scholarly journals Stimuli reduce the dimensionality of cortical activity

2015 ◽  
Author(s):  
Luca Mazzucato ◽  
Alfredo Fontanini ◽  
Giancarlo La Camera
Keyword(s):  
Neural Activity ◽  
Upper Bound ◽  
Network Models ◽  
Stimulus Presentation ◽  
Ensemble Size ◽  
Empirical Estimation ◽  
Theoretical Dependence ◽  
Number Of Clusters ◽  
Spiking Network ◽  
Evoked Activity

AbstractThe activity of ensembles of simultaneously recorded neurons can be represented as a set of points in the space of firing rates. Even though the dimension of this space is equal to the ensemble size, neural activity can be effectively localized on smaller subspaces. The dimensionality of the neural space is an important determinant of the computational tasks supported by the neural activity. Here, we investigate the dimensionality of neural ensembles from the sensory cortex of alert rats during periods of ongoing (intertrial) and stimulus-evoked activity. We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity. We explain these results using a spiking network model based on a clustered architecture. The model captures the difference in growth rate between ongoing and evoked activity and predicts a characteristic scaling with ensemble size that could be tested in high-density multi-electrode recordings. Moreover, we present a simple theory that predicts the existence of an upper bound on dimensionality. This upper bound is inversely proportional to the amount of pair-wise correlations and, compared to a homogeneous network without clusters, it is larger by a factor equal to the number of clusters. The empirical estimation of such bounds depends on the number and duration of trials and is well predicted by the theory. Together, these results provide a framework to analyze neural dimensionality in alert animals, its behavior under stimulus presentation, and its theoretical dependence on ensemble size, number of clusters, and correlations in spiking network models.

scholarly journals Integration of locomotion and auditory signals in the mouse inferior colliculus

2019 ◽  
Author(s):  
Yoonsun Yang ◽  
Joonyeol Lee ◽  
Gunsoo Kim
Keyword(s):  
Inferior Colliculus ◽  
Neural Activity ◽  
Stimulus Presentation ◽  
Sound Stimulus ◽  
Frequency Selectivity ◽  
Auditory Information ◽  
Neural Signals ◽  
Sound Processing ◽  
Auditory Integration ◽  
Evoked Activity

AbstractThe inferior colliculus (IC) is the major midbrain auditory integration center, where virtually all ascending auditory inputs converge. Although the IC has been extensively studied for sound processing, little is known about the neural activity of the IC in moving subjects, as frequently happens in natural hearing conditions. Here we show, by recording the IC neural activity in walking mice, the activity of IC neurons is strongly modulated by locomotion in the absence of sound stimulus presentation. Similar modulation was also found in deafened mice, demonstrating that IC neurons receive non-auditory, locomotion-related neural signals. Sound-evoked activity was attenuated during locomotion, and the attenuation increased frequency selectivity across the population, while maintaining preferred frequencies. Our results suggest that during behavior, integrating movement-related and auditory information is an essential aspect of sound processing in the IC.

scholarly journals Dynamic relationships between spontaneous and evoked electrophysiological activity

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Soren Wainio-Theberge ◽  
Annemarie Wolff ◽  
Georg Northoff
Keyword(s):  
Neural Activity ◽  
Large Scale ◽  
Behavioral Outcomes ◽  
Low Frequencies ◽  
Scale Free ◽  
Non Invasive ◽  
Spontaneous Neural Activity ◽  
Electrophysiological Signals ◽  
Evoked Activity ◽  
Dynamic Relationships

AbstractSpontaneous neural activity fluctuations have been shown to influence trial-by-trial variation in perceptual, cognitive, and behavioral outcomes. However, the complex electrophysiological mechanisms by which these fluctuations shape stimulus-evoked neural activity remain largely to be explored. Employing a large-scale magnetoencephalographic dataset and an electroencephalographic replication dataset, we investigate the relationship between spontaneous and evoked neural activity across a range of electrophysiological variables. We observe that for high-frequency activity, high pre-stimulus amplitudes lead to greater evoked desynchronization, while for low frequencies, high pre-stimulus amplitudes induce larger degrees of event-related synchronization. We further decompose electrophysiological power into oscillatory and scale-free components, demonstrating different patterns of spontaneous-evoked correlation for each component. Finally, we find correlations between spontaneous and evoked time-domain electrophysiological signals. Overall, we demonstrate that the dynamics of multiple electrophysiological variables exhibit distinct relationships between their spontaneous and evoked activity, a result which carries implications for experimental design and analysis in non-invasive electrophysiology.

scholarly journals Circuit mechanisms encoding odors and driving aging-associated behavioral declines in Caenorhabditis elegans

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Sarah G Leinwand ◽  
Claire J Yang ◽  
Daphne Bazopoulou ◽  
Nikos Chronis ◽  
Jagan Srinivasan ◽  
...  
Keyword(s):  
Caenorhabditis Elegans ◽  
Sensory Neurons ◽  
Neural Activity ◽  
Chemical Cues ◽  
Primary Neurons ◽  
Olfactory Neurons ◽  
Odor Concentration ◽  
Evoked Activity ◽  
Sensory Neural ◽  
Extract Information

Chemosensory neurons extract information about chemical cues from the environment. How is the activity in these sensory neurons transformed into behavior? Using Caenorhabditis elegans, we map a novel sensory neuron circuit motif that encodes odor concentration. Primary neurons, AWCON and AWA, directly detect the food odor benzaldehyde (BZ) and release insulin-like peptides and acetylcholine, respectively, which are required for odor-evoked responses in secondary neurons, ASEL and AWB. Consistently, both primary and secondary neurons are required for BZ attraction. Unexpectedly, this combinatorial code is altered in aged animals: odor-evoked activity in secondary, but not primary, olfactory neurons is reduced. Moreover, experimental manipulations increasing neurotransmission from primary neurons rescues aging-associated neuronal deficits. Finally, we correlate the odor responsiveness of aged animals with their lifespan. Together, these results show how odors are encoded by primary and secondary neurons and suggest reduced neurotransmission as a novel mechanism driving aging-associated sensory neural activity and behavioral declines.

scholarly journals Stimulus Feature-Specific Information Flow Along the Columnar Cortical Microcircuit Revealed by Multivariate Laminar Spiking Analysis

2020 ◽  
Vol 14 ◽  
Author(s):  
David A. Tovar ◽  
Jacob A. Westerberg ◽  
Michele A. Cox ◽  
Kacie Dougherty ◽  
Thomas A. Carlson ◽  
...  
Keyword(s):  
Neural Activity ◽  
Visual Stimulation ◽  
Brain Activity ◽  
Contextual Information ◽  
Univariate Analysis ◽  
Stimulus Presentation ◽  
Population Level ◽  
Granular Cell ◽  
Stimulus Orientation ◽  
Specific Information

Most of the mammalian neocortex is comprised of a highly similar anatomical structure, consisting of a granular cell layer between superficial and deep layers. Even so, different cortical areas process different information. Taken together, this suggests that cortex features a canonical functional microcircuit that supports region-specific information processing. For example, the primate primary visual cortex (V1) combines the two eyes' signals, extracts stimulus orientation, and integrates contextual information such as visual stimulation history. These processes co-occur during the same laminar stimulation sequence that is triggered by the onset of visual stimuli. Yet, we still know little regarding the laminar processing differences that are specific to each of these types of stimulus information. Univariate analysis techniques have provided great insight by examining one electrode at a time or by studying average responses across multiple electrodes. Here we focus on multivariate statistics to examine response patterns across electrodes instead. Specifically, we applied multivariate pattern analysis (MVPA) to linear multielectrode array recordings of laminar spiking responses to decode information regarding the eye-of-origin, stimulus orientation, and stimulus repetition. MVPA differs from conventional univariate approaches in that it examines patterns of neural activity across simultaneously recorded electrode sites. We were curious whether this added dimensionality could reveal neural processes on the population level that are challenging to detect when measuring brain activity without the context of neighboring recording sites. We found that eye-of-origin information was decodable for the entire duration of stimulus presentation, but diminished in the deepest layers of V1. Conversely, orientation information was transient and equally pronounced along all layers. More importantly, using time-resolved MVPA, we were able to evaluate laminar response properties beyond those yielded by univariate analyses. Specifically, we performed a time generalization analysis by training a classifier at one point of the neural response and testing its performance throughout the remaining period of stimulation. Using this technique, we demonstrate repeating (reverberating) patterns of neural activity that have not previously been observed using standard univariate approaches.

Confidence Interval Simulation for Systems of Random Variables

2005 ◽  
Vol 129 (3) ◽  
pp. 836-842 ◽  
Author(s):  
Thomas A. Cruse ◽  
Jeffrey M. Brown
Keyword(s):  
Experimental Data ◽  
Confidence Interval ◽  
Bayesian Network ◽  
Upper Bound ◽  
Random Variables ◽  
Network Models ◽  
Probability Of Failure ◽  
Cumulative Distribution ◽  
Probabilistic Design ◽  
Design Assessment

Bayesian network models are seen as important tools in probabilistic design assessment for complex systems. Such network models for system reliability analysis provide a single probability of failure value whether the experimental data used to model the random variables in the problem are perfectly known or derive from limited experimental data. The values of the probability of failure for each of those two cases are not the same, of course, but the point is that there is no way to derive a Bayesian type of confidence interval from such reliability network models. Bayesian confidence (or belief) intervals for a probability of failure are needed for complex system problems in order to extract information on which random variables are dominant, not just for the expected probability of failure but also for some upper bound, such as for a 95% confidence upper bound. We believe that such confidence bounds on the probability of failure will be needed for certifying turbine engine components and systems based on probabilistic design methods. This paper reports on a proposed use of a two-step Bayesian network modeling strategy that provides a full cumulative distribution function for the probability of failure, conditioned by the experimental evidence for the selected random variables. The example is based on a hypothetical high-cycle fatigue design problem for a transport aircraft engine application.

scholarly journals Tail asymptotics of signal-to-interference ratio dis­tribution in spatial cellular network models

2018 ◽  
Vol 37 (2) ◽  
pp. 431-453 ◽  
Author(s):  
Naoto Miyoshi ◽  
Tomoyuki Shirai
Keyword(s):  
Point Process ◽  
Upper Bound ◽  
Cellular Network ◽  
Path Loss ◽  
Network Models ◽  
Base Stations ◽  
Sufficient Condition ◽  
Tail Asymptotics ◽  
Tail Distribution ◽  
Stationary Point Process

TAIL ASYMPTOTICS OF SIGNAL-TO-INTERFERENCE RATI ODISTRIBUTION IN SPATIAL CELLULAR NETWORK MODELSWe consider a spatial stochastic model of wireless cellular networks, where the base stations BSs are deployed according to a simple and stationary point process on Rd, d > 2. In this model, we investigate tail asymptotics of the distribution of signal-to-interference ratio SIR, which is a key quantity in wireless communications. In the case where the pathloss function representing signal attenuation is unbounded at the origin, we derive the exact tail asymptotics of the SIR distribution under an appropriate sufficient condition. While we show that widely-used models based on a Poisson point process and on a determinantal point process meet the sufficient condition, we also give a counterexample violating it. In the case of bounded path-loss functions, we derive a logarithmically asymptotic upper bound on the SIR tail distribution for the Poisson-based and -Ginibrebased models. A logarithmically asymptotic lower bound with the same order as the upper bound is also obtained for the Poisson-based model.

scholarly journals Inhibitory control of frontal metastability sets the temporal signature of cognition

2020 ◽  
Author(s):  
Vincent Fontanier ◽  
Matthieu Sarazin ◽  
Frederic M. Stoll ◽  
Bruno Delord ◽  
Emmanuel Procyk
Keyword(s):  
Inhibitory Control ◽  
Time Constant ◽  
Neural Activity ◽  
Cognitive Functions ◽  
Network Models ◽  
Specific Area ◽  
Temporal Organization ◽  
Neural Dynamics ◽  
Cell Type ◽  
Fine Grained

AbstractCortical neural dynamics organizes over multiple anatomical and temporal scales. The mechanistic origin of the temporal organization and its contribution to cognition remain unknown. Here we demonstrate the cause of this organization by studying a specific temporal signature (autocorrelogram time constant and latency) of neural activity. In monkey frontal areas, recorded during flexible cognitive decisions, temporal signatures display highly specific area-dependent ranges, as well as anatomical and cell-type distributions. Moreover, temporal signatures are functionally adapted to behaviorally relevant timescales. Fine-grained biophysical network models, constrained to account for temporal signatures, reveal that after-hyperpolarization potassium and inhibitory GABA-B conductances critically determine areas’ specificity. They mechanistically account for temporal signatures by organizing activity into metastable states, with inhibition controlling state stability and transitions. As predicted by models, state durations non-linearly scale with temporal signatures in monkey, matching behavioral timescales. Thus, local inhibitory-controlled metastability constitutes the dynamical core specifying the temporal organization of cognitive functions in frontal areas.

scholarly journals Spontaneous and evoked activity patterns diverge over development

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Lilach Avitan ◽  
Zac Pujic ◽  
Jan Mölter ◽  
Shuyu Zhu ◽  
Biao Sun ◽  
...  
Keyword(s):  
Spontaneous Activity ◽  
Neural Activity ◽  
Activity Patterns ◽  
Higher Dimension ◽  
Immature Brain ◽  
Geometric Distance ◽  
Larval Zebrafish ◽  
Emerging Patterns ◽  
Evoked Activity ◽  
Bayesian Prior

The immature brain is highly spontaneously active. Over development this activity must be integrated with emerging patterns of stimulus-evoked activity, but little is known about how this occurs. Here we investigated this question by recording spontaneous and evoked neural activity in the larval zebrafish tectum from 4 to 15 days post fertilisation. Correlations within spontaneous and evoked activity epochs were comparable over development, and their neural assemblies properties refined in similar ways. However both the similarity between evoked and spontaneous assemblies, and also the geometric distance between spontaneous and evoked patterns, decreased over development. At all stages of development evoked activity was of higher dimension than spontaneous activity. Thus spontaneous and evoked activity do not converge over development in this system, and these results do not support the hypothesis that spontaneous activity evolves to form a Bayesian prior for evoked activity.

Responses to Rare Visual Target and Distractor Stimuli Using Event-Related fMRI

2000 ◽  
Vol 83 (5) ◽  
pp. 3133-3139 ◽  
Author(s):  
Vincent P. Clark ◽  
Sean Fannon ◽  
Song Lai ◽  
Randall Benson ◽  
Lance Bauer
Keyword(s):  
Brain Activity ◽  
Temporal Cortex ◽  
Stimulus Presentation ◽  
Brain Regions ◽  
Event Related Potential ◽  
Anterior Cingulate ◽  
Fmri Signal ◽  
Fmri Study ◽  
Evoked Activity ◽  
Anatomic Locations

Previous studies have found that the P300 or P3 event-related potential (ERP) component is useful in the diagnosis and treatment of many disorders that influence CNS function. However, the anatomic locations of brain regions involved in this response are not precisely known. In the present event-related functional magnetic resonance imaging (fMRI) study, methods of stimulus presentation, data acquisition, and data analysis were optimized for the detection of brain activity in response to stimuli presented in the three-stimulus oddball task. This paradigm involves the interleaved, pseudorandom presentation of single block-letter target and distractor stimuli that previously were found to generate the P3b and P3a ERP subcomponents, respectively, and frequent standard stimuli. Target stimuli evoked fMRI signal increases in multiple brain regions including the thalamus, the bilateral cerebellum, and the occipital-temporal cortex as well as bilateral superior, medial, inferior frontal, inferior parietal, superior temporal, precentral, postcentral, cingulate, insular, left middle temporal, and right middle frontal gyri. Distractor stimuli evoked an fMRI signal change bilaterally in inferior anterior cingulate, medial frontal, inferior frontal, and right superior frontal gyri, with additional activity in bilateral inferior parietal lobules, lateral cerebellar hemispheres and vermis, and left fusiform, middle occipital, and superior temporal gyri. Significant variation in the amplitude and polarity of distractor-evoked activity was observed across stimulus repetitions. No overlap was observed between target- and distractor-evoked activity. These event-related fMRI results shed light on the anatomy of responses to target and distractor stimuli that have proven useful in many ERP studies of healthy and clinically impaired populations.

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