scholarly journals Neuronal correlations in MT and MST impair population decoding of opposite directions of random dot motion

2018 ◽  
Author(s):  
Tristan A. Chaplin ◽  
Maureen A. Hagan ◽  
Benjamin J. Allitt ◽  
Leo L. Lui
Keyword(s):  
Population Coding ◽  
Correlation Structure ◽  
Single Neurons ◽  
Electrode Arrays ◽  
Preferred Direction ◽  
Dot Motion ◽  
Random Dot Patterns ◽  
Stimulus Features ◽  
Random Dot Motion ◽  
Spiking Activity

AbstractThe study of neuronal responses to random-dot motion patterns has provided some of the most valuable insights into how the activity of neurons is related to perception. In the opposite directions of motion paradigm, the motion signal strength is decreased by manipulating the coherence of random dot patterns to examine how well the activity of single neurons represents the direction of motion. To extend this paradigm to populations of neurons, studies have used modelling based on data from pairs of neurons, but several important questions require further investigation with larger neuronal datasets. We recorded neuronal populations in the middle temporal (MT) and medial superior temporal (MST) areas of anaesthetized marmosets with electrode arrays, while varying the coherence of random dot patterns in two opposite directions of motion (left and right). Using the spike rates of simultaneously recorded neurons, we decoded the direction of motion at each level of coherence with linear classifiers. We found that the presence of correlations had a detrimental effect to decoding performance, but that learning the correlation structure produced better decoding performance compared to decoders that ignored the correlation structure. We also found that reducing motion coherence increased neuronal correlations, but decoders did not need to be optimized for each coherence level. Finally, we showed that decoder weights depend of left-right selectivity at 100% coherence, rather than the preferred direction. These results have implications for understanding how the information encoded by populations of neurons is affected by correlations in spiking activity.Significance StatementMany studies have examined how the spiking activity of single neurons can encode stimulus features, such the direction of motion of visual stimuli. However, majority of such studies to date have only recorded from a small number of neurons at the same time, meaning that one cannot adequately account for the trial-to-trial correlations in spiking activity between neurons. Using multi-channel recordings, we were able to measure the neuronal correlations, and their effects on population coding of stimulus features. Our results have implications on the way which neural populations must be readout in order to maximize information.

scholarly journals The basis of area and dot number effects in random dot motion perception

1982 ◽  
Vol 22 (10) ◽  
pp. 1253-1259 ◽  
Author(s):  
Curtis L. Baker ◽  
Oliver J. Braddick
Keyword(s):  
Motion Perception ◽  
Dot Motion ◽  
Random Dot Motion

scholarly journals Sensitivity of Neurons in the Middle Temporal Area of Marmoset Monkeys to Random Dot Motion

2017 ◽  
Author(s):  
Tristan A. Chaplin ◽  
Benjamin J. Allitt ◽  
Maureen A. Hagan ◽  
Nicholas S. Price ◽  
Ramesh Rajan ◽  
...  
Keyword(s):  
Primate Species ◽  
Neural Basis ◽  
Temporal Area ◽  
Middle Temporal ◽  
Mt Neurons ◽  
Wide Range ◽  
Middle Temporal Area ◽  
Dot Motion ◽  
Marmoset Monkeys ◽  
Random Dot Motion

AbstractNeurons in the Middle Temporal area (MT) of the primate cerebral cortex respond to moving visual stimuli. The sensitivity of MT neurons to motion signals can be characterized by using random-dot stimuli, in which the strength of the motion signal is manipulated by adding different levels of noise (elements that move in random directions). In macaques, this has allowed the calculation of “neurometric” thresholds. We characterized the responses of MT neurons in sufentanil/nitrous oxide anesthetized marmoset monkeys, a species which has attracted considerable recent interest as an animal model for vision research. We found that MT neurons show a wide range of neurometric thresholds, and that the responses of the most sensitive neurons could account for the behavioral performance of macaques and humans. We also investigated factors that contributed to the wide range of observed thresholds. The difference in firing rate between responses to motion in the preferred and null directions was the most effective predictor of neurometric threshold, whereas the direction tuning bandwidth had no correlation with the threshold. We also showed that it is possible to obtain reliable estimates of neurometric thresholds using stimuli that were not highly optimized for each neuron, as is often necessary when recording from large populations of neurons with different receptive field concurrently, as was the case in this study. These results demonstrate that marmoset MT shows an essential physiological similarity to macaque MT, and suggest that its neurons are capable of representing motion signals that allow for comparable motion-in-noise judgments.New and NoteworthyWe report the activity of neurons in marmoset MT in response to random-dot motion stimuli of varying coherence. The information carried by individual MT neurons was comparable to that of the macaque, and that the maximum firing rates were a strong predictor of sensitivity. Our study provides key information regarding the neural basis of motion perception in the marmoset, a small primate species that is becoming increasingly popular as an experimental model.

scholarly journals Multi-shanks SiNAPS Active Pixel Sensor CMOS probe: 1024 simultaneously recording channels for high-density intracortical brain mapping

2019 ◽  
Author(s):  
Fabio Boi ◽  
Nikolas Perentos ◽  
Aziliz Lecomte ◽  
Gerrit Schwesig ◽  
Stefano Zordan ◽  
...  
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Brain Area ◽  
Large Population ◽  
Brain Structures ◽  
High Density ◽  
Single Neurons ◽  
Electrode Arrays ◽  
Unit Recording ◽  
Recording Channels

AbstractThe advent of implantable active dense CMOS neural probes opened a new era for electrophysiology in neuroscience. These single shank electrode arrays, and the emerging tailored analysis tools, provide for the first time to neuroscientists the neurotechnology means to spatiotemporally resolve the activity of hundreds of different single-neurons in multiple vertically aligned brain structures. However, while these unprecedented experimental capabilities to study columnar brain properties are a big leap forward in neuroscience, there is the need to spatially distribute electrodes also horizontally. Closely spacing and consistently placing in well-defined geometrical arrangement multiple isolated single-shank probes is methodologically and economically impractical. Here, we present the first high-density CMOS neural probe with multiple shanks integrating thousand’s of closely spaced and simultaneously recording microelectrodes to map neural activity across 2D lattice. Taking advantage from the high-modularity of our electrode-pixels-based SiNAPS technology, we realized a four shanks active dense probe with 256 electrode-pixels/shank and a pitch of 28 µm, for a total of 1024 simultaneously recording channels. The achieved performances allow for full-band, whole-array read-outs at 25 kHz/channel, show a measured input referred noise in the action potential band (300-7000 Hz) of 6.5 ± 2.1µVRMS, and a power consumption <6 µW/electrode-pixel. Preliminary recordings in awake behaving mice demonstrated the capability of multi-shanks SiNAPS probes to simultaneously record neural activity (both LFPs and spikes) from a brain area >6 mm2, spanning cortical, hippocampal and thalamic regions. High-density 2D array enables combining large population unit recording across distributed networks with precise intra- and interlaminar/nuclear mapping of the oscillatory dynamics. These results pave the way to a new generation of high-density and extremely compact multi-shanks CMOS-probes with tunable layouts for electrophysiological mapping of brain activity at the single-neurons resolution.

Implications of Neuronal Diversity on Population Coding

2006 ◽  
Vol 18 (8) ◽  
pp. 1951-1986 ◽  
Author(s):  
Maoz Shamir ◽  
Haim Sompolinsky
Keyword(s):  
Weighted Average ◽  
Population Coding ◽  
Information Capacity ◽  
Correlated Noise ◽  
Neuronal Diversity ◽  
Population Vector ◽  
Homogeneous Population ◽  
Optimal Linear ◽  
Stimulus Features ◽  
Experimental Findings

In many cortical and subcortical areas, neurons are known to modulate their average firing rate in response to certain external stimulus features. It is widely believed that information about the stimulus features is coded by a weighted average of the neural responses. Recent theoretical studies have shown that the information capacity of such a coding scheme is very limited in the presence of the experimentally observed pairwise correlations. However, central to the analysis of these studies was the assumption of a homogeneous population of neurons. Experimental findings show a considerable measure of heterogeneity in the response properties of different neurons. In this study, we investigate the effect of neuronal heterogeneity on the information capacity of a correlated population of neurons. We show that information capacity of a heterogeneous network is not limited by the correlated noise, but scales linearly with the number of cells in the population. This information cannot be extracted by the population vector readout, whose accuracy is greatly suppressed by the correlated noise. On the other hand, we show that an optimal linear readout that takes into account the neuronal heterogeneity can extract most of this information. We study analytically the nature of the dependence of the optimal linear readout weights on the neuronal diversity. We show that simple online learning can generate readout weights with the appropriate dependence on the neuronal diversity, thereby yielding efficient readout.

scholarly journals Optimal Tuning Widths in Population Coding of Periodic Variables

2006 ◽  
Vol 18 (7) ◽  
pp. 1555-1576 ◽  
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.

scholarly journals Stimulus features coded by single neurons of a macaque body category selective patch

2016 ◽  
Vol 113 (17) ◽  
pp. E2450-E2459 ◽  
Author(s):  
Ivo D. Popivanov ◽  
Philippe G. Schyns ◽  
Rufin Vogels
Keyword(s):  
Temporal Cortex ◽  
Superior Temporal Sulcus ◽  
The Body ◽  
Large Majority ◽  
Whole Body ◽  
Single Neurons ◽  
Curved Boundaries ◽  
Stimulus Features ◽  
Local Image

Body category-selective regions of the primate temporal cortex respond to images of bodies, but it is unclear which fragments of such images drive single neurons’ responses in these regions. Here we applied the Bubbles technique to the responses of single macaque middle superior temporal sulcus (midSTS) body patch neurons to reveal the image fragments the neurons respond to. We found that local image fragments such as extremities (limbs), curved boundaries, and parts of the torso drove the large majority of neurons. Bubbles revealed the whole body in only a few neurons. Neurons coded the features in a manner that was tolerant to translation and scale changes. Most image fragments were excitatory but for a few neurons both inhibitory and excitatory fragments (opponent coding) were present in the same image. The fragments we reveal here in the body patch with Bubbles differ from those suggested in previous studies of face-selective neurons in face patches. Together, our data indicate that the majority of body patch neurons respond to local image fragments that occur frequently, but not exclusively, in bodies, with a coding that is tolerant to translation and scale. Overall, the data suggest that the body category selectivity of the midSTS body patch depends more on the feature statistics of bodies (e.g., extensions occur more frequently in bodies) than on semantics (bodies as an abstract category).

scholarly journals Perceptual insensitivity to higher-order statistical moments of coherent random dot motion

2018 ◽  
Vol 18 (6) ◽  
pp. 9 ◽  
Author(s):  
Michael L. Waskom ◽  
Janeen Asfour ◽  
Roozbeh Kiani
Keyword(s):  
Higher Order ◽  
Statistical Moments ◽  
Dot Motion ◽  
Random Dot Motion

scholarly journals Emerging ideas and tools to study the emergent properties of the cortical neural circuits for voluntary motor control in non-human primates

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 749 ◽  
Author(s):  
John F. Kalaska
Keyword(s):  
Motor Control ◽  
Neural Control ◽  
Neural Circuits ◽  
Neuron Activity ◽  
Emergent Properties ◽  
Control Mechanisms ◽  
Single Neurons ◽  
Electrode Arrays ◽  
Voluntary Motor ◽  
Neurophysiological Studies

For years, neurophysiological studies of the cerebral cortical mechanisms of voluntary motor control were limited to single-electrode recordings of the activity of one or a few neurons at a time. This approach was supported by the widely accepted belief that single neurons were the fundamental computational units of the brain (the “neuron doctrine”). Experiments were guided by motor-control models that proposed that the motor system attempted to plan and control specific parameters of a desired action, such as the direction, speed or causal forces of a reaching movement in specific coordinate frameworks, and that assumed that the controlled parameters would be expressed in the task-related activity of single neurons. The advent of chronically implanted multi-electrode arrays about 20 years ago permitted the simultaneous recording of the activity of many neurons. This greatly enhanced the ability to study neural control mechanisms at the population level. It has also shifted the focus of the analysis of neural activity from quantifying single-neuron correlates with different movement parameters to probing the structure of multi-neuron activity patterns to identify the emergent computational properties of cortical neural circuits. In particular, recent advances in “dimension reduction” algorithms have attempted to identify specific covariance patterns in multi-neuron activity which are presumed to reflect the underlying computational processes by which neural circuits convert the intention to perform a particular movement into the required causal descending motor commands. These analyses have led to many new perspectives and insights on how cortical motor circuits covertly plan and prepare to initiate a movement without causing muscle contractions, transition from preparation to overt execution of the desired movement, generate muscle-centered motor output commands, and learn new motor skills. Progress is also being made to import optical-imaging and optogenetic toolboxes from rodents to non-human primates to overcome some technical limitations of multi-electrode recording technology.

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