Tonal response patterns of primary auditory cortex neurons in alert cats

Chimoto, Sohei; Kitama, Toshihiro; Qin, Ling; Sakayori, Shuichi; Sato, Yu

doi:10.1016/s0006-8993(02)02316-8

Cited by 48 publications

(45 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first (Goldstein et al, 1968) reported that ϳ80% of AI units in awake cat responded transiently to 100 ms tones. The second study (Chimoto et al, 2002) found that 38% of awake cat AI cells exhibited sustained responses to 500 ms tones, 22% adapted significantly within 500 ms, and 15% responded only with onset and/or offset spikes. Unlike the present study, neither report considered response decay beyond 500 ms.…”

Section: Discussionmentioning

confidence: 93%

Transformation of Temporal Properties between Auditory Midbrain and Cortex in the Awake Mongolian Gerbil

Ter-Mikaelian¹,

Sanes²,

Semple³

2007

Journal of Neuroscience

165

106

View full text Add to dashboard Cite

The neural representation of meaningful stimulus features is thought to rely on precise discharge characteristics of the auditory cortex. Precisely timed onset spikes putatively carry the majority of stimulus-related information in auditory cortical neurons but make a small contribution to stimulus representation in the auditory midbrain. Because these conclusions derive primarily from anesthetized preparations, we reexamined temporal coding properties of single neurons in the awake gerbil inferior colliculus (IC) and compared them with primary auditory cortex (AI). Surprisingly, AI neurons displayed a reduction of temporal precision compared with those in the IC. Furthermore, this hierarchical transition from high to low temporal fidelity was observed for both static and dynamic stimuli. Because most of the data that support temporal precision were obtained under anesthesia, we also reexamined response properties of IC and AI neurons under these conditions. Our results show that anesthesia has profound effects on the trial-to-trial variability and reliability of discharge and significantly improves the temporal precision of AI neurons to both tones and amplitude-modulated stimuli. In contrast, IC temporal properties are only mildly affected by anesthesia. These results underscore the pitfalls of using anesthetized preparations to study temporal coding. Our findings in awake animals reveal that AI neurons combine faster adaptation kinetics and a longer temporal window than evident in IC to represent ongoing acoustic stimuli.

show abstract

Section: Discussionmentioning

confidence: 93%

Transformation of Temporal Properties between Auditory Midbrain and Cortex in the Awake Mongolian Gerbil

Ter-Mikaelian¹,

Sanes²,

Semple³

2007

Journal of Neuroscience

165

106

View full text Add to dashboard Cite

show abstract

“…Responsiveness to stimulus onsets or offsets appears to be a general characteristic of many auditory-responsive neurons [13,19]. Perhaps not surprisingly, therefore, the single-unit responses to continuous tones were often predictive of responses to USVs.…”

Section: Discussionmentioning

confidence: 99%

“…Ten planned t-tests were performed on each unit to classify the stimulus-elicited firing pattern. The classification scheme, which was based on firing patterns that are commonly elicited by auditory stimuli in other brain regions [13,19], was intended to be as simple as possible and yet still capture some potentially-important differences among stimuli and among PR single units.…”

Section: Methodsmentioning

confidence: 99%

Single-unit responses to 22kHz ultrasonic vocalizations in rat perirhinal cortex

Allen

Furtak

Brown

2007

Behavioural Brain Research

View full text Add to dashboard Cite

Rats emit ultrasonic vocalizations (USVs) as social signals in several situations. Lesion studies have shown that rat perirhinal cortex (PR), a polymodal sensory region that is reciprocally connected with the amygdala, is critical for normal fear conditioning to so-called "22 kHz USVs". Here we evaluated single-unit responses in rat PR to 22 kHz USVs and other acoustic stimuli. One question was whether PR circuits are specifically and preferentially tuned, prior to fear conditioning, to respond to USVs and USV-like stimuli. Two 22 kHz USVs were pre-recorded from different conspecifics. Each USV consisted of a "bout" of several discrete calls. Using experimentally-naïve rats, single-unit responses to the USVs were compared with responses to continuous or discontinuous tones that had the same root frequency as the USVs (19 or 22 kHz). The on/off patterns of the discontinuous tones were temporally-matched to the call structure in the corresponding USVs. Compared to continuous tones, the USVs were no more likely to elicit single-unit firing changes in PR. On the other hand, the continuous tones and USVs clearly did elicit different firing patterns in many units. More specifically, the USVs sometimes elicited a transient increase in discharge frequency to each call in a bout of calls. Interestingly, the USVs and the temporally-matched tone segments usually elicited similar firing patterns. The USV-elicited firing pattern in PR thus appears to be controlled by the on/off temporal structure of the calls rather than by the frequency or amplitude modulations associated with each call in a bout of calls.

show abstract

“…However, in primary auditory cortex, there are also populations of neurons that respond to sound offset (Volkov and Galazjuk, 1991;Chimoto et al, 2002). In a predictive coding perspective, the mechanism that we describe should capture not only how the onset of one sound can be predicted from the onset of another but also how the offset of one sound can be predicted based on the onset of the same sound.…”

Section: Mmn To Changes In Durationmentioning

confidence: 99%

A Neuronal Model of Predictive Coding Accounting for the Mismatch Negativity

Wacongne¹,

Changeux²,

Dehaene³

2012

J. Neurosci.

486

423

View full text Add to dashboard Cite

The mismatch negativity (MMN) is thought to index the activation of specialized neural networks for active prediction and deviance detection. However, a detailed neuronal model of the neurobiological mechanisms underlying the MMN is still lacking, and its computational foundations remain debated. We propose here a detailed neuronal model of auditory cortex, based on predictive coding, that accounts for the critical features of MMN. The model is entirely composed of spiking excitatory and inhibitory neurons interconnected in a layered cortical architecture with distinct input, predictive, and prediction error units. A spike-timing dependent learning rule, relying upon NMDA receptor synaptic transmission, allows the network to adjust its internal predictions and use a memory of the recent past inputs to anticipate on future stimuli based on transition statistics. We demonstrate that this simple architecture can account for the major empirical properties of the MMN. These include a frequency-dependent response to rare deviants, a response to unexpected repeats in alternating sequences (ABABAA. . . ), a lack of consideration of the global sequence context, a response to sound omission, and a sensitivity of the MMN to NMDA receptor antagonists. Novel predictions are presented, and a new magnetoencephalography experiment in healthy human subjects is presented that validates our key hypothesis: the MMN results from active cortical prediction rather than passive synaptic habituation.

show abstract

Tonal response patterns of primary auditory cortex neurons in alert cats

Cited by 48 publications

References 31 publications

Transformation of Temporal Properties between Auditory Midbrain and Cortex in the Awake Mongolian Gerbil

Transformation of Temporal Properties between Auditory Midbrain and Cortex in the Awake Mongolian Gerbil

Single-unit responses to 22kHz ultrasonic vocalizations in rat perirhinal cortex

A Neuronal Model of Predictive Coding Accounting for the Mismatch Negativity

Contact Info

Product

Resources

About