scholarly journals Synaptic Mechanisms for Bandwidth Tuning in Awake Mouse Primary Auditory Cortex

2018 ◽  
Vol 29 (7) ◽  
pp. 2998-3009 ◽  
Author(s):  
Haifu Li ◽  
Feixue Liang ◽  
Wen Zhong ◽  
Linqing Yan ◽  
Lucas Mesik ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Cortical Neurons ◽  
Primary Auditory Cortex ◽  
Cell Voltage ◽  
Inhibitory Neurons ◽  
Complex Sounds ◽  
Excitatory Neurons ◽  
Layer 4 ◽  
Bandwidth Tuning ◽  
Spatial Size

Abstract Spatial size tuning in the visual cortex has been considered as an important neuronal functional property for sensory perception. However, an analogous mechanism in the auditory system has remained controversial. In the present study, cell-attached recordings in the primary auditory cortex (A1) of awake mice revealed that excitatory neurons can be categorized into three types according to their bandwidth tuning profiles in response to band-passed noise (BPN) stimuli: nonmonotonic (NM), flat, and monotonic, with the latter two considered as non-tuned for bandwidth. The prevalence of bandwidth-tuned (i.e., NM) neurons increases significantly from layer 4 to layer 2/3. With sequential cell-attached and whole-cell voltage-clamp recordings from the same neurons, we found that the bandwidth preference of excitatory neurons is largely determined by the excitatory synaptic input they receive, and that the bandwidth selectivity is further enhanced by flatly tuned inhibition observed in all cells. The latter can be attributed at least partially to the flat tuning of parvalbumin inhibitory neurons. The tuning of auditory cortical neurons for bandwidth of BPN may contribute to the processing of complex sounds.

scholarly journals Influence of Context and Behavior on Stimulus Reconstruction From Neural Activity in Primary Auditory Cortex

2009 ◽  
Vol 102 (6) ◽  
pp. 3329-3339 ◽  
Author(s):  
Nima Mesgarani ◽  
Stephen V. David ◽  
Jonathan B. Fritz ◽  
Shihab A. Shamma
Keyword(s):  
Auditory Cortex ◽  
Cortical Neurons ◽  
Receptive Fields ◽  
Primary Auditory Cortex ◽  
Neural Population ◽  
Stimulus Context ◽  
Sensory Stimuli ◽  
Complex Sounds ◽  
Population Responses ◽  
Stimulus Reconstruction

Population responses of cortical neurons encode considerable details about sensory stimuli, and the encoded information is likely to change with stimulus context and behavioral conditions. The details of encoding are difficult to discern across large sets of single neuron data because of the complexity of naturally occurring stimulus features and cortical receptive fields. To overcome this problem, we used the method of stimulus reconstruction to study how complex sounds are encoded in primary auditory cortex (AI). This method uses a linear spectro-temporal model to map neural population responses to an estimate of the stimulus spectrogram, thereby enabling a direct comparison between the original stimulus and its reconstruction. By assessing the fidelity of such reconstructions from responses to modulated noise stimuli, we estimated the range over which AI neurons can faithfully encode spectro-temporal features. For stimuli containing statistical regularities (typical of those found in complex natural sounds), we found that knowledge of these regularities substantially improves reconstruction accuracy over reconstructions that do not take advantage of this prior knowledge. Finally, contrasting stimulus reconstructions under different behavioral states showed a novel view of the rapid changes in spectro-temporal response properties induced by attentional and motivational state.

Neural Population Coding of Natural Sounds in Non-flying Mammals

2017 ◽  
Author(s):  
Israel Nelken
Keyword(s):  
Auditory Cortex ◽  
Auditory System ◽  
Cortical Neurons ◽  
Dominant Role ◽  
Primary Auditory Cortex ◽  
Population Coding ◽  
Neural Population ◽  
Good Effect ◽  
Natural Sounds ◽  
Complex Sounds

Understanding the principles by which sensory systems represent natural stimuli is one of the holy grails of neuroscience. In the auditory system, the study of the coding of natural sounds has a particular prominence. Indeed, the relationships between neural responses to simple stimuli (usually pure tone bursts)—often used to characterize auditory neurons—and complex sounds (in particular natural sounds) may be complex. Many different classes of natural sounds have been used to study the auditory system. Sound families that researchers have used to good effect in this endeavor include human speech, species-specific vocalizations, an “acoustic biotope” selected in one way or another, and sets of artificial sounds that mimic important features of natural sounds. Peripheral and brainstem representations of natural sounds are relatively well understood. The properties of the peripheral auditory system play a dominant role, and further processing occurs mostly within the frequency channels determined by these properties. At the level of the inferior colliculus, the highest brainstem station, representational complexity increases substantially due to the convergence of multiple processing streams. Undoubtedly, the most explored part of the auditory system, in term of responses to natural sounds, is the primary auditory cortex. In spite of over 50 years of research, there is still no commonly accepted view of the nature of the population code for natural sounds in the auditory cortex. Neurons in the auditory cortex are believed by some to be primarily linear spectro-temporal filters, by others to respond to conjunctions of important sound features, or even to encode perceptual concepts such as “auditory objects.” Whatever the exact mechanism is, many studies consistently report a substantial increase in the variability of the response patterns of cortical neurons to natural sounds. The generation of such variation may be the main contribution of auditory cortex to the coding of natural sounds.

Temporally precise population coding of dynamic sounds by auditory cortex

2021 ◽  
Author(s):  
Joshua D Downer ◽  
James Bigelow ◽  
Melissa Runfeldt ◽  
Brian James Malone
Keyword(s):  
Auditory Cortex ◽  
Cortical Neurons ◽  
Frequency Tuning ◽  
Average Rate ◽  
Relative Effectiveness ◽  
Primary Auditory Cortex ◽  
Population Based ◽  
Population Coding ◽  
Complex Sounds ◽  
Animal Vocalizations

Fluctuations in the amplitude envelope of complex sounds provide critical cues for hearing, particularly for speech and animal vocalizations. Responses to amplitude modulation (AM) in the ascending auditory pathway have chiefly been described for single neurons. How neural populations might collectively encode and represent information about AM remains poorly characterized, even in primary auditory cortex (A1). We modeled population responses to AM based on data recorded from A1 neurons in awake squirrel monkeys and evaluated how accurately single trial responses to modulation frequencies from 4 to 512 Hz could be decoded as functions of population size, composition, and correlation structure. We found that a population-based decoding model that simulated convergent, equally weighted inputs was highly accurate and remarkably robust to the inclusion of neurons that were individually poor decoders. By contrast, average rate codes based on convergence performed poorly; effective decoding using average rates was only possible when the responses of individual neurons were segregated, as in classical population decoding models using labeled lines. The relative effectiveness of dynamic rate coding in auditory cortex was explained by shared modulation phase preferences among cortical neurons, despite heterogeneity in rate-based modulation frequency tuning. Our results indicate significant population-based synchrony in primary auditory cortex and suggest that robust population coding of the sound envelope information present in animal vocalizations and speech can be reliably achieved even with indiscriminate pooling of cortical responses. These findings highlight the importance of firing rate dynamics in population-based sensory coding.

Neural Responses in Primary Auditory Cortex Mimic Psychophysical, Across-Frequency-Channel, Gap-Detection Thresholds

2000 ◽  
Vol 84 (3) ◽  
pp. 1453-1463 ◽  
Author(s):  
Jos J. Eggermont
Keyword(s):  
Auditory Cortex ◽  
Cortical Neurons ◽  
Primary Auditory Cortex ◽  
Noise Burst ◽  
Gap Detection ◽  
Frequency Content ◽  
Neural Responses ◽  
Frequency Channel ◽  
Interval Representations ◽  
Onset Response

Responses of single- and multi-units in primary auditory cortex were recorded for gap-in-noise stimuli for different durations of the leading noise burst. Both firing rate and inter-spike interval representations were evaluated. The minimum detectable gap decreased in exponential fashion with the duration of the leading burst to reach an asymptote for durations of 100 ms. Despite the fact that leading and trailing noise bursts had the same frequency content, the dependence on leading burst duration was correlated with psychophysical estimates of across frequency channel (different frequency content of leading and trailing burst) gap thresholds in humans. The duration of the leading burst plus that of the gap was represented in the all-order inter-spike interval histograms for cortical neurons. The recovery functions for cortical neurons could be modeled on basis of fast synaptic depression and after-hyperpolarization produced by the onset response to the leading noise burst. This suggests that the minimum gap representation in the firing pattern of neurons in primary auditory cortex, and minimum gap detection in behavioral tasks is largely determined by properties intrinsic to those, or potentially subcortical, cells.

scholarly journals Fast-spiking GABA circuit dynamics in the auditory cortex predict recovery of sensory processing following peripheral nerve damage

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Jennifer Resnik ◽  
Daniel B Polley
Keyword(s):  
Peripheral Nerve ◽  
Auditory Cortex ◽  
Sensory Processing ◽  
Cortical Neurons ◽  
Sensory Nerve ◽  
Intracortical Inhibition ◽  
Nerve Fibers ◽  
Nerve Damage ◽  
Inhibitory Neurons ◽  
Sound Processing

Cortical neurons remap their receptive fields and rescale sensitivity to spared peripheral inputs following sensory nerve damage. To address how these plasticity processes are coordinated over the course of functional recovery, we tracked receptive field reorganization, spontaneous activity, and response gain from individual principal neurons in the adult mouse auditory cortex over a 50-day period surrounding either moderate or massive auditory nerve damage. We related the day-by-day recovery of sound processing to dynamic changes in the strength of intracortical inhibition from parvalbumin-expressing (PV) inhibitory neurons. Whereas the status of brainstem-evoked potentials did not predict the recovery of sensory responses to surviving nerve fibers, homeostatic adjustments in PV-mediated inhibition during the first days following injury could predict the eventual recovery of cortical sound processing weeks later. These findings underscore the potential importance of self-regulated inhibitory dynamics for the restoration of sensory processing in excitatory neurons following peripheral nerve injuries.

scholarly journals Serotonin Transporter Defect Disturbs Structure and Function of the Auditory Cortex in Mice

2021 ◽  
Vol 15 ◽  
Author(s):  
Wenlu Pan ◽  
Jing Pan ◽  
Yan Zhao ◽  
Hongzheng Zhang ◽  
Jie Tang
Keyword(s):  
Auditory Cortex ◽  
Serotonin Transporter ◽  
Cortical Neurons ◽  
Pyramidal Neurons ◽  
Frequency Tuning ◽  
Primary Auditory Cortex ◽  
Neuronal Morphology ◽  
Neuronal Connections ◽  
The Central Nervous System ◽  
And Function

Serotonin transporter (SERT) modulates the level of 5-HT and significantly affects the activity of serotonergic neurons in the central nervous system. The manipulation of SERT has lasting neurobiological and behavioral consequences, including developmental dysfunction, depression, and anxiety. Auditory disorders have been widely reported as the adverse events of these mental diseases. It is unclear how SERT impacts neuronal connections/interactions and what mechanism(s) may elicit the disruption of normal neural network functions in auditory cortex. In the present study, we report on the neuronal morphology and function of auditory cortex in SERT knockout (KO) mice. We show that the dendritic length of the fourth layer (L-IV) pyramidal neurons and the second-to-third layer (L-II/III) interneurons were reduced in the auditory cortex of the SERT KO mice. The number and density of dendritic spines of these neurons were significantly less than those of wild-type neurons. Also, the frequency-tonotopic organization of primary auditory cortex was disrupted in SERT KO mice. The auditory neurons of SERT KO mice exhibited border frequency tuning with high-intensity thresholds. These findings indicate that SERT plays a key role in development and functional maintenance of auditory cortical neurons. Auditory function should be examined when SERT is selected as a target in the treatment for psychiatric disorders.

Tonotopic and functional organization in the auditory cortex of the big brown bat, Eptesicus fuscus

1993 ◽  
Vol 70 (5) ◽  
pp. 1988-2009 ◽  
Author(s):  
S. P. Dear ◽  
J. Fritz ◽  
T. Haresign ◽  
M. Ferragamo ◽  
J. A. Simmons
Keyword(s):  
Auditory Cortex ◽  
Surface Area ◽  
Cortical Neurons ◽  
Functional Organization ◽  
Primary Auditory Cortex ◽  
Target Range ◽  
Cortical Surface ◽  
Behavioral Experiments ◽  
Cortical Surface Area ◽  
Harmonic Ratio

1. In Eptesicus the auditory cortex, as defined by electrical activity recorded from microelectrodes in response to tone bursts, FM sweeps, and combinations of FM sweeps, encompasses an average cortical surface area of 5.7 mm2. This area is large with respect to the total cortical surface area and reflects the importance of auditory processing to this species of bat. 2. The predominant pattern of organization in response to tone bursts observed in each cortex is tonotopic, with three discernible divisions revealed by our data. However, although cortical best-frequency (BF) maps from most of the individual bats are similar, no two maps are identical. The largest division contains an average of 84% of the auditory cortical surface area, with BF tonotopically mapped from high to low along the anteroposterior axis and is part of the primary auditory cortex. The medium division encompasses an average of 13% of the auditory cortical surface area, with highly variable BF organization across bats. The third region is the smallest, with an average of only 3% of auditory cortical surface area and is located at the anterolateral edge of the cortex. This region is marked by a reversal of the tonotopic axis and a restriction in the range of BFs as compared with the larger, tonotopically organized division. 3. A population of cortical neurons was found (n = 39) in which each neuron exhibited two BF threshold minima (BF1 and BF2) in response to tone bursts. These neurons thus have multipeaked frequency threshold tuning curves. In Eptesicus the majority of multipeaked frequency-tuned neurons (n = 27) have threshold minima at frequencies that correspond to a harmonic ratio of three-to-one. In contrast, the majority of multipeaked neurons in cats have threshold minima at frequencies in a ratio of three-to-two. A three-to-one harmonic ratio corresponds to the "spectral notches" produced by interference between overlapping echoes from multiple reflective surfaces in complex sonar targets. Behavioral experiments have demonstrated the ability of Eptesicus to use spectral interference notches for perceiving target shape, and this subpopulation of multipeaked frequency-tuned neurons may be involved in coding of spectral notches. 4. The auditory cortex contains delay-tuned neurons that encode target range (n = 99). Most delay-tuned neurons respond poorly to tones or individual FM sweeps and require combinations of FM sweeps. They are combination sensitive and delay tuned.(ABSTRACT TRUNCATED AT 400 WORDS)

Neural interaction in cat primary auditory cortex II. Effects of sound stimulation

1994 ◽  
Vol 71 (1) ◽  
pp. 246-270 ◽  
Author(s):  
J. J. Eggermont
Keyword(s):  
Auditory Cortex ◽  
Cortical Neurons ◽  
Relative Size ◽  
Primary Auditory Cortex ◽  
Mean Value ◽  
Neural Synchrony ◽  
Sound Stimulation ◽  
Common Input ◽  
Neural Correlation ◽  
The Mean

1. The effect of auditory stimulation with click trains, noise bursts, amplitude-modulated noise bursts, and amplitude-modulated tone bursts on the correlation of firing of 1,290 neuron pairs recorded on one or two electrodes in primary auditory cortex of the cat was investigated. A distinction was made between neural synchrony (the correlation under stimulus conditions) and neural correlation (the correlation under spontaneous or under stimulus conditions after correction for stimulus-related correlations). For neural correlation 63% of the single-electrode pairs showed a unilateral excitation component, often combined with a common-input peak, and only 11% of the dual electrode pairs showed this unilateral excitation. 2. Under poststimulus conditions the incidence of correlograms with clear peaks was high for single-electrode pairs (80–90% range) and somewhat lower for dual-electrode pairs (50–60% range). The strength of the neural correlation for poststimulus conditions, from 0.5 to 2 s after a 1-s stimulus, was comparable with that obtained for 15-min continuous silence, suggesting that aftereffects of stimulation had largely disappeared after 0.5 s. A stationary analysis of the correlation coefficient corroborated this. 3. Two stimulus-correction procedures, one based on the shift predictor and the other based on the joint peristimulus-time histogram (JPSTH) were compared. The mean value of the neural correlation under stimulus conditions obtained after applying the poststimulus time (PST) predictor was on average 20% larger than the mean value obtained after application of the shift predictor; however, this was not significantly different at the 0.05 level. There were no differences in the shape of the correlograms. This suggests that the less time-consuming shift predictor-based stimulus-correction procedure can be used for cortical neurons. 4. Under stimulus conditions neural correlation coefficients could be < or = 50% smaller than for spontaneous conditions. The strength of the stimulus-corrected neural correlation was inversely related to the relative size of the stimulus predictor (compared with the neural synchrony) and thus to the effectiveness of stimulation. This suggests that the assumption of additivity of stimulus and connectivity effects on neural synchrony is generally violated both for shift predictor and PST predictor procedures. 5. The neural correlogram peaks were narrower for single-electrode pairs than for dual-electrode pairs both under stimulus and spontaneous conditions. Under stimulus conditions the peaks were generally narrower than under spontaneous firing conditions.(ABSTRACT TRUNCATED AT 400 WORDS)

Postnatal Maturation of Primary Auditory Cortex in the Mustached Bat, Pteronotus parnellii

2010 ◽  
Vol 103 (5) ◽  
pp. 2339-2354 ◽  
Author(s):  
M. Vater ◽  
E. Foeller ◽  
E. C. Mora ◽  
F. Coro ◽  
I. J. Russell ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Cortical Neurons ◽  
Second Harmonic ◽  
Tuning Curve ◽  
Primary Auditory Cortex ◽  
Constant Frequency ◽  
Low Frequencies ◽  
Postnatal Maturation ◽  
Mustached Bat ◽  
Pteronotus Parnellii

The primary auditory cortex (AI) of adult Pteronotus parnellii features a foveal representation of the second harmonic constant frequency (CF2) echolocation call component. In the corresponding Doppler-shifted constant frequency (DSCF) area, the 61 kHz range is over-represented for extraction of frequency-shift information in CF2 echoes. To assess to which degree AI postnatal maturation depends on active echolocation or/and reflects ongoing cochlear maturation, cortical neurons were recorded in juveniles up to postnatal day P29, before the bats are capable of active foraging. At P1-2, neurons in posterior AI are tuned sensitively to low frequencies (22–45 dB SPL, 28–35 kHz). Within the prospective DSCF area, neurons had insensitive responses (>60 dB SPL) to frequencies <40 kHz and lacked sensitive tuning curve tips. Up to P10, when bats do not yet actively echolocate, tonotopy is further developed and DSCF neurons respond to frequencies of 51–57 kHz with maximum tuning sharpness ( Q10dB) of 57. Between P11 and 20, the frequency representation in AI includes higher frequencies anterior and dorsal to the DSCF area. More multipeaked neurons (33%) are found than at older age. In the oldest group, DSCF neurons are tuned to frequencies close to 61 kHz with Q10dB values ≤212, and threshold sensitivity, tuning sharpness and cortical latencies are adult-like. The data show that basic aspects of cortical tonotopy are established before the bats actively echolocate. Maturation of tonotopy, increase of tuning sharpness, and upward shift in the characteristic frequency of DSCF neurons appear to strongly reflect cochlear maturation.

Sign in / Sign up

Export Citation Format

Share Document