scholarly journals A Statistical Model of Shared Variability in the Songbird Auditory System

2017 ◽  
Author(s):  
Lars Buesing ◽  
Ana Calabrese ◽  
John P. Cunningham ◽  
Sarah M. N. Woolley ◽  
Liam Paninski
Keyword(s):  
Simple Model ◽  
Simple Structure ◽  
Vocal Communication ◽  
Factor Model ◽  
Primary Auditory Cortex ◽  
Single Neurons ◽  
Correlated Activity ◽  
Neural Populations ◽  
Cortical Regions ◽  
Simultaneous Activity

AbstractVocal communication evokes robust responses in primary auditory cortex (A1) of songbirds, and single neurons from superficial and deep regions of A1 have been shown to respond selectively to songs over complex, synthetic sounds. However, little is known about how this song selectivity arises and manifests itself on the level of networks of neurons in songbird A1. Here, we examined the network-level coding of song and synthetic sounds in A1 by simultaneously recording the responses of multiple neurons in unanesthetized zebra finches. We developed a latent factor model of the joint simultaneous activity of these neural populations, and found that the shared variability in the activity has a surprisingly simple structure; it is dominated by an unobserved latent source with one degree-of-freedom. This simple model captures the structure of the correlated activity in these populations in both spontaneous and stimulus-driven conditions, and given both song and synthetic stimuli. The inferred latent variability is strongly suppressed under stimulation, consistent with similar observations in a range of mammalian cortical regions.

scholarly journals Music-selective cortex is sensitive to structure in both pitch and time

2021 ◽  
Author(s):  
Dana L Boebinger ◽  
Sam V Norman-Haignere ◽  
Josh H McDermott ◽  
Nancy G Kanwisher
Keyword(s):  
Auditory Cortex ◽  
Primary Auditory Cortex ◽  
Acoustic Properties ◽  
Musical Structure ◽  
Synthetic Control ◽  
Neural Responses ◽  
Wide Range ◽  
Neural Populations ◽  
Cortical Regions ◽  
High Level

Converging evidence suggests that neural populations within human non-primary auditory cortex respond selectively to music. These neural populations respond strongly to a wide range of music stimuli, and weakly to other natural sounds and to synthetic control stimuli matched to music in many acoustic properties, suggesting that they are driven by high-level musical features. What are these features? Here we used fMRI to test the extent to which musical structure in pitch and time contribute to music-selective neural responses. We used voxel decomposition to derive music-selective response components in each of 15 participants individually, and then measured the response of these components to synthetic music clips in which we selectively disrupted musical structure by scrambling either the note pitches and/or onset times. Both types of scrambling produced lower responses compared to when melodic or rhythmic structure was intact. This effect was much stronger in the music-selective component than in the other response components, even those with substantial spatial overlap with the music component. We further found no evidence for any cortical regions sensitive to pitch but not time structure, or vice versa. Our results suggest that the processing of melody and rhythm are intertwined within auditory cortex.

Coding Perceptual Decisions: From Single Units to Emergent Signaling Properties in Cortical Circuits

2020 ◽  
Vol 6 (1) ◽  
pp. 387-409
Author(s):  
Kristine Krug
Keyword(s):  
Spatial Distribution ◽  
Visual Cortex ◽  
Functional Properties ◽  
Sensory Neurons ◽  
Single Neurons ◽  
Cortical Circuits ◽  
Task Context ◽  
Complex Interactions ◽  
Correlated Activity ◽  
Primate Visual Cortex

Spiking activity in single neurons of the primate visual cortex has been tightly linked to perceptual decisions. Any mechanism that reads out these perceptual signals to support behavior must respect the underlying neuroanatomy that shapes the functional properties of sensory neurons. Spatial distribution and timing of inputs to the next processing levels are critical, as conjoint activity of precursor neurons increases the spiking rate of downstream neurons and ultimately drives behavior. I set out how correlated activity might coalesce into a micropool of task-sensitive neurons signaling a particular percept to determine perceptual decision signals locally and for flexible interarea transmission depending on the task context. As data from more and more neurons and their complex interactions are analyzed, the space of computational mechanisms must be constrained based on what is plausible within neurobiological limits. This review outlines experiments to test the new perspectives offered by these extended methods.

scholarly journals Modeling the Correlated Activity of Neural Populations: A Review

2019 ◽  
Vol 31 (2) ◽  
pp. 233-269 ◽  
Author(s):  
Christophe Gardella ◽  
Olivier Marre ◽  
Thierry Mora
Keyword(s):  
Latent Variables ◽  
Collective Effects ◽  
Neural Function ◽  
Neural Encoding ◽  
Correlated Activity ◽  
Neural Populations ◽  
Large Populations ◽  
Single Spike ◽  
Highly Correlated ◽  
And Behavior

The principles of neural encoding and computations are inherently collective and usually involve large populations of interacting neurons with highly correlated activities. While theories of neural function have long recognized the importance of collective effects in populations of neurons, only in the past two decades has it become possible to record from many cells simultaneously using advanced experimental techniques with single-spike resolution and to relate these correlations to function and behavior. This review focuses on the modeling and inference approaches that have been recently developed to describe the correlated spiking activity of populations of neurons. We cover a variety of models describing correlations between pairs of neurons, as well as between larger groups, synchronous or delayed in time, with or without the explicit influence of the stimulus, and including or not latent variables. We discuss the advantages and drawbacks or each method, as well as the computational challenges related to their application to recordings of ever larger populations.

Topography of binaural organization in primary auditory cortex of the cat: effects of changing interaural intensity

1986 ◽  
Vol 56 (3) ◽  
pp. 663-682 ◽  
Author(s):  
R. A. Reale ◽  
R. E. Kettner
Keyword(s):  
Discharge Rate ◽  
Primary Auditory Cortex ◽  
Tone Burst ◽  
Average Frequency ◽  
Single Type ◽  
Intensity Difference ◽  
Single Neurons ◽  
Binaural Interaction ◽  
Quantitative Measurements ◽  
Frequency Representation

Responses from neuron clusters were used to derive binaural and aural dominance maps within the 5- to 30-kHz frequency representation of the primary auditory cortical (AI) field in the barbiturate-anesthetized cat. Tone burst stimuli were presented dichotically using a calibrated and sealed acoustic delivery system to parametrically vary interaural intensity difference (IID). Neuron cluster responses were divided into three binaural interaction classes using audiovisual criteria: summation (56%), suppression (25%), and mixed (17%). Neurons in the summation and suppression classes demonstrated a single type of binaural interaction, regardless of intensity manipulations. Neurons in the mixed binaural class demonstrated summation responses when dichotic tonal intensities were near their threshold levels and the IID was small, but suppression responses when the IID was increased. The relative proportions of the three binaural interaction classes changed with distance along the dorsal-to-ventral isofrequency dimension. Nearly equal proportions of each class were observed at the ventral end of field AI, whereas quite different proportions of each class were seen at the dorsal extreme of the field. The average frequency of occurrence of the mixed binaural class increased nearly monotonically with increasing distance from the dorsal end of field AI. The majority of mapped AI loci exhibited a contralateral aural dominance (65%) with equidominance (25%), ipsilateral aural dominance (6%), and predominantly binaural (4%) classes accounting for the remainder. Average topographic distributions of aural dominance suggested that the ventral end of field AI consisted almost exclusively of the contralateral dominance class, whereas more equal proportions of the four classes were observed near the dorsal extreme of the field. The highest average proportions of ipsilateral aural dominance and predominantly binaural classes were found in the dorsal half of field AI. Single neurons, isolated at cortical loci assigned to the mixed binaural class during the mapping of neuron clusters, were shown to demonstrate both summation and suppression responses. Quantitative measurements relating either discharge rate or response latency to changes in the IID appeared to distinguish these cells from other single neurons studied. Typically, the probability of discharge was initially increased and subsequently decreased by progressive changes in IID that increased the intensity of the ipsilateral tone relative to the contralateral tone. The initial changes in IID characteristically shortened the latent period to the binaural response while subsequent increments in IID produced a more comp

scholarly journals Impact of Correlated Neural Activity on Decision-Making Performance

2013 ◽  
Vol 25 (2) ◽  
pp. 289-327 ◽  
Author(s):  
Nicholas Cain ◽  
Eric Shea-Brown
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Higher Order ◽  
Firing Rates ◽  
Pairwise Correlations ◽  
Neural Populations ◽  
Cell Groups ◽  
Nonlinear Computation ◽  
Standard Integration ◽  
Simultaneous Activity

Stimulus from the environment that guides behavior and informs decisions is encoded in the firing rates of neural populations. Neurons in the populations, however, do not spike independently: spike events are correlated from cell to cell. To what degree does this apparent redundancy have an impact on the accuracy with which decisions can be made and the computations required to optimally decide? We explore these questions for two illustrative models of correlation among cells. Each model is statistically identical at the level of pairwise correlations but differs in higher-order statistics that describe the simultaneous activity of larger cell groups. We find that the presence of correlations can diminish the performance attained by an ideal decision maker to either a small or large extent, depending on the nature of the higher-order correlations. Moreover, although this optimal performance can in some cases be obtained using the standard integration-to-bound operation, in others it requires a nonlinear computation on incoming spikes. Overall, we conclude that a given level of pairwise correlations, even when restricted to identical neural populations, may not always indicate redundancies that diminish decision-making performance.

Topography of intensity tuning in cat primary auditory cortex: single-neuron versus multiple-neuron recordings

1995 ◽  
Vol 73 (1) ◽  
pp. 190-204 ◽  
Author(s):  
M. L. Sutter ◽  
C. E. Schreiner
Keyword(s):  
Single Neuron ◽  
Primary Auditory Cortex ◽  
Single Units ◽  
Pool Data ◽  
Single Neurons ◽  
Firing Rates ◽  
A Cell ◽  
Multiple Unit ◽  
Pure Tones ◽  
Multiple Units

1. We studied the spatial distributions of amplitude tuning (monotonicity of rate-level functions) and response threshold of single neurons along the dorsoventral extent of cat primary auditory cortex (AI). To pool data across animals, we used the multiple-unit map of monotonicity as a frame of reference. Amplitude selectivity of multiple units is known to vary systematically along isofrequency contours, which run roughly in the dorsoventral direction. Clusters sharply tuned for intensity (i.e., "nonmonotonic" clusters) are located near the center of the contour. A second nonmonotonic region can be found several millimeters dorsal to the center. We used the locations of these two nonmonotonic regions as reference points to normalize data across animals. Additionally, to compare this study to sharpness of frequency tuning results, we also used multiple-unit bandwidth (BW) maps as references to pool data. 2. The multiple-unit amplitude-related topographies recorded in previous studies were confirmed. Pooled multiple-unit maps closely approximated the previously reported individual case maps when the multiple-unit monotonicity or the map of bandwidth (in octaves) of pure tones to which a cell responds 40 dB above minimum threshold were used as the pooling reference. When the map of bandwidth (in octaves) of pure tones to which a cell responds 10 dB above minimum threshold map was used as part of the measure, the pooled spatial pattern of multiple-unit activity was degraded. 3. Single neurons exhibited nonmonotonic rate-level functions more frequently than multiple units. Although common in single-neuron recordings (28%), strongly nonmonotonic recordings (firing rates reduced by > 50% at high intensities) were uncommon (8%) in multiple-unit recordings. Intermediately nonmonotonic neurons (firing rates reduced between 20% and 50% at high intensities) occurred with nearly equal probability in single-neuron (28%) and multiple-unit (26%) recordings. The remaining recordings for multiple units (66%) and single units (44%) were monotonic (firing rates within 20% of the maximum at the highest tested intensity). 4. In ventral AI (AIv), the topography of monotonicity for single units was qualitatively similar to multiple units, although single units were on average more intensity selective. In dorsal AI (AId) we consistently found a spatial gradient for sharpness of intensity tuning for multiple units; however, for pooled single units in Aid there was no clear topographic gradient. 5. Response (intensity) thresholds of single neurons were not uniformly distributed across the dorsoventral extent of AI.(ABSTRACT TRUNCATED AT 400 WORDS)

scholarly journals Social experience dependent plasticity of mouse song selectivity without that of song components

2021 ◽  
Author(s):  
Swapna Agarwalla ◽  
Sharba Bandyopadhyay
Keyword(s):  
Speech Perception ◽  
Auditory Cortex ◽  
Social Communication ◽  
Male Mouse ◽  
Primary Auditory Cortex ◽  
Ultrasonic Vocalizations ◽  
Single Neurons ◽  
Female Mice ◽  
Differential Selectivity ◽  
Social Exposure

Syllable sequences in male mouse ultrasonic-vocalizations (USVs), songs, contain structure - quantified through predictability, like birdsong and aspects of speech. Apparent USV innateness and lack of learnability, discount mouse USVs for modelling speech-like social communication and its deficits. Informative contextual natural sequences (SN) were theoretically extracted and they were preferred by female mice. Primary auditory cortex (A1) supragranular neurons show differential selectivity to the same syllables in SN and random sequences (SR). Excitatory neurons (EXNs) in females showed increases in selectivity to whole SNs over SRs based on extent of social exposure with male, but syllable selectivity remained unchanged. Thus mouse A1 single neurons adaptively represent entire order of acoustic units without altering selectivity of individual units, fundamental to speech perception. Additionally, observed plasticity was replicated with silencing of somatostatin positive neurons, which had plastic effects opposite to EXNs, thus pointing out possible pathways involved in perception of sound sequences.

scholarly journals Dissociation of task engagement and arousal effects in auditory cortex and midbrain

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Daniela Saderi ◽  
Zachary P Schwartz ◽  
Charles R Heller ◽  
Jacob R Pennington ◽  
Stephen V David
Keyword(s):  
Auditory Cortex ◽  
Auditory Processing ◽  
Generalized Linear Model ◽  
Pupil Size ◽  
Primary Auditory Cortex ◽  
Task Engagement ◽  
State Variables ◽  
Neural Populations ◽  
Evoked Activity ◽  
Sensory Neural

Both generalized arousal and engagement in a specific task influence sensory neural processing. To isolate effects of these state variables in the auditory system, we recorded single-unit activity from primary auditory cortex (A1) and inferior colliculus (IC) of ferrets during a tone detection task, while monitoring arousal via changes in pupil size. We used a generalized linear model to assess the influence of task engagement and pupil size on sound-evoked activity. In both areas, these two variables affected independent neural populations. Pupil size effects were more prominent in IC, while pupil and task engagement effects were equally likely in A1. Task engagement was correlated with larger pupil; thus, some apparent effects of task engagement should in fact be attributed to fluctuations in pupil size. These results indicate a hierarchy of auditory processing, where generalized arousal enhances activity in midbrain, and effects specific to task engagement become more prominent in cortex.

scholarly journals Auditory cortical generators of the Frequency Following Response are modulated by intermodal attention

2019 ◽  
Author(s):  
Thomas Hartmann ◽  
Nathan Weisz
Keyword(s):  
Primary Auditory Cortex ◽  
Top Down ◽  
Attention Task ◽  
Surface Level ◽  
Frequency Following Response ◽  
Speech Stimuli ◽  
Brainstem Response ◽  
Subcortical Regions ◽  
Cortical Regions ◽  
The Right

AbstractThe vast efferent connectivity of the auditory system suggests that subcortical (thalamic and brainstem) auditory regions should also be sensitive to top-down processes such as selective attention. In electrophysiology, the Frequency Following Response (FFR) to simple speech stimuli has been used extensively to study these subcortical areas. Despite being seemingly straight-forward in addressing the issue of attentional modulations of subcortical regions by means of the FFR, the existing results are highly inconsistent. Moreover, the notion that the FFR exclusively represents subcortical generators has been recently challenged. By applying these techniques to data recorded from 102 magnetoencephalography (MEG) magnetometers in 34 participants during a cross-modal attention task, we aimed to gain a more differentiated perspective on how the generators of the FFR are modulated by either attending to the visual or auditory input. In a first step our results confirm the strong contribution of also cortical regions to the FFR. Interestingly, of all regions exhibiting a measurable FFR response, only the right primary auditory cortex was significantly affected by intermodal attention. By showing a clear cortical contribution to the attentional FFR effect, our work significantly extends previous reports that focus on surface level recordings only. It underlines the importance of making a greater effort to disentangle the different contributing sources of the FFR and serves as a clear precaution of simplistically interpreting the FFR as brainstem response.

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