scholarly journals Hierarchical recurrent state space models reveal discrete and continuous dynamics of neural activity in C. elegans

2019 ◽  
Author(s):  
Scott Linderman ◽  
Annika Nichols ◽  
David Blei ◽  
Manuel Zimmer ◽  
Liam Paninski
Keyword(s):  
State Space ◽  
Neural Activity ◽  
Large Scale ◽  
Motor Behavior ◽  
Transition Probabilities ◽  
Sensory Information ◽  
Individual Variability ◽  
State Space Models ◽  
Model Organisms ◽  
C Elegans

AbstractModern recording techniques enable large-scale measurements of neural activity in a variety of model organisms. The dynamics of neural activity shed light on how organisms process sensory information and generate motor behavior. Here, we study these dynamics using optical recordings of neural activity in the nematode C. elegans. To understand these data, we develop state space models that decompose neural time-series into segments with simple, linear dynamics. We incorporate these models into a hierarchical framework that combines partial recordings from many worms to learn shared structure, while still allowing for individual variability. This framework reveals latent states of population neural activity, along with the discrete behavioral states that govern dynamics in this state space. We find stochastic transition patterns between discrete states and see that transition probabilities are determined by both current brain activity and sensory cues. Our methods automatically recover transition times that closely match manual labels of different behaviors, such as forward crawling, reversals, and turns. Finally, the resulting model can simulate neural data, faithfully capturing salient patterns of whole brain dynamics seen in real data.

scholarly journals Neural Field Models for Latent State Inference: Application to Large-Scale Neuronal Recordings

2019 ◽  
Author(s):  
M. E. Rule ◽  
D. Schnoerr ◽  
M. H. Hennig ◽  
G. Sanguinetti
Keyword(s):  
State Space ◽  
Point Process ◽  
Neural Activity ◽  
Large Scale ◽  
Single Neuron ◽  
Neural Field ◽  
State Space Models ◽  
Neuron Activity ◽  
Single Neuron Activity ◽  
Neural Field Models

AbstractLarge-scale neural recordings are becoming increasingly better at providing a window into functional neural networks in the living organism. Interpreting such rich data sets, however, poses fundamental statistical challenges. The neural field models of Wilson, Cowan and colleagues remain the mainstay of mathematical population modeling owing to their interpretable, mechanistic parameters and amenability to mathematical analysis. We developed a method based on moment closure to interpret neural field models as latent state-space point-process models, making mean field models amenable to statistical inference. We demonstrate that this approach can infer latent neural states, such as active and refractory neurons, in large populations. After validating this approach with synthetic data, we apply it to high-density recordings of spiking activity in the developing mouse retina. This confirms the essential role of a long lasting refractory state in shaping spatio-temporal properties of neonatal retinal waves. This conceptual and methodological advance opens up new theoretical connections between mathematical theory and point-process state-space models in neural data analysis.SignificanceDeveloping statistical tools to connect single-neuron activity to emergent collective dynamics is vital for building interpretable models of neural activity. Neural field models relate single-neuron activity to emergent collective dynamics in neural populations, but integrating them with data remains challenging. Recently, latent state-space models have emerged as a powerful tool for constructing phenomenological models of neural population activity. The advent of high-density multi-electrode array recordings now enables us to examine large-scale collective neural activity. We show that classical neural field approaches can yield latent statespace equations and demonstrate inference for a neural field model of excitatory spatiotemporal waves that emerge in the developing retina.

scholarly journals Efficient Forced Response Computations of Acoustical Systems with a State-Space Approach

Acoustics ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 581-594
Author(s):  
Art J. R. Pelling ◽  
Ennes Sarradj
Keyword(s):  
State Space ◽  
Large Scale ◽  
State Space Model ◽  
Measurement Data ◽  
Numerical Linear Algebra ◽  
Forced Response ◽  
State Space Models ◽  
High Dimensional ◽  
Diagonal Form ◽  
The Past

State-space models have been successfully employed for model order reduction and control purposes in acoustics in the past. However, due to the cubic complexity of the singular value decomposition, which makes up the core of many subspace system identification (SSID) methods, the construction of large scale state-space models from high-dimensional measurement data has been problematic in the past. Recent advances of numerical linear algebra have brought forth computationally efficient randomized rank-revealing matrix factorizations and it has been shown that these factorizations can be used to enhance SSID methods such as the Eigensystem Realization Algorithm (ERA). In this paper, we demonstrate the applicability of the so-called generalized ERA to acoustical systems and high-dimensional input data by means of an example. Furthermore, we introduce a new efficient method of forced response computation that relies on a state-space model in quasi-diagonal form. Numerical experiments reveal that our proposed method is more efficient than previous state-space methods and can even outperform frequency domain convolutions in certain scenarios.

scholarly journals A robotic system for high-throughput automated lifespan and phenotyping analysis in C. elegans

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 886-886
Author(s):  
Elena Vayndorf ◽  
Jason Pitt ◽  
Judy Wu ◽  
Emily Chang ◽  
Richard Nguyen ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Nematode Species ◽  
Robotic System ◽  
Model Organisms ◽  
Biological Aging ◽  
Digital Cameras ◽  
C Elegans ◽  
Age Related ◽  
3D Printers

Abstract A goal of gerontology-related research is to develop therapies to improve the healthy period of life by understanding and targeting the molecular hallmarks of biological aging. Much progress has been made toward understanding the genetic and biochemical nature of these hallmarks through studies using simple invertebrate model organisms, such as the nematode Caenorhabditis elegans. Over the past decade, the identification of potential genetic and pharmacological modifiers of lifespan and age-related pathologies in C. elegans and other model organisms has yielded fruitful leads for follow-up investigation. However, such studies are typically time- consuming and labor-intensive. The goal of our work is to automate tasks that require frequent, repeated observations and hours of manual labor to collect and analyze lifespan, motility, and other behavioral data in C. elegans and other nematode models. The advent of affordable high-quality digital cameras, robotics systems, and 3D printers, as well as the decreasing financial and computational costs of image storage and processing, have allowed us to automate data capture and analysis on a large scale. To this end, our group recently developed a tool, we call the WormBot, consisting of an unbiased, high-throughput, automated robotic system and corresponding software, to perform genetic and pharmacological quantification of lifespan and health measures in C. elegans and related nematode species. We will report updates recently made to this system, including significant improvements to hardware, and present screening results from proteasome stimulator drugs known to reduce the accumulation of proteotoxic proteins linked to neurodegenerative diseases and aging.

scholarly journals Exploratory Assessment of the Limiting Extended Kalman Filter Properties

2013 ◽  
Vol 14 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Constantinos Antoniou ◽  
Alexandra Kondyli ◽  
Georgia-Maria Lykogianni ◽  
Elias Gyftodimos
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
Extended Kalman Filter ◽  
Large Scale ◽  
State Space Models ◽  
Dynamic State ◽  
Gain Matrix ◽  
First Case ◽  
Kalman Gain

Abstract Most of the methodologies for the solution of state-space models are based on the Kalman Filter algorithm (Kalman, 1960), developed for the solution of linear, dynamic state-space models. The most straightforward extension to nonlinear systems is the Extended Kalman Filter (EKF). The Limiting EKF (LimEKF) is a new algorithm that obviates the need to compute the Kalman gain matrix on-line, as it can be calculated off-line from pre-computed gain matrices. In this research, several different strategies for the construction of the gain matrices are presented: e.g. average of previously computed matrices per interval per demand level and average of previously computed matrices per interval independent of demand level. Two case studies are presented to investigate the performance of the LimEKF under the different assumptions. In the first case study, a detailed experimental design was developed and a large number of simulation runs was performed in a synthetic network. The results suggest that indeed the LimEKF algorithm is robust and - while not requiring the explicit computation of the Kalman gain matrix, and thus having vastly superior computational properties - its accuracy is close to that of the “exact” EKF. In the second case study, a smaller number of scenarios is evaluated using a real-world, large-scale network in Stockholm, Sweden, with similarly encouraging results. Taking the average of various pre-computed Kalman Gain matrices possibly reduces the noise that creeps into the computation of the individual Kalman gain matrices, and this may be one of the key reasons for the good performance of the LimEKF (i.e. increased robustness).

Parsimony inducing priors for large scale state–space models

2021 ◽  
Author(s):  
Hedibert F. Lopes ◽  
Robert E. McCulloch ◽  
Ruey S. Tsay
Keyword(s):  
State Space ◽  
Large Scale ◽  
State Space Models

scholarly journals Thermoresponsive motor behavior is mediated by ring neuron circuits in the central complex of Drosophila

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Edgar Buhl ◽  
Benjamin Kottler ◽  
James J. L. Hodge ◽  
Frank Hirth
Keyword(s):  
Neural Activity ◽  
Motor Behavior ◽  
Calcium Influx ◽  
Sensory Information ◽  
Central Complex ◽  
Temperature Sensitive ◽  
External Temperature ◽  
Ellipsoid Body ◽  
Survival And Reproduction ◽  
Transsynaptic Tracing

AbstractInsects are ectothermal animals that are constrained in their survival and reproduction by external temperature fluctuations which require either active avoidance of or movement towards a given heat source. In Drosophila, different thermoreceptors and neurons have been identified that mediate temperature sensation to maintain the animal’s thermal preference. However, less is known how thermosensory information is integrated to gate thermoresponsive motor behavior. Here we use transsynaptic tracing together with calcium imaging, electrophysiology and thermogenetic manipulations in freely moving Drosophila exposed to elevated temperature and identify different functions of ellipsoid body ring neurons, R1-R4, in thermoresponsive motor behavior. Our results show that warming of the external surroundings elicits calcium influx specifically in R2-R4 but not in R1, which evokes threshold-dependent neural activity in the outer layer ring neurons. In contrast to R2, R3 and R4d neurons, thermogenetic inactivation of R4m and R1 neurons expressing the temperature-sensitive mutant allele of dynamin, shibireTS, results in impaired thermoresponsive motor behavior at elevated 31 °C. trans-Tango mediated transsynaptic tracing together with physiological and behavioral analyses indicate that integrated sensory information of warming is registered by neural activity of R4m as input layer of the ellipsoid body ring neuropil and relayed on to R1 output neurons that gate an adaptive motor response. Together these findings imply that segregated activities of central complex ring neurons mediate sensory-motor transformation of external temperature changes and gate thermoresponsive motor behavior in Drosophila.

The glycomes of Caenorhabditis elegans and other model organisms

2002 ◽  
Vol 69 ◽  
pp. 117-134 ◽  
Author(s):  
Stuart M. Haslam ◽  
David Gems ◽  
Howard R. Morris ◽  
Anne Dell
Keyword(s):  
Caenorhabditis Elegans ◽  
Current Knowledge ◽  
Structural Data ◽  
Model Organisms ◽  
Entire Genome ◽  
C Elegans ◽  
The Face ◽  
Area Of Concern ◽  
Nematode Worm

There is no doubt that the immense amount of information that is being generated by the initial sequencing and secondary interrogation of various genomes will change the face of glycobiological research. However, a major area of concern is that detailed structural knowledge of the ultimate products of genes that are identified as being involved in glycoconjugate biosynthesis is still limited. This is illustrated clearly by the nematode worm Caenorhabditis elegans, which was the first multicellular organism to have its entire genome sequenced. To date, only limited structural data on the glycosylated molecules of this organism have been reported. Our laboratory is addressing this problem by performing detailed MS structural characterization of the N-linked glycans of C. elegans; high-mannose structures dominate, with only minor amounts of complex-type structures. Novel, highly fucosylated truncated structures are also present which are difucosylated on the proximal N-acetylglucosamine of the chitobiose core as well as containing unusual Fucα1–2Gal1–2Man as peripheral structures. The implications of these results in terms of the identification of ligands for genomically predicted lectins and potential glycosyltransferases are discussed in this chapter. Current knowledge on the glycomes of other model organisms such as Dictyostelium discoideum, Saccharomyces cerevisiae and Drosophila melanogaster is also discussed briefly.

Parameter Estimation Using a Modified Version of SPSA Algorithm Applied to State Space Models

2009 ◽  
Vol 129 (12) ◽  
pp. 1187-1194 ◽  
Author(s):  
Jorge Ivan Medina Martinez ◽  
Kazushi Nakano ◽  
Kohji Higuchi
Keyword(s):  
Parameter Estimation ◽  
State Space ◽  
State Space Models
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