Encoding of Naturalistic Optic Flow by a Population of Blowfly Motion-Sensitive Neurons

In sensory systems information is encoded by the activity of populations of neurons. To analyze the coding properties of neuronal populations sensory stimuli have usually been used that were much simpler than those encountered in real life. It has been possible only recently to stimulate visual interneurons of the blowfly with naturalistic visual stimuli reconstructed from eye movements measured during free flight. Therefore we now investigate with naturalistic optic flow the coding properties of a small neuronal population of identified visual interneurons in the blowfly, the so-called VS and HS neurons. These neurons are motion sensitive and directionally selective and are assumed to extract information about the animal's self-motion from optic flow. We could show that neuronal responses of VS and HS neurons are mainly shaped by the characteristic dynamical properties of the fly's saccadic flight and gaze strategy. Individual neurons encode information about both the rotational and the translational components of the animal's self-motion. Thus the information carried by individual neurons is ambiguous. The ambiguities can be reduced by considering neuronal population activity. The joint responses of different subpopulations of VS and HS neurons can provide unambiguous information about the three rotational and the three translational components of the animal's self-motion and also, indirectly, about the three-dimensional layout of the environment.

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State-Dependent Performance of Optic-Flow Processing Interneurons

Journal of Neurophysiology ◽

10.1152/jn.00395.2009 ◽

2009 ◽

Vol 102 (6) ◽

pp. 3606-3618 ◽

Cited By ~ 45

Author(s):

Kit D. Longden ◽

Holger G. Krapp

Keyword(s):

Sensory Processing ◽

Optic Flow ◽

Visual Processing ◽

Metabolic Cost ◽

Sensory Stimuli ◽

State Dependent ◽

Directional Motion ◽

Save Energy ◽

Self Motion ◽

The Impact

Active locomotive states are metabolically expensive and require efficient sensory processing both to avoid wasteful movements and to cope with an extended bandwidth of sensory stimuli. This is particularly true for flying animals because flight, as opposed to walking or resting, imposes a steplike increase in metabolism for the precise execution and control of movements. Sensory processing itself carries a significant metabolic cost, but the principles governing the adjustment of sensory processing to different locomotor states are not well understood. We use the blowfly as a model system to study the impact on visual processing of a neuromodulator, octopamine, which is known to be involved in the regulation of flight physiology. We applied an octopamine agonist and recorded the directional motion responses of identified visual interneurons known to process self-motion–induced optic flow to directional motion stimuli. The neural response range of these neurons is increased and the response latency is reduced. We also found that, due to an elevated spontaneous spike rate, the cells' negative signaling range is increased. Meanwhile, the preferred self-motion parameters the cells encode were state independent. Our results indicate that in the blowfly energetically expensive sensory coding strategies, such as rapid, large responses, and high spontaneous spike activity could be adjusted by the neuromodulator octopamine, likely to save energy during quiet locomotor states.

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Optimal Tuning Widths in Population Coding of Periodic Variables

Neural Computation ◽

10.1162/neco.2006.18.7.1555 ◽

2006 ◽

Vol 18 (7) ◽

pp. 1555-1576 ◽

Cited By ~ 13

Author(s):

Marcelo A. Montemurro ◽

Stefano Panzeri

Keyword(s):

Cortical Neurons ◽

Neuronal Population ◽

Population Coding ◽

Model Parameters ◽

Population Activity ◽

Sensory Stimuli ◽

Tuning Curves ◽

Periodic Stimulus ◽

Tuning Width ◽

Stimulus Features

We study the relationship between the accuracy of a large neuronal population in encoding periodic sensory stimuli and the width of the tuning curves of individual neurons in the population. By using general simple models of population activity, we show that when considering one or two periodic stimulus features, a narrow tuning width provides better population encoding accuracy. When encoding more than two periodic stimulus features, the information conveyed by the population is instead maximal for finite values of the tuning width. These optimal values are only weakly dependent on model parameters and are similar to the width of tuning to orientation ormotion direction of real visual cortical neurons. A very large tuning width leads to poor encoding accuracy, whatever the number of stimulus features encoded. Thus, optimal coding of periodic stimuli is different from that of nonperiodic stimuli, which, as shown in previous studies, would require infinitely large tuning widths when coding more than two stimulus features.

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What, if anything, is the true neurophysiological significance of “rotational dynamics”?

10.1101/597419 ◽

2019 ◽

Cited By ~ 1

Author(s):

Mikhail A. Lebedev ◽

Alexei Ossadtchi ◽

Nil Adell Mill ◽

Núria Armengol Urpí ◽

Maria R. Cervera ◽

...

Keyword(s):

Neuronal Population ◽

Rotational Dynamics ◽

Neuronal Responses ◽

Linear Transformations ◽

Experimental Conditions ◽

Temporal Shift ◽

Firing Rates ◽

Motor Behaviors ◽

Neuronal Populations ◽

Neuronal Mechanisms

AbstractBack in 2012, Churchland and his colleagues proposed that “rotational dynamics”, uncovered through linear transformations of multidimensional neuronal data, represent a fundamental type of neuronal population processing in a variety of organisms, from the isolated leech central nervous system to the primate motor cortex. Here, we evaluated this claim using Churchland’s own data and simple simulations of neuronal responses. We observed that rotational patterns occurred in neuronal populations when (1) there was a temporal shift in peak firing rates exhibited by individual neurons, and (2) the temporal sequence of peak rates remained consistent across different experimental conditions. Provided that such a temporal order of peak firing rates existed, rotational patterns could be easily obtained using a rather arbitrary computer simulation of neural activity; modeling of any realistic properties of motor cortical responses was not needed. Additionally, arbitrary traces, such as Lissajous curves, could be easily obtained from Churchland’s data with multiple linear regression. While these observations suggest that temporal sequences of neuronal responses could be visualized as rotations with various methods, we express doubt about Churchland et al.’s exaggerated assessment that such rotations are related to “an unexpected yet surprisingly simple structure in the population response”, which “explains many of the confusing features of individual neural responses.” Instead, we argue that their approach provides little, if any, insight on the underlying neuronal mechanisms employed by neuronal ensembles to encode motor behaviors in any species.

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Primary motor cortex activity traces distinct trajectories of population dynamics during spontaneous facial motor sequences in mice

10.1101/2021.02.15.431209 ◽

2021 ◽

Author(s):

Juan Carlos Boffi ◽

Tristan Wiessalla ◽

Robert Prevedel

Keyword(s):

Population Dynamics ◽

Motor Cortex ◽

Primary Motor Cortex ◽

Neuronal Population ◽

Future Research ◽

Sequencing Analysis ◽

Population Activity ◽

Neuronal Populations ◽

Custom Made ◽

Facial Area

AbstractWe explore the link between on-going neuronal activity at primary motor cortex (M1) and face movement in awake mice. By combining custom-made behavioral sequencing analysis and fast volumetric Ca2+-imaging, we simultaneously tracked M1 population activity during many different facial motor sequences. We show that a facial area of M1 displays distinct trajectories of neuronal population dynamics across different spontaneous facial motor sequences, suggesting an underlying population dynamics code.Significance statementHow our brain controls a seemingly limitless diversity of body movements remains largely unknown. Recent research brings new light into this subject by showing that neuronal populations at the primary motor cortex display different dynamics during forelimb reaching movements versus grasping, which suggests that different motor sequences could be associated with distinct motor cortex population dynamics. To explore this possibility, we designed an experimental paradigm for simultaneously tracking the activity of neuronal populations in motor cortex across many different motor sequences. Our results support the concept that distinct population dynamics encode different motor sequences, bringing new insight into the role of motor cortex in sculpting behavior while opening new avenues for future research.

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Analysis of neuronal ensemble activity reveals the pitfalls and shortcomings of rotation dynamics

Scientific Reports ◽

10.1038/s41598-019-54760-4 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 2

Author(s):

Mikhail A. Lebedev ◽

Alexei Ossadtchi ◽

Nil Adell Mill ◽

Núria Armengol Urpí ◽

Maria R. Cervera ◽

...

Keyword(s):

Neuronal Population ◽

Neuronal Responses ◽

Linear Transformations ◽

Experimental Conditions ◽

Firing Rates ◽

Motor Behaviors ◽

Neuronal Ensembles ◽

Neuronal Populations ◽

Activity Modeling ◽

Neuronal Mechanisms

AbstractBack in 2012, Churchland and his colleagues proposed that “rotational dynamics”, uncovered through linear transformations of multidimensional neuronal data, represent a fundamental type of neuronal population processing in a variety of organisms, from the isolated leech central nervous system to the primate motor cortex. Here, we evaluated this claim using Churchland’s own data and simple simulations of neuronal responses. We observed that rotational patterns occurred in neuronal populations when (1) there was a temporal sequence in peak firing rates exhibited by individual neurons, and (2) this sequence remained consistent across different experimental conditions. Provided that such a temporal order of peak firing rates existed, rotational patterns could be easily obtained using a rather arbitrary computer simulation of neural activity; modeling of any realistic properties of motor cortical responses was not needed. Additionally, arbitrary traces, such as Lissajous curves, could be easily obtained from Churchland’s data with multiple linear regression. While these observations suggest that temporal sequences of neuronal responses could be visualized as rotations with various methods, we express doubt about Churchland et al.’s bold assessment that such rotations are related to “an unexpected yet surprisingly simple structure in the population response”, which “explains many of the confusing features of individual neural responses”. Instead, we argue that their approach provides little, if any, insight on the underlying neuronal mechanisms employed by neuronal ensembles to encode motor behaviors in any species.

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Human electrocorticography reveals a common neurocognitive pathway for mentalizing about the self and others

10.21203/rs.3.rs-257986/v1 ◽

2021 ◽

Author(s):

Kevin Tan ◽

Amy Daitch ◽

Pedro Pinheiro-Chagas ◽

Kieran Fox ◽

Josef Parvizi ◽

...

Keyword(s):

Social Cognitive ◽

Response Times ◽

Human Subjects ◽

Neuronal Population ◽

Spatiotemporal Resolution ◽

Population Activity ◽

Psychological Traits ◽

Self And Other ◽

Neuronal Populations ◽

The Social

Abstract Hundreds of neuroimaging studies show that mentalizing (i.e., theory of mind) recruits default mode network (DMN) regions with remarkable consistency. Nevertheless, the social-cognitive functions of individual DMN regions remain unclear, perhaps due to the limited spatiotemporal resolution of neuroimaging. We used electrocorticography (ECoG) to record neuronal population activity while 16 human subjects judged the psychological traits of themselves and others. Self- and other-mentalizing recruited near-identical neuronal populations in a common spatiotemporal sequence: activations were earliest in visual cortex, followed by temporoparietal DMN regions, and finally medial prefrontal cortex. Critically, regions with later activations showed greater functional specificity for mentalizing, greater self/other differentiation, and stronger associations with behavioral response times. Moreover, other-mentalizing evoked slower and lengthier activations than self-mentalizing across successive DMN regions, suggesting temporally extended demands on higher-level processes. Our results reveal a common neurocognitive pathway for self- and other-mentalizing that follows a hierarchy of functional specialization across DMN regions.

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Learning and attention reveal a general relationship between neuronal variability and perception

10.1101/137083 ◽

2017 ◽

Cited By ~ 2

Author(s):

Amy M. Ni ◽

Douglas A. Ruff ◽

Joshua J. Alberts ◽

Jen Symmonds ◽

Marlene R. Cohen

Keyword(s):

Visual Cortex ◽

Neuronal Population ◽

Population Coding ◽

Task Demands ◽

Spike Count ◽

Current Debate ◽

Visually Guided ◽

Population Activity ◽

Neuronal Populations ◽

And Performance

The trial-to-trial response variability that is shared between pairs of neurons (termed spike count correlations1 or rSC) has been the subject of many recent studies largely because it might limit the amount of information that can be encoded by neuronal populations. Spike count correlations are flexible and change depending on task demands2-7. However, the relationship between correlated variability and information coding is a matter of current debate2-14. This debate has been difficult to resolve because testing the theoretical predictions would require simultaneous recordings from an experimentally unfeasible number of neurons. We hypothesized that if correlated variability limits population coding, then spike count correlations in visual cortex should a) covary with subjects’ performance on visually guided tasks and b) lie along the dimensions in neuronal population space that contain information that is used to guide behavior. We focused on two processes that are known to improve visual performance: visual attention, which allows observers to focus on important parts of a visual scene15-17, and perceptual learning, which slowly improves observers’ ability to discriminate specific, well-practiced stimuli18-20. Both attention and learning improve performance on visually guided tasks, but the two processes operate on very different timescales and are typically studied using different perceptual tasks. Here, by manipulating attention and learning in the same task, subjects, trials, and neuronal populations, we show that there is a single, robust relationship between correlated variability in populations of visual neurons and performance on a change-detection task. We also propose an explanation for the mystery of how correlated variability might affect performance: it is oriented along the dimensions of population space used by the animal to make perceptual decisions. Our results suggest that attention and learning affect the same aspects of the neuronal population activity in visual cortex, which may be responsible for learning- and attention-related improvements in behavioral performance. More generally, our study provides a framework for leveraging the activity of simultaneously recorded populations of neurons, cognitive factors, and perceptual decisions to understand the neuronal underpinnings of behavior.

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Low rank mechanisms underlying flexible visual representations

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2005797117 ◽

2020 ◽

Vol 117 (47) ◽

pp. 29321-29329 ◽

Cited By ~ 1

Author(s):

Douglas A. Ruff ◽

Cheng Xue ◽

Lily E. Kramer ◽

Faisal Baqai ◽

Marlene R. Cohen

Keyword(s):

Visual Cortex ◽

Cognitive Processes ◽

Task Switching ◽

Visual Processing ◽

Neuronal Population ◽

Low Rank ◽

Visual Neurons ◽

Sensory Stimuli ◽

Neuronal Populations ◽

Wide Range

Neuronal population responses to sensory stimuli are remarkably flexible. The responses of neurons in visual cortex have heterogeneous dependence on stimulus properties (e.g., contrast), processes that affect all stages of visual processing (e.g., adaptation), and cognitive processes (e.g., attention or task switching). Understanding whether these processes affect similar neuronal populations and whether they have similar effects on entire populations can provide insight into whether they utilize analogous mechanisms. In particular, it has recently been demonstrated that attention has low rank effects on the covariability of populations of visual neurons, which impacts perception and strongly constrains mechanistic models. We hypothesized that measuring changes in population covariability associated with other sensory and cognitive processes could clarify whether they utilize similar mechanisms or computations. Our experimental design included measurements in multiple visual areas using four distinct sensory and cognitive processes. We found that contrast, adaptation, attention, and task switching affect the variability of responses of populations of neurons in primate visual cortex in a similarly low rank way. These results suggest that a given circuit may use similar mechanisms to perform many forms of modulation and likely reflects a general principle that applies to a wide range of brain areas and sensory, cognitive, and motor processes.

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Spontaneous behaviors drive multidimensional, brainwide activity

Science ◽

10.1126/science.aav7893 ◽

2019 ◽

Vol 364 (6437) ◽

pp. eaav7893 ◽

Cited By ~ 224

Author(s):

Carsen Stringer ◽

Marius Pachitariu ◽

Nicholas Steinmetz ◽

Charu Bai Reddy ◽

Matteo Carandini ◽

...

Keyword(s):

Visual Cortex ◽

Spontaneous Activity ◽

Random Noise ◽

Population Activity ◽

Sensory Stimuli ◽

Neuronal Populations ◽

Ongoing Behavior ◽

Visual Cortical ◽

Mouse Visual Cortex ◽

External Stimuli

Neuronal populations in sensory cortex produce variable responses to sensory stimuli and exhibit intricate spontaneous activity even without external sensory input. Cortical variability and spontaneous activity have been variously proposed to represent random noise, recall of prior experience, or encoding of ongoing behavioral and cognitive variables. Recording more than 10,000 neurons in mouse visual cortex, we observed that spontaneous activity reliably encoded a high-dimensional latent state, which was partially related to the mouse’s ongoing behavior and was represented not just in visual cortex but also across the forebrain. Sensory inputs did not interrupt this ongoing signal but added onto it a representation of external stimuli in orthogonal dimensions. Thus, visual cortical population activity, despite its apparently noisy structure, reliably encodes an orthogonal fusion of sensory and multidimensional behavioral information.

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A computational toolbox and step-by-step tutorial for the analysis of neuronal population dynamics in calcium imaging data

10.1101/103879 ◽

2017 ◽

Cited By ~ 2

Author(s):

Sebastián A. Romano ◽

Verónica Pérez-Schuster ◽

Adrien Jouary ◽

Alessia Candeo ◽

Jonathan Boulanger-Weill ◽

...

Keyword(s):

Population Dynamics ◽

Calcium Imaging ◽

Single Neuron ◽

Neuronal Population ◽

Population Coding ◽

Imaging Data ◽

Neuronal Responses ◽

Population Activity ◽

The Brain

The development of new imaging and optogenetics techniques to study the dynamics of large neuronal circuits is generating datasets of unprecedented volume and complexity, demanding the development of appropriate analysis tools. We present a tutorial for the use of a comprehensive computational toolbox for the analysis of neuronal population activity imaging. It consists of tools for image pre-processing and segmentation, estimation of significant single-neuron single-trial signals, mapping event-related neuronal responses, detection of activity-correlated neuronal clusters, exploration of population dynamics, and analysis of clusters’ features against surrogate control datasets. They are integrated in a modular and versatile processing pipeline, adaptable to different needs. The clustering module is capable of detecting flexible, dynamically activated neuronal assemblies, consistent with the distributed population coding of the brain. We demonstrate the suitability of the toolbox for a variety of calcium imaging datasets, and provide a case study to explain its implementation.

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