Global Brain Dynamics Embed the Motor Command Sequence of Caenorhabditis elegans

Kato, Saul; Kaplan, Harris S.; Schrödel, Tina; Skora, Susanne; Lindsay, Theodore H.; Yemini, Eviatar; Lockery, Shawn R.; Zimmer, Manuel

doi:10.1016/j.cell.2015.09.034

Cited by 515 publications

(942 citation statements)

References 46 publications

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“…LFADS is forced to find low-dimensional dynamics that explain the recorded data because the number of dynamic factors in the model is constrained. This is consistent with repeated empirical observations that the dimensionality of neural population activity in areas like motor and prefrontal cortices is, in several cases, much lower than the number of recorded neurons Kato et al 2015;) ; see (P. Gao and Ganguli 2015) for a full discussion].Her e we apply LFADS to a variety of datasets from r hesus macaque motor and pre-motor cortices using both chronic electrode arrays and acute linear array recordings as well as human All rights reserved. No reuse allowed without permission.…”

supporting

confidence: 76%

Inferring single-trial neural population dynamics using sequential auto-encoders

Pandarinath

O’Shea

Collins

et al. 2017

Preprint

196

411

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Neuroscience is experiencing a data revolution in which simultaneous recording of many hundreds or thousands of neurons is revealing structure in population activity that is not apparent from single-neuron responses. This structure is typically extracted from trial-averaged data. Single-trial analyses are challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. Here we introduce Latent Factor Analysis via Dynamical Systems (LFADS), a deep learning method to infer latent dynamics from single-trial neural spiking data. LFADS uses a nonlinear dynamical system (a recurrent neural network) to infer the dynamics underlying observed population activity and to extract 'de-noised' single-trial firing rates from neural spiking data. We apply LFADS to a variety of monkey and human motor cortical datasets, demonstrating its ability to predict observed behavioral variables with unprecedented accuracy, extract precise estimates of neural dynamics on single trials, infer perturbations to those dynamics that correlate with behavioral choices, and combine data from non-overlapping recording sessions (spanning months) to improve inference of underlying dynamics. In summary, LFADS leverages all observations of a neural population's activity to accurately model its dynamics on single trials, opening the door to a detailed understanding of the role of dynamics in performing computation and ultimately driving behavior.Increasing evidence suggests that in many brain areas, such as the motor and prefrontal cortices, the activity of large populations of neurons, termed the neural population state, is often well-described by low-dimensional dynamics [e.g. (Afshar et al. 2011; Harvey, Coen, and Tank 2012; Kaufman et al. 2014;Sadtler et al. 2014;Kobak et al. 2016a) ]. Recovering these dynamics on single trials is essential for illuminating the relationship between neural population activity and behavior, and for advancing therapeutic neurotechnologies such as closed-loop deep brain stimulation and brain-machine interfaces. However, recovering population dynamics All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/152884 doi: bioRxiv preprint first posted online Jun. 20, 2017; on single trials is difficult due to trial-to-trial variability (e.g. in behavior or arousal) and fluctuations in the spiking of individual neurons. Even with dramatic increases in the numbers of neurons that can be simultaneously recorded using multichannel electrode arrays or optical imaging, accurately recovering population dynamics from single trials remains a significant challenge for data-analysis methods.Standard analyses sacrifice single-trial information for the sake of better estimates of trial-averaged neural states (Ahrens et al. 2012; Kobak et al. 2016b) . Techniques for extrac...

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supporting

confidence: 76%

Inferring single-trial neural population dynamics using sequential auto-encoders

Pandarinath

O’Shea

Collins

et al. 2017

Preprint

196

411

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“…Action selection in nematodes is driven by shifts in global brain dynamics (119), and there is sufficient plasticity in the nematode nervous system for their responses to vary with system state (120). However, there is no evidence that nematodes can actively hunt for things beyond their immediate sensory environment.…”

Section: Beyond Insectsmentioning

confidence: 99%

What insects can tell us about the origins of consciousness

Barron

Klein

2016

Proc. Natl. Acad. Sci. U.S.A.

263

183

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How, why, and when consciousness evolved remain hotly debated topics. Addressing these issues requires considering the distribution of consciousness across the animal phylogenetic tree. Here we propose that at least one invertebrate clade, the insects, has a capacity for the most basic aspect of consciousness: subjective experience. In vertebrates the capacity for subjective experience is supported by integrated structures in the midbrain that create a neural simulation of the state of the mobile animal in space. This integrated and egocentric representation of the world from the animal's perspective is sufficient for subjective experience. Structures in the insect brain perform analogous functions. Therefore, we argue the insect brain also supports a capacity for subjective experience. In both vertebrates and insects this form of behavioral control system evolved as an efficient solution to basic problems of sensory reafference and true navigation. The brain structures that support subjective experience in vertebrates and insects are very different from each other, but in both cases they are basal to each clade. Hence we propose the origins of subjective experience can be traced to the Cambrian. subjective experience | primary consciousness | vertebrate midbrain | central complex

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“…High-speed light sheet microscopy, light field microscopy, multifocus microscopy, and two-photon structured illumination microscopy have proved effective for rapidly recording large numbers of neurons in immobilized, intact, transparent animals like larval zebrafish and nematodes (15)(16)(17)(18)(19). However, these methods are problematic when attempting to track many neurons within the bending and moving body of a behaving animal.…”

mentioning

confidence: 99%

Pan-neuronal imaging in roaming Caenorhabditis elegans

Venkatachalam

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

221

216

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We present an imaging system for pan-neuronal recording in crawling Caenorhabditis elegans. A spinning disk confocal microscope, modified for automated tracking of the C. elegans head ganglia, simultaneously records the activity and position of ∼80 neurons that coexpress cytoplasmic calcium indicator GCaMP6s and nuclear localized red fluorescent protein at 10 volumes per second. We developed a behavioral analysis algorithm that maps the movements of the head ganglia to the animal's posture and locomotion. Image registration and analysis software automatically assigns an index to each nucleus and calculates the corresponding calcium signal. Neurons with highly stereotyped positions can be associated with unique indexes and subsequently identified using an atlas of the worm nervous system. To test our system, we analyzed the brainwide activity patterns of moving worms subjected to thermosensory inputs. We demonstrate that our setup is able to uncover representations of sensory input and motor output of individual neurons from brainwide dynamics. Our imaging setup and analysis pipeline should facilitate mapping circuits for sensory to motor transformation in transparent behaving animals such as C. elegans and Drosophila larva.C. elegans | Drosophila | calcium imaging | thermotaxis U nderstanding how brain dynamics creates behaviors requires quantifying the flow and transformation of sensory information to motor output in behaving animals. Optical imaging using genetically encoded calcium or voltage fluorescent probes offers a minimally invasive method to record neural activity in intact animals. The nematode Caenorhabditis elegans is particularly ideal for optical neurophysiology owing to its small size, optical transparency, compact nervous system, and ease of genetic manipulation. Imaging systems for tracking the activity of small numbers of neurons have been effective in determining their role during nematode locomotion and navigational behaviors like chemotaxis, thermotaxis, and the escape response (1-6).Recordings from large numbers of interconnected neurons are required to understand how neuronal ensembles carry out the systematic transformations of sensory input into motor patterns that build behavioral decisions.Several methods for fast 3D imaging of neural activity in a fixed imaging volume have been developed for different model organisms (7)(8)(9)(10)(11)(12)(13)(14). High-speed light sheet microscopy, light field microscopy, multifocus microscopy, and two-photon structured illumination microscopy have proved effective for rapidly recording large numbers of neurons in immobilized, intact, transparent animals like larval zebrafish and nematodes (15)(16)(17)(18)(19). However, these methods are problematic when attempting to track many neurons within the bending and moving body of a behaving animal. Panneuronal recording in moving animals poses higher demands on spatial and temporal resolution. Furthermore, extracting neuronal signals from recordings in a behaving animal requires an effective analysis pipel...

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Global Brain Dynamics Embed the Motor Command Sequence of Caenorhabditis elegans

Cited by 515 publications

References 46 publications

Inferring single-trial neural population dynamics using sequential auto-encoders

Inferring single-trial neural population dynamics using sequential auto-encoders

What insects can tell us about the origins of consciousness

Pan-neuronal imaging in roaming Caenorhabditis elegans

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