Response characteristics and connections of auditory cortex in squirrels

1985 ◽  
Vol 78 (S1) ◽  
pp. S67-S67
Author(s):  
L. E. Luethke ◽  
L. Krubitzer ◽  
J. H. Kaas
Keyword(s):  
Auditory Cortex ◽  
Response Characteristics

Neural response characteristics in auditory cortex of the awake ferret

2002 ◽  
Vol 112 (5) ◽  
pp. 2287-2287
Author(s):  
Didier A. Depireux ◽  
Bing‐Zhong Chen ◽  
Peter Marvit ◽  
Yaan Li
Keyword(s):  
Auditory Cortex ◽  
Neural Response ◽  
Response Characteristics

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.

Neuronal activation times to simple, complex, and natural sounds in cat primary and nonprimary auditory cortex

2011 ◽  
Vol 106 (3) ◽  
pp. 1166-1178 ◽  
Author(s):  
Andres Carrasco ◽  
Stephen G. Lomber
Keyword(s):  
Response Time ◽  
Auditory Cortex ◽  
Neuronal Response ◽  
Sensory Information ◽  
Acoustic Signals ◽  
Directional Pattern ◽  
Primary Objective ◽  
Neuronal Activation ◽  
Response Latencies ◽  
Response Characteristics

Interactions between living organisms and the environment are commonly regulated by accurate and timely processing of sensory signals. Hence, behavioral response engagement by an organism is typically constrained by the arrival time of sensory information to the brain. While psychophysical response latencies to acoustic information have been investigated, little is known about how variations in neuronal response time relate to sensory signal characteristics. Consequently, the primary objective of the present investigation was to determine the pattern of neuronal activation induced by simple (pure tones), complex (noise bursts and frequency modulated sweeps), and natural (conspecific vocalizations) acoustic signals of different durations in cat auditory cortex. Our analysis revealed three major cortical response characteristics. First, latency measures systematically increase in an antero-dorsal to postero-ventral direction among regions of auditory cortex. Second, complex acoustic stimuli reliably provoke faster neuronal response engagement than simple stimuli. Third, variations in neuronal response time induced by changes in stimulus duration are dependent on acoustic spectral features. Collectively, these results demonstrate that acoustic signals, regardless of complexity, induce a directional pattern of activation in auditory cortex.

Comparison of Response Characteristics in Auditory Cortex of the Awake and Anesthetized Ferret

2002 ◽  
Author(s):  
Mounya Elhilali ◽  
Jonathan B. Fritz ◽  
David Bozak ◽  
Didier A. Depireux ◽  
Jonathan Z. Simon ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Response Characteristics

Reorganization in Awake Rat Auditory Cortex by Local Microstimulation and Its Effect on Frequency-Discrimination Behavior

2001 ◽  
Vol 86 (4) ◽  
pp. 1555-1572 ◽  
Author(s):  
Sanjiv K. Talwar ◽  
George L. Gerstein
Keyword(s):  
Auditory Cortex ◽  
Frequency Response ◽  
Perceptual Learning ◽  
Large Scale ◽  
Frequency Discrimination ◽  
Specific Stimulus ◽  
Response Characteristics ◽  
Awake Rat ◽  
Central Representation ◽  
Frequency Response Characteristics

In common with other sensory cortices, the mammalian primary auditory cortex (AI) demonstrates the capacity for large-scale reorganization following many experimental situations. For example, training animals in frequency-discrimination tasks has been shown to result in an increase in cortical frequency representation. Such central changes—most commonly, an increase in central representation of specific stimulus parameters—have been hypothesized to underlie the improvements in perceptual acuity (perceptual learning) seen in many learning situations. The actual behavioral relevance of central reorganizations, however, remains speculative. Here, we directly examine this issue. We first show that stimulating the AI cortex of the awake rat with a weak electric current (intracortical microstimulation or ICMS) has the effect of inducing central reorganizations similar to those accompanying the traditional plasticity experiments (a result previously noted only in anesthetized preparations). Depending on the site of AI stimulation, ICMS enlarged the cortical representation of certain frequencies. Next we examined the direct perceptual consequences of ICMS-induced AI reorganization for the rat's ability to discriminate frequencies. Over the course of the experiment, we also detailed, and made comparisons between, the frequency-response characteristics of rat AI cortex in the awake and ketamine-anesthetized animal. AI cells that responded to pure tones were divided into two categories—strongly and weakly responsive—based on the strength of their evoked discharge. Individual cells maintained their respective response strengths in both awake and anesthetized conditions. Strongly responsive cells showed at least four different temporal responses and tended to be narrowly tuned. Their responses were stable over the long term. In general frequency-response characteristics were qualitatively similar in the anesthetized and awake animal; bandwidths tended to be broader in awake animals. Although both strong and weak cell populations respond to tones, only the strongly responsive cells fit into a tonotopically organized scheme. By contrast, weakly responsive cells did not exhibit a frequency mapping and may represent a more diffuse input to AI than that underlying strongly responsive cells. In general, the overall frequency organization of AI was found to be equally well expressed in both the awake and anesthetized rat. ICMS reorganization of AI did not alter frequency-discrimination behavior in the rat—either signal detectability or response bias—suggesting that an increase in central representation, by itself, is insufficient to account for perceptual learning. It is likely that cortical reorganizations that accompany perceptual learning are strongly keyed to specific behavioral contexts.

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