Ripple Analysis in Ferret Primary Auditory Cortex. 1. Response Characteristics of Single Units to Sinusoidally Rippled Spectra

1994 ◽  
Author(s):  
S. A. Shamma ◽  
H. Versnel ◽  
N. Kowalski
Keyword(s):  
Auditory Cortex ◽  
Primary Auditory Cortex ◽  
Single Units ◽  
Response Characteristics ◽  
Rippled Spectra

scholarly journals Effects of deafness and cochlear implant use on temporal response characteristics in cat primary auditory cortex

2014 ◽  
Vol 315 ◽  
pp. 1-9 ◽  
Author(s):  
James B. Fallon ◽  
Robert K. Shepherd ◽  
David A.X. Nayagam ◽  
Andrew K. Wise ◽  
Leon F. Heffer ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Cochlear Implant ◽  
Primary Auditory Cortex ◽  
Temporal Response ◽  
Response Characteristics

Spectrotemporal Structure of Receptive Fields in Areas AI and AAF of Mouse Auditory Cortex

2003 ◽  
Vol 90 (4) ◽  
pp. 2660-2675 ◽  
Author(s):  
Jennifer F. Linden ◽  
Robert C. Liu ◽  
Maneesh Sahani ◽  
Christoph E. Schreiner ◽  
Michael M. Merzenich
Keyword(s):  
Receptive Field ◽  
Auditory Cortex ◽  
Receptive Fields ◽  
Primary Auditory Cortex ◽  
Dynamic Stimuli ◽  
Tuning Curves ◽  
Response Characteristics ◽  
Spectrotemporal Receptive Fields ◽  
Auditory Field ◽  
Tonal Stimuli

The mouse is a promising model system for auditory cortex research because of the powerful genetic tools available for manipulating its neural circuitry. Previous studies have identified two tonotopic auditory areas in the mouse—primary auditory cortex (AI) and anterior auditory field (AAF)— but auditory receptive fields in these areas have not yet been described. To establish a foundation for investigating auditory cortical circuitry and plasticity in the mouse, we characterized receptive-field structure in AI and AAF of anesthetized mice using spectrally complex and temporally dynamic stimuli as well as simple tonal stimuli. Spectrotemporal receptive fields (STRFs) were derived from extracellularly recorded responses to complex stimuli, and frequency-intensity tuning curves were constructed from responses to simple tonal stimuli. Both analyses revealed temporal differences between AI and AAF responses: peak latencies and receptive-field durations for STRFs and first-spike latencies for responses to tone bursts were significantly longer in AI than in AAF. Spectral properties of AI and AAF receptive fields were more similar, although STRF bandwidths were slightly broader in AI than in AAF. Finally, in both AI and AAF, a substantial minority of STRFs were spectrotemporally inseparable. The spectrotemporal interaction typically appeared in the form of clearly disjoint excitatory and inhibitory subfields or an obvious spectrotemporal slant in the STRF. These data provide the first detailed description of auditory receptive fields in the mouse and suggest that although neurons in areas AI and AAF share many response characteristics, area AAF may be specialized for faster temporal processing.

scholarly journals Population Responses Represent Vocalization Identity, Intensity, and Signal-to-Noise Ratio in Primary Auditory Cortex

2019 ◽  
Author(s):  
Ruiye Ni ◽  
David A. Bender ◽  
Dennis L. Barbour
Keyword(s):  
Auditory Cortex ◽  
Single Unit ◽  
Temporal Dynamics ◽  
Population Analysis ◽  
Primary Auditory Cortex ◽  
Population Coding ◽  
Single Units ◽  
Neural Population ◽  
Population Responses ◽  
Noise Ratio

AbstractThe ability to process speech signals under challenging listening environments is critical for speech perception. Great efforts have been made to reveal the underlying single unit encoding mechanism. However, big variability is usually discovered in single-unit responses, and the population coding mechanism is yet to be revealed. In this study, we are aimed to study how a population of neurons encodes behaviorally relevant signals subjective to change in intensity and signal-noise-ratio (SNR). We recorded single-unit activity from the primary auditory cortex of awake common marmoset monkeys (Callithrix jacchus) while delivering conspecific vocalizations degraded by two different background noises: broadband white noise (WGN) and vocalization babble (Babble). By pooling all single units together, the pseudo-population analysis showed the population neural responses track intra- and inter-trajectory angle evolutions track vocalization identity and intensity/SNR, respectively. The ability of the trajectory to track the vocalizations attribute was degraded to a different degree by different noises. Discrimination of neural populations evaluated by neural response classifiers revealed that a finer optimal temporal resolution and longer time scale of temporal dynamics were needed for vocalizations in noise than vocalizations at multiple different intensities. The ability of population responses to discriminate between different vocalizations were mostly retained above the detection threshold.Significance StatementHow our brain excels in the challenge of precise acoustic signal encoding against noisy environment is of great interest for scientists. Relatively few studies have strived to tackle this mystery from the perspective of neural population responses. Population analysis reveals the underlying neural encoding mechanism of complex acoustic stimuli based upon a pool of single units via vector coding. We suggest the spatial population response vectors as one important way for neurons to integrate multiple attributes of natural acoustic signals, specifically, marmots’ vocalizations.

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