Bridging neuronal correlations and dimensionality reduction

The response of many neurons in the whisker somatosensory system depends on the direction in which a whisker is deflected. Although it is known that the spike count conveys information about this parameter, it is not known how important spike timing might be. The aim of this study was to compare neural codes based on spike count and first-spike latency, respectively. We extracellularly recorded single units from either the rat trigeminal ganglion (primary sensory afferents) or ventroposteromedial (VPM) thalamic nucleus in response to deflection in different directions and quantified alternative neural codes using mutual information. We found that neurons were diverse: some (58% in ganglion, 32% in VPM) conveyed information only by spike count; others conveyed additional information by latency. An issue with latency coding is that latency is measured with respect to the time of stimulus onset, a quantity known to the experimenter but not directly to the subject's brain. We found a potential solution using the integrated population activity as an internal timing signal: in this way, 91% of the first-spike latency information could be recovered. Finally, we asked how well direction could be decoded. For large populations, spike count and latency codes performed similarly; for small ones, decoding was more accurate using the latency code. Our findings indicate that whisker deflection direction is more efficiently encoded by spike timing than by spike count. Spike timing decreases the population size necessary for reliable information transmission and may thereby bring significant advantages in both wiring and metabolic efficiency.

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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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Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction

10.7287/peerj.preprints.27340v1 ◽

2018 ◽

Author(s):

Ryan C Williamson ◽

Brent Doiron ◽

Matt A Smith ◽

Byron M Yu

Keyword(s):

Dimensionality Reduction ◽

Large Scale ◽

Common Ground ◽

Network Models ◽

Neuronal Population ◽

Dimensional Structure ◽

Neural Network Modeling ◽

Population Activity ◽

Large Scale Network ◽

Scale Network

A long-standing goal in neuroscience has been to bring together neuronal recordings and neural network modeling to understand brain function. Neuronal recordings can inform the development of network models, and network models can in turn provide predictions for subsequent experiments. Traditionally, neuronal recordings and network models have been related using single-neuron and pairwise spike train statistics. We review here recent studies that have begun to relate neuronal recordings and network models based on the multi-dimensional structure of neuronal population activity, as identified using dimensionality reduction. This approach has been used to study working memory, decision making, motor control, and more. Dimensionality reduction has provided common ground for incisive comparisons and tight interplay between neuronal recordings and network models.

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Inter- and intraindividual approaches to study functional and somatic reactionsof individuals

Experimental Psychology (Russia) ◽

10.17759/exppsy.2015080305 ◽

2015 ◽

Vol 8 (3) ◽

pp. 47-61

Author(s):

E.I. Rasskazova

Keyword(s):

Quality Of Life ◽

Functional Condition ◽

Normative Sample ◽

Individual Sensitivity ◽

Empirical Relationships ◽

Subjective Stress ◽

Somatic Condition ◽

Adult Participants ◽

Approaches To Study

Paper is devoted to comparisons of inter- and intra-individual approaches to study functional and somatic reactions of individuals to subjective stress in the normative sample. Adult participants (N=79) appraised stress level, experienced somatic symptoms and filled the test of the differentiated assessment of a functional condition within four days in the evenings. In addition, illness frequency and quality of life were estimated. According to the results, appraisals of subjective stress levels, irritability, joints movements’ difficulties, headaches and symptoms that are rare in normative sample should not be averaged due to high variability. Application of the intra-individual approach in addition to the inter-individual one allows revealing cases of individual resistance and mobilization of a functional and somatic condition under the stress. Different patterns of the empirical relationships with illness frequency and quality of life are revealed depending on whether the average estimates of a stress and functioning, or individual sensitivity to a stress are measured.

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Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction

10.7287/peerj.preprints.27340 ◽

2019 ◽

Author(s):

Ryan C Williamson ◽

Brent Doiron ◽

Matt A Smith ◽

Byron M Yu

Keyword(s):

Dimensionality Reduction ◽

Large Scale ◽

Common Ground ◽

Network Models ◽

Neuronal Population ◽

Dimensional Structure ◽

Neural Network Modeling ◽

Population Activity ◽

Large Scale Network ◽

Scale Network

A long-standing goal in neuroscience has been to bring together neuronal recordings and neural network modeling to understand brain function. Neuronal recordings can inform the development of network models, and network models can in turn provide predictions for subsequent experiments. Traditionally, neuronal recordings and network models have been related using single-neuron and pairwise spike train statistics. We review here recent studies that have begun to relate neuronal recordings and network models based on the multi-dimensional structure of neuronal population activity, as identified using dimensionality reduction. This approach has been used to study working memory, decision making, motor control, and more. Dimensionality reduction has provided common ground for incisive comparisons and tight interplay between neuronal recordings and network models.

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Dimensionality reduction for neural population decoding

10.1101/2021.04.18.440336 ◽

2021 ◽

Author(s):

Charles R Heller ◽

Stephen V David

Keyword(s):

Dimensionality Reduction ◽

Large Scale ◽

Population Coding ◽

Neural Population ◽

Neural Decoding ◽

Population Activity ◽

Neural Recordings ◽

Analytical Tools ◽

Population Decoding

Rapidly developing technology for large scale neural recordings has allowed researchers to measure the activity of hundreds to thousands of neurons at single cell resolution in vivo. Neural decoding analyses are a widely used tool used for investigating what information is represented in this complex, high-dimensional neural population activity. Most population decoding methods assume that correlated activity between neurons has been estimated accurately. In practice, this requires large amounts of data, both across observations and across neurons. Unfortunately, most experiments are fundamentally constrained by practical variables that limit the number of times the neural population can be observed under a single stimulus and/or behavior condition. Therefore, new analytical tools are required to study neural population coding while taking into account these limitations. Here, we present a simple and interpretable method for dimensionality reduction that allows neural decoding metrics to be calculated reliably, even when experimental trial numbers are limited. We illustrate the method using simulations and compare its performance to standard approaches for dimensionality reduction and decoding by applying it to single-unit electrophysiological data collected from auditory cortex.

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Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction

10.7287/peerj.preprints.27340v2 ◽

2019 ◽

Author(s):

Ryan C Williamson ◽

Brent Doiron ◽

Matt A Smith ◽

Byron M Yu

Keyword(s):

Dimensionality Reduction ◽

Large Scale ◽

Common Ground ◽

Network Models ◽

Neuronal Population ◽

Dimensional Structure ◽

Neural Network Modeling ◽

Population Activity ◽

Large Scale Network ◽

Scale Network

A long-standing goal in neuroscience has been to bring together neuronal recordings and neural network modeling to understand brain function. Neuronal recordings can inform the development of network models, and network models can in turn provide predictions for subsequent experiments. Traditionally, neuronal recordings and network models have been related using single-neuron and pairwise spike train statistics. We review here recent studies that have begun to relate neuronal recordings and network models based on the multi-dimensional structure of neuronal population activity, as identified using dimensionality reduction. This approach has been used to study working memory, decision making, motor control, and more. Dimensionality reduction has provided common ground for incisive comparisons and tight interplay between neuronal recordings and network models.

Download Full-text