Neural Activity Measures and Their Dynamics

Shlizerman, Eli; Schröder, Konrad; Kutz, J. Nathan

doi:10.1137/110843630

Cited by 18 publications

(22 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, when the SVD-based approach is applied to multi-node time-series network dynamics, it decomposes the data into spatial modes and their associated time-dependent coefficients. The spatial modes are orthogonal vectors representing activity patterns of the nodes [23]. When the recorded data have been conditioned on a particular event, each spatial mode vector is effectively a vector containing a response score for each node.…”

Section: (C) Dimension Reductionmentioning

confidence: 99%

Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network ofCaenorhabditis elegans

Liu

Kim

Shlizerman

2018

Phil. Trans. R. Soc. B

Self Cite

View full text Add to dashboard Cite

We propose an approach to represent neuronal network dynamics as a probabilistic graphical model (PGM). To construct the PGM, we collect time series of neuronal responses produced by the neuronal network and use singular value decomposition to obtain a low-dimensional projection of the time-series data. We then extract dominant patterns from the projections to get pairwise dependency information and create a graphical model for the full network. The outcome model is a functional connectome that captures how stimuli propagate through the network and thus represents causal dependencies between neurons and stimuli. We apply our methodology to a model of the somatic nervous system to validate and show an example of our approach. The structure and dynamics of the nervous system are well studied and a model that generates neuronal responses is available. The resulting PGM enables us to obtain and verify underlying neuronal pathways for known behavioural scenarios and detect possible pathways for novel scenarios.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling at cellular resolution'.

show abstract

Section: (C) Dimension Reductionmentioning

confidence: 99%

Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network ofCaenorhabditis elegans

Liu

Kim

Shlizerman

2018

Phil. Trans. R. Soc. B

Self Cite

View full text Add to dashboard Cite

show abstract

“…Next we analyzed voltage responses using Singular Value Decomposition (SVD) to elucidate their characteristics. The SVD method decomposes the responses into spatial neuronal population modes (PC modes) and their temporal coefficients (23,38,43). We first apply SVD to understand the representation of each individual movement, in particular, we determine (i) the number of spatial modes needed to represent each activity and (ii) what is the complexity of the trajectories of the coefficients.…”

Section: Resultsmentioning

confidence: 99%

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

Kim

Santos

Alkema

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

The ability to fully discern how the brain orchestrates behavior requires the development of successful computational approaches to integrate and inform in-vivo investigations of the nervous system. To effectively assist with such investigations, computational approaches must be generic, scalable and unbiased. We propose such a comprehensive framework to investigate the interaction between the nervous system and the body for the nematode Caenorhabditis elegans (C. elegans). Specifically, we introduce a model that computationally emulates the activity of the complete somatic nervous system and its response to stimuli. The model builds upon the full anatomical wiring diagram, the connectome, and integrates it with additional layers including intra-cellular and extra-cellular bio-physically relevant neural dynamics, layers translating neural activity to muscle forces and muscle impulses to body postures. In addition, it implements inverse integration which modulates neural dynamics according to external forces on the body. We validate the model by in-silico injection of currents into sensory-and interneurons known to play a role in locomotion behaviors (e.g. posterior/anterior touch) and by applying external forces on the body. We are able to generate characteristic baseline locomotion behaviors (forward and backward movements). Inclusion of proprioceptive feedback, implemented through inverse integration, shows that feedback can entrain and sustain movements initiated by neural or mechanical triggers. We further apply neural stimuli, experimentally known to modulate locomotion, and show that our model supports natural behavioral responses such as turns, reversals and avoidance. The proposed model can be utilized to infer neural circuits involved in sensorimotor behavior. For this purpose, we develop large-scale computational ablation approaches such as (i) ablation survey and (ii) conditional ablation. Our results show how an ablation survey can identify neurons required for a ventral turning behavior. We also show how conditional ablation can identify alternative novel neural pathways, e.g. propose neurons which facilitate steering behavior towards olfactory attractants.The outcomes of our study show that the framework can be utilized to identify neural circuits, which control, mediate and generate natural behavior.

show abstract

“…The external input current is given by . Note that this is, essentially, a fairly standard single-compartment membrane equation [49], and its governing equations are formally identical to that used by Wicks et al [48] except for our use of the full somatic connectome, our simplifying parameter assumptions, and minor differences in the treatment of synaptic dynamics taken from [50]. …”

Section: Methodsmentioning

confidence: 99%

“…This form of sigmoidal activation is taken from [50]. Note that it can be shown (by setting ) that, as in [48], the equilibrium value of s i depends sigmoidally upon V i .…”

Section: Methodsmentioning

confidence: 99%

Functionality and Robustness of Injured Connectomic Dynamics in C. elegans: Linking Behavioral Deficits to Neural Circuit Damage

2017

Self Cite

View full text Add to dashboard Cite

Using a model for the dynamics of the full somatic nervous system of the nematode C. elegans, we address how biological network architectures and their functionality are degraded in the presence of focal axonal swellings (FAS) arising from neurodegenerative disease and/or traumatic brain injury. Using biophysically measured FAS distributions and swelling sizes, we are able to simulate the effects of injuries on the neural dynamics of C. elegans, showing how damaging the network degrades its low-dimensional dynamical responses. We visualize these injured neural dynamics by mapping them onto the worm’s low-dimensional postures, i.e. eigenworm modes. We show that a diversity of functional deficits arise from the same level of injury on a connectomic network. Functional deficits are quantified using a statistical shape analysis, a procrustes analysis, for deformations of the limit cycles that characterize key behaviors such as forward crawling. This procrustes metric carries information on the functional outcome of injuries in the model. Furthermore, we apply classification trees to relate injury structure to the behavioral outcome. This makes testable predictions for the structure of an injury given a defined functional deficit. More critically, this study demonstrates the potential role of computational simulation studies in understanding how neuronal networks process biological signals, and how this processing is impacted by network injury.

show abstract

Neural Activity Measures and Their Dynamics

Cited by 18 publications

References 63 publications

Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network ofCaenorhabditis elegans

Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network ofCaenorhabditis elegans

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

Functionality and Robustness of Injured Connectomic Dynamics in C. elegans: Linking Behavioral Deficits to Neural Circuit Damage

Contact Info

Product

Resources

About