not undergo elaborate experimental studies. The development of the Neural Interactome was guided by generic concepts to be applicable to neuronal networks with different neural connectivity and dynamics.\n\nAuthor SummaryEmerging neuroimaging techniques and novel optical interfaces which record and control neural dynamics enable detailed computational connectivity and dynamics models for neurobiological systems. An open question stemming from these advances is how to validate, simulate and apply these models to predict network functionality. Supervised empirical exploration to identify functional stimulations is an elaborate process, and direct computational approach of sequential stimulation is also formidable since produces large amounts of data without clarity on how it can be used to steer toward meaningful functionalities. We therefore develop a platform to inspect network dynamics in real time while preserving structural connectivity properties, displaying the dynamics on a graph, with possibilities to identify functional sub circuits and review the simulated dynamics. The platform allows for real time interactions with the network such as variation of stimuli and performing connectivity changes as neural ablation. We apply the platform to Caenorhabditis elegans nematode nervous system model. We revisit experimentally known scenarios of stimulations and show how our platform helps to detect associated neural dynamic patterns within seconds through few interactions. In addition, we show how the platform could provide novel hypotheses for scenarios that were not yet explored empirically. By implementing the platform with flexibility for changes in connectivity and dynamic models, this work sets forth a generic methodology applicable to various neurobiological systems.

neuroscience↗

bioRxiv2017

Functional Connectomics from Data: Probabilistic Graphical Models for Neuronal Network of C. elegans

We propose a data-driven approach to represent neuronal network dynamics as a Probabilistic Graphical Model (PGM). Our approach learns the PGM structure by employing dimension reduction to network response dynamics evoked by stimuli applied to each neuron separately. The outcome model captures how stimuli propagate through the network and thus represents functional dependencies between neurons, i.e., functional connectome. The benefit of using a PGM as the functional connectome is that posterior inference can be done efficiently and circumvent the complexities in direct inference of response pathways in dynamic neuronal networks. In particular, posterior inference reveals the relations between known stimuli and downstream neurons or allows to query which stimuli are associated with downstream neurons. For validation and as an example for our approach we apply our methodology to a model of Caenorhabiditis elegans nervous system which structure and dynamics are well-studied. From its dynamical model we collect time series of the network response and use singular value decomposition to obtain a low-dimensional projection of the time series data. We then extract dominant patterns in each data matrix to get pairwise dependency information and create a graphical model for the full somatic nervous system. The PGM enables us to obtain and verify underlying neuronal pathways dominant for known behavioral scenarios and to detect possible pathways for novel scenarios.

bioinformatics↗