scholarly journals Predicting natural behavior from whole-brain neural dynamics

2018 ◽  
Author(s):  
Monika Scholz ◽  
Ashley N Linder ◽  
Francesco Randi ◽  
Anuj K Sharma ◽  
Xinwei Yu ◽  
...  
Keyword(s):  
Linear Combination ◽  
Linear Model ◽  
Neural Activity ◽  
Single Neuron ◽  
Neural Dynamics ◽  
Fictive Locomotion ◽  
Natural Behavior ◽  
C Elegans ◽  
Predicting Behavior ◽  
Freely Moving

AbstractWe record calcium activity from the majority of head neurons in freely moving C. elegans to reveal where and how natural behavior is encoded in a compact brain. We find that a sparse subset of neurons distributed throughout the head encode locomotion. A linear combination of these neurons’ activity predicts the animal’s velocity and body curvature and is sufficient to infer its posture. This sparse linear model outperforms single neuron or PCA models at predicting behavior. Among neurons important for the prediction are well-known locomotory neurons, such as AVA, as well as neurons not traditionally associated with locomotion. We compare neural activity of the same animal during unrestrained movement and during immobilization and find large differences between brain-wide neural dynamics during real and fictive locomotion.One Sentence SummaryC. elegans behavior is predicted from neural activity.

scholarly journals Decoding locomotion from population neural activity in moving C. elegans

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Kelsey M Hallinen ◽  
Ross Dempsey ◽  
Monika Scholz ◽  
Xinwei Yu ◽  
Ashley Linder ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Neural Activity ◽  
Single Neuron ◽  
Neural Representation ◽  
Correlation Structure ◽  
Experimental Measurements ◽  
Population Activity ◽  
Calcium Activity ◽  
C Elegans

We investigated the neural representation of locomotion in the nematode C. elegans by recording population calcium activity during movement. We report that population activity more accurately decodes locomotion than any single neuron. Relevant signals are distributed across neurons with diverse tunings to locomotion. Two largely distinct subpopulations are informative for decoding velocity and curvature, and different neurons’ activities contribute features relevant for different aspects of a behavior or different instances of a behavioral motif. To validate our measurements, we labeled neurons AVAL and AVAR and found that their activity exhibited expected transients during backward locomotion. Finally, we compared population activity during movement and immobilization. Immobilization alters the correlation structure of neural activity and its dynamics. Some neurons positively correlated with AVA during movement become negatively correlated during immobilization and vice versa. This work provides needed experimental measurements that inform and constrain ongoing efforts to understand population dynamics underlying locomotion in C. elegans.

scholarly journals Simultaneous optogenetic manipulation and calcium imaging in freely movingC. elegans

2013 ◽  
Author(s):  
Frederick B. Shipley ◽  
Christopher M. Clark ◽  
Mark J. Alkema ◽  
Andrew M. Leifer
Keyword(s):  
Neural Activity ◽  
Calcium Imaging ◽  
Neural Circuits ◽  
Neural Response ◽  
Sensory Stimuli ◽  
Systems Neuroscience ◽  
C Elegans ◽  
Fundamental Goal ◽  
Freely Moving ◽  
Interneuron Activity

A fundamental goal of systems neuroscience is to probe the dynamics of neural activity that drive behavior. Here we present an instrument to simultaneously manipulate neural activity via Channelrhodopsin, monitor neural response via GCaMP3, and observes behavior in freely moving C. elegans. We use the instrument to directly observe the relation between sensory stimuli, interneuron activity and locomotion in the mechanosensory circuit. Now published as: Front Neural Circuits 8:28, doi:10.3389/fncir.2014.00028

scholarly journals Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement

2021 ◽  
Author(s):  
Catalin Mitelut ◽  
Yongxu Zhang ◽  
Yuki Sekino ◽  
Jamie Boyd ◽  
Federico Bolanos ◽  
...  
Keyword(s):  
Neural Activity ◽  
Subjective Experience ◽  
Random Processes ◽  
Neural Mechanisms ◽  
Human Studies ◽  
Voluntary Action ◽  
Neural Dynamics ◽  
Behavior Prediction ◽  
Natural Behavior ◽  
Changes Over Time

Volition - the sense of control or agency over one's voluntary actions - is widely recognized as the basis of both human subjective experience and natural behavior in non-human animals. To date, several human studies have found peaks in neural activity preceding voluntary actions, e.g. the readiness potential (RP), and some have shown upcoming actions could be decoded even before awareness. These findings remain controversial with some suggesting they pose a challenge to traditional accounts of human volition while others proposing that random processes underlie pre-movement neural activity. Here we seek to address part of this controversy by evaluating whether pre-movement neural activity in mice contains structure beyond that expected from random processes. Implementing a self-initiated water-rewarded lever pull paradigm in mice while recording widefield [Ca++] neural activity we find that cortical activity changes in variance seconds prior to movement and that upcoming lever pulls or spontaneous body movements could be predicted between 1 second to more than 10 seconds prior to movement, similar to but even earlier than in human studies. We show that mice, like humans, are biased towards initiation of voluntary actions during specific phases of neural activity oscillations but that the pre-movement neural code in mice changes over time and is widely distributed as behavior prediction improved when using all vs single cortical areas. These findings support the presence of structured multi-second neural dynamics preceding voluntary action beyond that expected from random processes. Our results also suggest that neural mechanisms underlying self-initiated voluntary action could be preserved between mice and humans.

scholarly journals Automated long-term recording and analysis of neural activity in behaving animals

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Ashesh K Dhawale ◽  
Rajesh Poddar ◽  
Steffen BE Wolff ◽  
Valentin A Normand ◽  
Evi Kopelowitz ◽  
...  
Keyword(s):  
Neural Activity ◽  
Neural Circuits ◽  
Large Datasets ◽  
Neural Dynamics ◽  
Single Unit Activity ◽  
Firing Rates ◽  
Freely Moving ◽  
Automated Platform ◽  
And Behavior

Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons in experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics across different behavioral states. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving rodents. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals.

scholarly journals Where is all the nonlinearity: flexible nonlinear modeling of behaviorally relevant neural dynamics using recurrent neural networks

2021 ◽  
Author(s):  
Omid G Sani ◽  
Bijan Pesaran ◽  
Maryam M Shanechi
Keyword(s):  
Neural Networks ◽  
Dynamic Modeling ◽  
Neural Activity ◽  
Recurrent Neural Networks ◽  
Nonlinear Modeling ◽  
Brain Regions ◽  
Neural Dynamics ◽  
Predicting Behavior ◽  
Nonlinear Learning ◽  
Nonlinear Dynamic Modeling

Understanding the dynamical transformation of neural activity to behavior requires modeling this transformation while both dissecting its potential nonlinearities and dissociating and preserving its nonlinear behaviorally relevant neural dynamics, which remain unaddressed. We present RNN PSID, a nonlinear dynamic modeling method that enables flexible dissection of nonlinearities, dissociation and preferential learning of neural dynamics relevant to specific behaviors, and causal decoding. We first validate RNN PSID in simulations and then use it to investigate nonlinearities in monkey spiking and LFP activity across four tasks and different brain regions. Nonlinear RNN PSID successfully dissociated and preserved nonlinear behaviorally relevant dynamics, thus outperforming linear and non-preferential nonlinear learning methods in behavior decoding while reaching similar neural prediction. Strikingly, dissecting the nonlinearities with RNN PSID revealed that consistently across all tasks, summarizing the nonlinearity only in the mapping from the latent dynamics to behavior was largely sufficient for predicting behavior and neural activity. RNN PSID provides a novel tool to reveal new characteristics of nonlinear neural dynamics underlying behavior.

scholarly journals Automated long-term recording and analysis of neural activity in behaving animals

2015 ◽  
Author(s):  
Ashesh K. Dhawale ◽  
Rajesh Poddar ◽  
Evi Kopelowitz ◽  
Valentin Normand ◽  
Steffen B. E. Wolff ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Unit Activity ◽  
Neural Activity ◽  
Single Unit ◽  
Single Units ◽  
Neural Dynamics ◽  
Single Unit Activity ◽  
Freely Moving ◽  
And Behavior

SummaryAddressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons during experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics across different behavioral states. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving animals. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals.HighlightsWe record neural activity and behavior in rodents continuously (24/7) over monthsAn automated spike-sorting method isolates and tracks single units over many weeksNeural dynamics and motor representations are highly stable over long timescalesNeurons cluster into functional groups based on their activity in different stateseTOC BlurbDhawale et al. describe experimental infrastructure for recording neural activity and behavior continuously over months in freely moving rodents. A fully automated spike-sorting algorithm allows single units to be tracked over weeks of recording. Recordings from motor cortex and striatum revealed a remarkable long-term stability in both single unit activity and network dynamics.

scholarly journals Can binocular rivalry reveal neural correlates of consciousness?

2014 ◽  
Vol 369 (1641) ◽  
pp. 20130211 ◽  
Author(s):  
Randolph Blake ◽  
Jan Brascamp ◽  
David J. Heeger
Keyword(s):  
Decision Making ◽  
Binocular Rivalry ◽  
Neural Activity ◽  
Neural Correlates ◽  
Causal Link ◽  
Neural Dynamics ◽  
Perceptual Decision Making ◽  
Neural Correlate ◽  
Neural Correlates Of Consciousness ◽  
Neural Correlate Of Consciousness

This essay critically examines the extent to which binocular rivalry can provide important clues about the neural correlates of conscious visual perception. Our ideas are presented within the framework of four questions about the use of rivalry for this purpose: (i) what constitutes an adequate comparison condition for gauging rivalry's impact on awareness, (ii) how can one distinguish abolished awareness from inattention, (iii) when one obtains unequivocal evidence for a causal link between a fluctuating measure of neural activity and fluctuating perceptual states during rivalry, will it generalize to other stimulus conditions and perceptual phenomena and (iv) does such evidence necessarily indicate that this neural activity constitutes a neural correlate of consciousness? While arriving at sceptical answers to these four questions, the essay nonetheless offers some ideas about how a more nuanced utilization of binocular rivalry may still provide fundamental insights about neural dynamics, and glimpses of at least some of the ingredients comprising neural correlates of consciousness, including those involved in perceptual decision-making.

scholarly journals Omitted Variable Bias in GLMs of Neural Spiking Activity

2018 ◽  
Vol 30 (12) ◽  
pp. 3227-3258 ◽  
Author(s):  
Ian H. Stevenson
Keyword(s):  
Neural Activity ◽  
Single Neuron ◽  
Linear Models ◽  
Theory And Practice ◽  
Parameter Estimates ◽  
Omitted Variables ◽  
History Effects ◽  
Omitted Variable Bias ◽  
Wide Range ◽  
Variable Bias

Generalized linear models (GLMs) have a wide range of applications in systems neuroscience describing the encoding of stimulus and behavioral variables, as well as the dynamics of single neurons. However, in any given experiment, many variables that have an impact on neural activity are not observed or not modeled. Here we demonstrate, in both theory and practice, how these omitted variables can result in biased parameter estimates for the effects that are included. In three case studies, we estimate tuning functions for common experiments in motor cortex, hippocampus, and visual cortex. We find that including traditionally omitted variables changes estimates of the original parameters and that modulation originally attributed to one variable is reduced after new variables are included. In GLMs describing single-neuron dynamics, we then demonstrate how postspike history effects can also be biased by omitted variables. Here we find that omitted variable bias can lead to mistaken conclusions about the stability of single-neuron firing. Omitted variable bias can appear in any model with confounders—where omitted variables modulate neural activity and the effects of the omitted variables covary with the included effects. Understanding how and to what extent omitted variable bias affects parameter estimates is likely to be important for interpreting the parameters and predictions of many neural encoding models.

scholarly journals Assessment of Brain Functional Activity Using a Miniaturized Head-Mounted Scanning Photoacoustic Imaging System in Awake and Freely Moving Rats

Biosensors ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 429
Author(s):  
Yuhling Wang ◽  
Tsung-Sheng Chu ◽  
Yan-Ren Lin ◽  
Chia-Hui Tsao ◽  
Chia-Hua Tsai ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Photoacoustic Imaging ◽  
T Test ◽  
Natural Behavior ◽  
Freely Moving Rats ◽  
Anesthetized Rats ◽  
Freely Moving ◽  
Paired T Test ◽  
Awake Rats

Understanding the relationship between brain function and natural behavior remains a significant challenge in neuroscience because there are very few convincing imaging/recording tools available for the evaluation of awake and freely moving animals. Here, we employed a miniaturized head-mounted scanning photoacoustic imaging (hmPAI) system to image real-time cortical dynamics. A compact photoacoustic (PA) probe based on four in-house optical fiber pads and a single custom-made 48-MHz focused ultrasound transducer was designed to enable focused dark-field PA imaging, and miniature linear motors were included to enable two-dimensional (2D) scanning. The total dimensions and weight of the proposed hmPAI system are only approximately 50 × 64 × 48 mm and 58.7 g (excluding cables). Our ex vivo phantom experimental tests revealed that a spatial resolution of approximately 0.225 mm could be achieved at a depth of 9 mm. Our in vivo results further revealed that the diameters of cortical vessels draining into the superior sagittal sinus (SSS) could be clearly imaged and continuously observed in both anesthetized rats and awake, freely moving rats. Statistical analysis showed that the full width at half maximum (FWHM) of the PA A-line signals (relative to the blood vessel diameter) was significantly increased in the selected SSS-drained cortical vessels of awake rats (0.58 ± 0.17 mm) compared with those of anesthetized rats (0.31 ± 0.09 mm) (p < 0.01, paired t-test). In addition, the number of pixels in PA B-scan images (relative to the cerebral blood volume (CBV)) was also significantly increased in the selected SSS-drained blood vessels of awake rats (107.66 ± 23.02 pixels) compared with those of anesthetized rats (81.99 ± 21.52 pixels) (p < 0.01, paired t-test). This outcome may result from a more active brain in awake rats than in anesthetized rats, which caused cerebral blood vessels to transport more blood to meet the increased nutrient demand of the tissue, resulting in an obvious increase in blood vessel volume. This hmPAI system was further validated for utility in the brains of awake and freely moving rats, showing that their natural behavior was unimpaired during vascular imaging, thereby providing novel opportunities for studies of behavior, cognition, and preclinical models of brain diseases.

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