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

2019 ◽  
Vol 9 (1) ◽  
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.

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

2019 ◽  
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.

scholarly journals Stretching and squeezing of neuronal log firing rate distribution by psychedelic and intrinsic brain state transitions

2021 ◽  
Author(s):  
Bradley Dearnley ◽  
Martynas Dervinis ◽  
Melissa Shaw ◽  
Michael Okun
Keyword(s):  
Neuronal Population ◽  
Neuronal Firing ◽  
State Transitions ◽  
Brain State ◽  
Rate Distribution ◽  
Firing Rates ◽  
Neuronal Populations ◽  
Psychedelic Drugs ◽  
Large Populations ◽  
Intrinsic Fluctuations

AbstractHow psychedelic drugs change the activity of cortical neuronal populations and whether such changes are specific to transition into the psychedelic brain state or shared with other brain state transitions is not well understood. Here, we used Neuropixels probes to record from large populations of neurons in prefrontal cortex of mice given the psychedelic drug TCB-2. Drug ingestion significantly stretched the distribution of log firing rates of the population of recorded neurons. This phenomenon was previously observed across transitions between sleep and wakefulness, which suggested that stretching of the log-rate distribution can be triggered by different kinds of brain state transitions and prompted us to examine it in more detail. We found that modulation of the width of the log-rate distribution of a neuronal population occurred in multiple areas of the cortex and in the hippocampus even in awake drug-free mice, driven by intrinsic fluctuations in their arousal level. Arousal, however, did not explain the stretching of the log-rate distribution by TCB-2. In both psychedelic and naturally occurring brain state transitions, the stretching or squeezing of the log-rate distribution of an entire neuronal population reflected concomitant changes in two subpopulations, with one subpopulation undergoing a downregulation and often also stretching of its neurons’ log-rate distribution, while the other subpopulation undergoes upregulation and often also a squeeze of its log-rate distribution. In both subpopulations, the stretching and squeezing were a signature of a greater relative impact of the brain state transition on the rates of the slow-firing neurons. These findings reveal a generic pattern of reorganisation of neuronal firing rates by different kinds of brain state transitions.

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

2006 ◽  
Vol 96 (3) ◽  
pp. 1602-1614 ◽  
Author(s):  
K. Karmeier ◽  
J. H. van Hateren ◽  
R. Kern ◽  
M. Egelhaaf
Keyword(s):  
Optic Flow ◽  
Three Dimensional ◽  
Real Life ◽  
Neuronal Population ◽  
Neuronal Responses ◽  
Free Flight ◽  
Population Activity ◽  
Sensory Stimuli ◽  
Neuronal Populations ◽  
Self Motion

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.

scholarly journals Neuronal population mechanisms of lightness perception

2018 ◽  
Vol 120 (5) ◽  
pp. 2296-2310 ◽  
Author(s):  
Douglas A. Ruff ◽  
David H. Brainard ◽  
Marlene R. Cohen
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Neuronal Population ◽  
Neuronal Responses ◽  
Lightness Perception ◽  
Neuronal Mechanism ◽  
Area V4 ◽  
Neuronal Populations ◽  
Types Of Information ◽  
The Way

The way that humans and animals perceive the lightness of an object depends on its physical luminance as well as its surrounding context. While neuronal responses throughout the visual pathway are modulated by context, the relationship between neuronal responses and lightness perception is poorly understood. We searched for a neuronal mechanism of lightness by recording responses of neuronal populations in monkey primary visual cortex (V1) and area V4 to stimuli that produce a lightness illusion in humans, in which the lightness of a disk depends on the context in which it is embedded. We found that the way individual units encode the luminance (or equivalently for our stimuli, contrast) of the disk and its context is extremely heterogeneous. This motivated us to ask whether the population representation in either V1 or V4 satisfies three criteria: 1) disk luminance is represented with high fidelity, 2) the context surrounding the disk is also represented, and 3) the representations of disk luminance and context interact to create a representation of lightness that depends on these factors in a manner consistent with human psychophysical judgments of disk lightness. We found that populations of units in both V1 and V4 fulfill the first two criteria but that we cannot conclude that the two types of information in either area interact in a manner that clearly predicts human psychophysical measurements: the interpretation of our population measurements depends on how subsequent areas read out lightness from the population responses. NEW & NOTEWORTHY A core question in visual neuroscience is how the brain extracts stable representations of object properties from the retinal image. We searched for a neuronal mechanism of lightness perception by determining whether the responses of neuronal populations in primary visual cortex and area V4 could account for a lightness illusion measured using human psychophysics. Our results suggest that comparing psychophysics with population recordings will yield insight into neuronal mechanisms underlying a variety of perceptual phenomena.

scholarly journals Neuronal population mechanisms of lightness perception

2018 ◽  
Author(s):  
Douglas A. Ruff ◽  
David H. Brainard ◽  
Marlene R. Cohen
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Neuronal Population ◽  
Neuronal Responses ◽  
Lightness Perception ◽  
Neuronal Mechanism ◽  
Area V4 ◽  
Neuronal Populations ◽  
Types Of Information ◽  
The Way

AbstractThe way that humans and animals perceive the lightness of an object depends on its physical luminance as well as its surrounding context. While neuronal responses throughout the visual pathway are modulated by context, the relationship between neuronal responses and lightness perception is poorly understood. We searched for a neuronal mechanism of lightness by recording responses of neuronal populations in monkey primary visual cortex (V1) and area V4 to stimuli that produce a lightness illusion in humans, in which the lightness of a disk depends on the context in which it is embedded. We found that the way individual units encode the luminance (or equivalently for our stimuli, contrast) of the disk and its context is extremely heterogeneous. This motivated us to ask whether the population representation in either V1 or V4 satisfies three criteria: 1) disk luminance is represented with high fidelity, 2) the context surrounding the disk is also represented, and 3) the representations of disk luminance and context interact to create a representation of lightness that depends on these factors in a manner consistent with human psychophysical judgments of disk lightness. We found that populations of units in both V1 and V4 fulfill the first two criteria, but that we cannot conclude that the two types of information in either area interact in a manner that clearly predicts human psychophysical measurements: the interpretation of our population measurements depends on how subsequent areas read out lightness from the population responses.New & NoteworthyA core question in visual neuroscience is how the brain extracts stable representations of object properties from the retinal image. We searched for a neuronal mechanism of lightness perception by determining whether the responses of neuronal populations in primary visual cortex and area V4 could account for a lightness illusion measured using human psychophysics. Our results suggest that comparing psychophysics with population recordings will yield insight into neuronal mechanisms underlying a variety of perceptual phenomena.

scholarly journals Lognormal firing rate distribution reveals prominent fluctuation-driven regime in spinal motor networks

2016 ◽  
Author(s):  
Peter C. Petersen ◽  
Rune W. Berg
Keyword(s):  
Neuronal Population ◽  
Output Curve ◽  
Dynamical Regime ◽  
Rhythmic Movements ◽  
Firing Rates ◽  
Motor Behaviors ◽  
Spinal Motor ◽  
Rich Diversity ◽  
The Mean ◽  
Lumbar Spinal

ABSTRACTWhen spinal circuits generate rhythmic movements it is important that the neuronal activity remains within stable bounds to avoid saturation and to preserve responsiveness. In what dynamical regime does the neuronal population operate in order to achieve this? Here, we simultaneously record from hundreds of neurons in lumbar spinal circuits and establish the neuronal fraction that operates within either a ‘mean-driven’ or a ‘fluctuation–driven’ regime during generation of multiple motor behaviors. We find a rich diversity of firing rates across the neuronal population as reflected in a lognormal distribution and demonstrate that half of the neurons spend at least 50% of the time in the ‘fluctuation–driven’ regime regardless of behavior. Since neurons in this regime have a ‘supralinear’ input–output curve, which enhances sensitivity, whereas the mean–driven regime reduces sensitivity, this fraction may reflect a fine trade–off between stability and sensitivity in order to maintain flexibility across motor behaviors.

scholarly journals A deep convolutional visual encoding model of neuronal responses in the LGN

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Eslam Mounier ◽  
Bassem Abdullah ◽  
Hani Mahdi ◽  
Seif Eldawlatly
Keyword(s):  
Firing Rate ◽  
Visual Information ◽  
Visual Stimulation ◽  
Neuronal Population ◽  
Neuronal Firing ◽  
Electrode Arrays ◽  
Deep Convolutional Neural Networks ◽  
Firing Rates ◽  
Spatiotemporal Representation

AbstractThe Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing.

Spatio-temporal visual properties in the ascending tectofugal system

2010 ◽  
Vol 5 (1) ◽  
pp. 21-30 ◽  
Author(s):  
Alice Rokszin ◽  
Zita Márkus ◽  
Gábor Braunitzer ◽  
Antal Berényi ◽  
Marek Wypych ◽  
...  
Keyword(s):  
Visual Information ◽  
Frequency Tuning ◽  
Temporal Frequency ◽  
Neuronal Population ◽  
Neuronal Responses ◽  
Temporal Properties ◽  
Visual Receptive Field ◽  
Detailed Statistical Analysis ◽  
Spatio Temporal ◽  
Visual Properties

AbstractOur study compares the spatio-temporal visual receptive field properties of different subcortical stages of the ascending tectofugal visual system. Extracellular single-cell recordings were performed in the superficial (SCs) and intermediate (SCi) layers of the superior colliculus (SC), the suprageniculate nucleus (Sg) of the posterior thalamus and the caudate nucleus (CN) of halothane-anesthetized cats. Neuronal responses to drifting gratings of various spatial and temporal frequencies were recorded. The neurons of each structure responded optimally to low spatial and high temporal frequencies and displayed narrow spatial and temporal frequency tuning. The detailed statistical analysis revealed that according to its stimulus preferences the SCs has markedly different spatio-temporal properties from the homogeneous group formed by the SCi, Sg and CN. The SCs neurons preferred higher spatial and lower temporal frequencies and had broader spatial tuning than the other structures. In contrast to the SCs the visually active SCi, as well as the Sg and the CN neurons possessed consequently similar spatio-temporal preferences. These data support our hypothesis that the visually active SCi, Sg and CN neurons form a homogeneous neuronal population given a similar spatio-temporal frequency preference and a common function in processing of dynamic visual information.

scholarly journals Perceptual detection depends on spike count integration

2019 ◽  
Author(s):  
Jackson J. Cone ◽  
Morgan L. Bade ◽  
Nicolas Y. Masse ◽  
Elizabeth A. Page ◽  
David J. Freedman ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cerebral Cortex ◽  
Visual Cortex ◽  
Recurrent Neural Networks ◽  
Neural Circuit ◽  
Visual Detection ◽  
Spike Count ◽  
Neuronal Responses ◽  
Neuronal Populations ◽  
And Behavior

AbstractWhenever the retinal image changes some neurons in visual cortex increase their rate of firing, while others decrease their rate of firing. Linking specific sets of neuronal responses with perception and behavior is essential for understanding mechanisms of neural circuit computation. We trained mice to perform visual detection tasks and used optogenetic perturbations to increase or decrease neuronal spiking primary visual cortex (V1). Perceptual reports were always enhanced by increments in V1 spike counts and impaired by decrements, even when increments and decrements were delivered to the same neuronal populations. Moreover, detecting changes in cortical activity depended on spike count integration rather than instantaneous changes in spiking. Recurrent neural networks trained in the task similarly relied on increments in neuronal activity when activity was costly. This work clarifies neuronal decoding strategies employed by cerebral cortex to translate cortical spiking into percepts that can be used to guide behavior.

scholarly journals Confidence-Related Decision Making

2010 ◽  
Vol 104 (1) ◽  
pp. 539-547 ◽  
Author(s):  
Andrea Insabato ◽  
Mario Pannunzi ◽  
Edmund T. Rolls ◽  
Gustavo Deco
Keyword(s):  
Decision Making ◽  
Cognitive Functioning ◽  
Neuronal Activity ◽  
Emergent Property ◽  
Neuronal Responses ◽  
Attractor Network ◽  
Firing Rates ◽  
Integrate And Fire ◽  
The Brain ◽  
High Firing

Neurons have been recorded that reflect in their firing rates the confidence in a decision. Here we show how this could arise as an emergent property in an integrate-and-fire attractor network model of decision making. The attractor network has populations of neurons that respond to each of the possible choices, each biased by the evidence for that choice, and there is competition between the attractor states until one population wins the competition and finishes with high firing that represents the decision. Noise resulting from the random spiking times of individual neurons makes the decision making probabilistic. We also show that a second attractor network can make decisions based on the confidence in the first decision. This system is supported by and accounts for neuronal responses recorded during decision making and makes predictions about the neuronal activity that will be found when a decision is made about whether to stay with a first decision or to abort the trial and start again. The research shows how monitoring can be performed in the brain and this has many implications for understanding cognitive functioning.

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