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 Integration of locomotion and auditory signals in the mouse inferior colliculus

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Yoonsun Yang ◽  
Joonyeol Lee ◽  
Gunsoo Kim
Keyword(s):  
Inferior Colliculus ◽  
Neural Activity ◽  
Neuronal Population ◽  
Frequency Selectivity ◽  
Hearing Impaired ◽  
Auditory Information ◽  
Neural Signals ◽  
Sound Processing ◽  
Auditory Integration ◽  
Evoked Activity

The 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, by recording neural activity in walking mice, we show that the activity of IC neurons is strongly modulated by locomotion, even in the absence of sound stimuli. Similar modulation was also found in hearing-impaired mice, demonstrating that IC neurons receive non-auditory, locomotion-related neural signals. Sound-evoked activity was attenuated during locomotion, and this attenuation increased frequency selectivity across the neuronal 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 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.

Neuromodulatory Feedback to the Inferior Colliculus

2018 ◽  
pp. 576-610 ◽  
Author(s):  
Laura Hurley
Keyword(s):  
Inferior Colliculus ◽  
Auditory System ◽  
Auditory Processing ◽  
Internal State ◽  
Brain Regions ◽  
Auditory Information ◽  
Situational Variables ◽  
Acoustic Stimuli ◽  
Functionally Diverse ◽  
Behavioral Information

The inferior colliculus (IC) receives prominent projections from centralized neuromodulatory systems. These systems include extra-auditory clusters of cholinergic, dopaminergic, noradrenergic, and serotonergic neurons. Although these modulatory sites are not explicitly part of the auditory system, they receive projections from primary auditory regions and are responsive to acoustic stimuli. This bidirectional influence suggests the existence of auditory-modulatory feedback loops. A characteristic of neuromodulatory centers is that they integrate inputs from anatomically widespread and functionally diverse sets of brain regions. This connectivity gives neuromodulatory systems the potential to import information into the auditory system on situational variables that accompany acoustic stimuli, such as context, internal state, or experience. Once released, neuromodulators functionally reconfigure auditory circuitry through a variety of receptors expressed by auditory neurons. In addition to shaping ascending auditory information, neuromodulation within the IC influences behaviors that arise subcortically, such as prepulse inhibition of the startle response. Neuromodulatory systems therefore provide a route for integrative behavioral information to access auditory processing from its earliest levels.

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 Interpersonal Comparison of Utility by Measuring Neural Activity

2021 ◽  
Author(s):  
Kaosu Matsumori ◽  
Kazuki Iijima ◽  
Yukihito Yomogida ◽  
Kenji Matsumoto
Keyword(s):  
Neural Activity ◽  
Optimal Taxation ◽  
Cost Benefit ◽  
Third Party ◽  
Impossibility Theorem ◽  
Interpersonal Comparison ◽  
Neural Signals ◽  
Collective Decisions ◽  
Economic Goods ◽  
Household Incomes

Aggregating welfare across individuals to reach collective decisions is one of the most fundamental problems in our society. Interpersonal comparison of utility is pivotal and inevitable for welfare aggregation, because if each person's utility is not interpersonally comparable, there is no rational aggregation procedure that simultaneously satisfies even some very mild conditions for validity (Arrow's impossibility theorem). However, scientific methods for interpersonal comparison of utility have thus far not been available. Here, we have developed a method for interpersonal comparison of utility based on brain signals, by measuring the neural activity of participants performing gambling tasks. We found that activity in the medial frontal region was correlated with changes in expected utility, and that, for the same amount of money, the activity evoked was larger for participants with lower household incomes than for those with higher household incomes. Furthermore, we found that the ratio of neural signals from lower-income participants to those of higher-income participants coincided with estimates of their psychological pleasure by "impartial spectators", i.e. disinterested third-party participants satisfying specific conditions. Finally, we derived a decision rule based on aggregated welfare from our experimental data, and confirmed that it was applicable to a distribution problem. These findings suggest that our proposed method for interpersonal comparison of utility enables scientifically reasonable welfare aggregation by escaping from Arrow's impossibility and has implications for the fair distribution of economic goods. Our method can be further applied for evidence-based policy making in nations that use cost-benefit analyses or optimal taxation theory for policy evaluation.

scholarly journals Dissociation of early and late face-related processes in Autism Spectrum Disorder and Williams syndrome

2021 ◽  
Author(s):  
Alice Gomez ◽  
Guillaume Lio ◽  
Manuela Costa ◽  
Angela Sirigu ◽  
Caroline Demily
Keyword(s):  
Williams Syndrome ◽  
Autism Spectrum ◽  
Neural Signals ◽  
Face To Face ◽  
Face Features ◽  
Early Signal ◽  
Reverse Pattern ◽  
The Face ◽  
Evoked Activity ◽  
Multivariate Pattern

Abstract Williams syndrome (WS) and Autism Spectrum Disorders (ASD) are psychiatric conditions associated with atypical but opposite face-to-face interactions patterns: WS patients overly stare at others, ASD individuals escape eye contact. Whether these behaviors result from dissociable visual processes within the occipito-temporal pathways is unknown. Using high-density electroencephalography, multivariate pattern classification and group blind source separation, we searched for face-related neural signals that could best discriminate WS (N = 14), ASD (N = 14) and neurotypical populations (N = 14). We found two peaks in neurotypical participants: the first at 170ms, an early signal known to be implicated in low-level face features, the second at 260ms, a late component implicated in decoding salient face social cues. The late 260ms signal varied as a function of the distance of the eyes in the face stimulus with respect to the viewers’ fovea, meaning that it was strongest when the eyes were projected on the fovea and weakest when projected in the retinal periphery. Remarkably, both components were found distinctly impaired and preserved in WS and ASD. In WS, we could weakly decode the 170ms signal probably due to their relatively poor ability to process faces’ morphology while the late 260ms component shown to be eye sensitive was highly significant. The reverse pattern was observed in ASD participants who showed neurotypical like early 170ms evoked activity but impaired late evoked 260ms signal. Our study reveals a dissociation between WS and ASD patients and point at different neural origins for their social impairments.

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.

scholarly journals Coordinated neuronal ensembles in primary auditory cortical columns

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Jermyn Z See ◽  
Craig A Atencio ◽  
Vikaas S Sohal ◽  
Christoph E Schreiner
Keyword(s):  
Information Processing ◽  
Primary Auditory Cortex ◽  
Local Network ◽  
Auditory Information ◽  
Synchronous Activity ◽  
Information Encoding ◽  
Neuronal Ensembles ◽  
Cortical Columns ◽  
Reduction Techniques ◽  
Evoked Activity

The synchronous activity of groups of neurons is increasingly thought to be important in cortical information processing and transmission. However, most studies of processing in the primary auditory cortex (AI) have viewed neurons as independent filters; little is known about how coordinated AI neuronal activity is expressed throughout cortical columns and how it might enhance the processing of auditory information. To address this, we recorded from populations of neurons in AI cortical columns of anesthetized rats and, using dimensionality reduction techniques, identified multiple coordinated neuronal ensembles (cNEs), which are groups of neurons with reliable synchronous activity. We show that cNEs reflect local network configurations with enhanced information encoding properties that cannot be accounted for by stimulus-driven synchronization alone. Furthermore, similar cNEs were identified in both spontaneous and evoked activity, indicating that columnar cNEs are stable functional constructs that may represent principal units of information processing in AI.

Sign in / Sign up

Export Citation Format

Share Document