Demixed principal component analysis of neural population data

Kobak, Dmitry; Brendel, Wieland; Constantinidis, Christos; Feierstein, Claudia E.; Kepecs, Ádám; Mainen, Zachary F.; Qi, Xue Lian; Romo, Ranulfo; Uchida, Naoshige; Machens, Christian K.

doi:10.7554/elife.10989

Cited by 464 publications

(727 citation statements)

References 47 publications

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“…In this view, the time-dependent activity of individual neurons is simply a reflection of the latent variables (Fig. 1B) (Kaufman et al, 2016; Kobak et al, 2016; Macke et al, 2011). Consider the neural space in Fig.…”

Section: From Single Neurons To Neural Manifoldsmentioning

confidence: 99%

“…A number of studies have shown that the largely heterogeneous activity patterns of individual neurons in monkey (Kobak et al, 2016; Machens et al, 2010; Mante et al, 2013; Markowitz et al, 2015) and rat (Durstewitz et al, 2010) prefrontal cortex, monkey (Churchland et al, 2010b) and rat (Forsberg et al, 2016) V1, rat olfactory cortex (Kobak et al, 2016), rat thalamus (Chapin and Nicolelis, 1999), rat parietal cortex (Raposo et al, 2014), locust olfactory system (Stopfer et al, 2003), aplysia pedal ganglion (Bruno et al, 2015), and perhaps the entire zebrafish brain (Ahrens et al, 2012) can be explained as generated by a small set of latent variables associated with neural modes. In all these studies, neural modes and their time-varying activation helped describe previously unexplained mechanisms of neural function.…”

Section: Neural Manifolds In Non-motor Brain Corticesmentioning

confidence: 99%

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Neural Manifolds for the Control of Movement

Gallego

Perich

Miller

et al. 2017

Neuron

465

499

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The analysis of neural dynamics in several brain cortices has consistently uncovered low-dimensional manifolds that capture a significant fraction of neural variability. These neural manifolds are spanned by specific patterns of correlated neural activity, the “neural modes.” We discuss a model for neural control of movement in which the timedependent activation of these neural modes is the generator of motor behavior. This manifold-based view of motor cortex may lead to a better understanding of how the brain controls movement.

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Section: From Single Neurons To Neural Manifoldsmentioning

confidence: 99%

Section: Neural Manifolds In Non-motor Brain Corticesmentioning

confidence: 99%

Neural Manifolds for the Control of Movement

Gallego

Perich

Miller

et al. 2017

Neuron

465

499

View full text Add to dashboard Cite

show abstract

“…Briefly, for each of the single-session models and the multi-session model, we performed demixing principal components analysis [20] on the factor outputs. We then projected the factors along the highest-variance, condition-independent mode, and normalized the projection to a range of 0 to 1.…”

Section: Fig 5 -Kinematic Predictions Of Lfads Multi-session and Sinmentioning

confidence: 99%

“…( 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.…”

mentioning

confidence: 99%

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

Pandarinath

O’Shea

Collins

et al. 2017

Preprint

193

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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“…The ‘factor loadings’, that is each factor’s influence of neural responses, corresponds to an ‘axis’ recovered in the conditional approach above, and can in turn be used to infer estimates of the corresponding internal variable over time [4 •• ], or on a trial-by-trial basis [5 •• ]. ‘Demixed PCA’ [19] presents a promising mixed approach, incorporating hypotheses about multiple internal variables into jointly fitting p( r , I ). Knowledge of the inferred variables’ values and their corresponding ‘tuning curves’ may allow a functional interpretation of them, for example as the influence of anesthesia [4 •• ], attentional state [8 •• , 20, 21, 22], motor activity [23], or in terms of their sensory representation, e.g.…”

Section: Inferring Internal Variables and Their Influence On Neuralmentioning

confidence: 99%

Characterizing and interpreting the influence of internal variables on sensory activity

Lange

Haefner

2017

Current Opinion in Neurobiology

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The concept of a tuning curve has been central for our understanding of how the responses of cortical neurons depend on external stimuli. Here, we describe how the influence of unobserved internal variables on sensory responses, in particular correlated neural variability, can be understood in a similar framework. We suggest that this will lead to deeper insights into the relationship between stimulus, sensory responses, and behavior. We review related recent work and discuss its implication for distinguishing feedforward from feedback influences on sensory responses, and for the information contained in those responses.

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Demixed principal component analysis of neural population data

Cited by 464 publications

References 47 publications

Neural Manifolds for the Control of Movement

Neural Manifolds for the Control of Movement

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

Characterizing and interpreting the influence of internal variables on sensory activity

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