Intracellular study of the cat's primary auditory cortex

Characteristics of neurons in auditory cortex of monkeys performing a simple auditory task.

1. The functional organization of neuronal tuning to the azimuthal location and sound pressure level (SPL) of noise bursts was studied in high-frequency primary auditory cortex (AI) of barbiturate-anesthetized cats. Three data collection strategies were used to map neural responses: 1) electrode penetrations oriented normal to the cortical surface provided information on the radial organization of neurons' responses; 2) neurons' responses were examined at a few points in the middle cortical layers in multiple normal penetrations across AI to produce fine-grain maps of azimuth and level selectivity; and 3) electrode penetrations oriented tangential to the cortical surface provided information on neurons' responses along the isofrequency dimension. 2. An azimuth-level data set was obtained for each single- or multiple- (multi-) unit recording; this consisted of responses to noise bursts at five SPLs (0-80 dB in 20-dB steps) from seven azimuthal locations in the frontal hemifield (-90 to +90 degrees in 30 degrees steps; 0 degree elevation). An azimuth function was derived from these data by averaging response magnitude over all SPLs at each azimuth tested. A preferred azimuth range (PAR; range of azimuths over which the response was > or = 75% of maximum) was calculated from the azimuth function and provided a level-independent measure of azimuth selectivity. Each PAR was assigned to one of four azimuth preference categories (contralateral-, midline-, ipsilateral-preferring, or broad/multipeaked) according to its location and extent. A level function obtained from the data set (responsiveness averaged over all azimuths) was classified as monotonic if it showed a decrease of < or = 25% (relative to maximum) at the highest SPL tested (usually 80 dB), and nonmonotonic if it showed a decrease of > 25%. The percentage reduction in responsiveness, relative to maximum, at the highest level tested (termed nonmonotonic strength) and the preferred level range (PLR; range of SPLs over which responsiveness was > or = 75% of maximum) of each response was also determined. 3. Normal penetrations typically showed a predominance of one azimuth preference category and/or level function type. The majority of penetrations (26/36: 72.2%) showed statistically significant azimuth preference homogeneity, and approximately one-half (17/36: 47.2%) showed significant level function type homogeneity. Over one-third (13/36) showed significant homogeneity for both azimuth preference and level function type. 4. Mapping experiments (n = 4) sampled the azimuth and level response functions at two or more depths in closely spaced normal penetrations that covered several square millimeters of AI.(ABSTRACT TRUNCATED AT 400 WORDS)

Properties of sequence-sensitive neurons in primary auditory cortex of cats were explored in detail. Stimuli were sequences of two tones, in which the frequency and intensity of the first tone and the temporal separation between the first and second, or probe, tone were parametrically varied. After presentation of the first tone, the responses of 32 single units and 48 multiunits to the probe tone were found to be enhanced up to 140-5270% (median 340%) above the response obtained in the single-tone condition. Probe tone enhancement was induced from a considerable number of sequence conditions and depended on the frequency and intensity of the first tone and on the temporal separation between the onsets of the first and the probe tone. On average, the maximally enhanced response occurred when the first tone was 1 octave below or above the probe tone and its intensity was 14 dB louder than the probe tone. The most effective temporal separation of the tones for an enhancement effect was approximately 100 ms. The range of enhancing tones was largely outside the excitatory tuning curve of a neuron. Results extend previous findings of properties of sequence-sensitive neurons in the auditory cortex of echolocating bats and non-echolocating mammals, and suggest that sequence-sensitive neurons are quite common and involved in the cortical representation of spectrotemporal patterns of acoustic signals.

Experiments using intracellular recording of potentials from neurons of the primary auditory cortex of cats anesthetized with pentobarbital showed that under the influence of tones of characteristic frequency for the neuron under test, or of electrical stimulation of nerve fibers of the spiral ganglion, innervating the center of the receptive field of the neuron, transient excitation of the latter is followed by the development of prolonged (20–250 msec) inhibition. The cause of this inhibition is an IPSP arising in the neuron after the action potential. On the basis of data showing a close connection between inhibition and the preceding spike it is concluded that it arises through the participation of a mechanism of recurrent inhibition. During the action of tones of uncharacteristic frequency or electrical stimulation of the peripheral part of the receptive field of the neuron, a response consisting of EPSP-IPSP arises in the neuron. This IPSP is accompanied by inhibition of spontaneous activity of the neuron and its responses to testing stimulation. It has been shown that this inhibition is lateral in its genesis. Characteristics of these two types of inhibition are given.

Reliability and Representational Bandwidth in the Auditory Cortex

Multiple representations of information in the primary auditory cortex of cats: I. Stability and change in slow components of unit activity after conditioning with a click conditioned stimulus

Stimulus induced and spontaneous rhythmic firing of single units in cat primary auditory cortex

Differential representation of spectral and temporal information by primary auditory cortex neurons in awake cats: Relevance to auditory scene analysis

SUMMARYNeuronal oscillations are suggested to play an important role in auditory working memory (WM), but their contribution to content-specific representations has remained unclear. Here, we measure magnetoencephalography during a retro-cueing task with parametric ripple-sound stimuli, which are spectrotemporally similar to speech but resist non-auditory memory strategies. Using machine learning analyses, with rigorous between-subject cross-validation and non-parametric permutation testing, we show that memorized sound content is strongly represented in phase-synchronization patterns between subregions of auditory and frontoparietal cortices. These phase-synchronization patterns predict the memorized sound content steadily across the studied maintenance period. In addition to connectivity-based representations, there are indices of more local, “activity silent” representations in auditory cortices, where the decoding accuracy of WM content significantly increases after task-irrelevant “impulse stimuli.” Our results demonstrate that synchronization patterns across auditory sensory and association areas orchestrate neuronal coding of auditory WM content. This connectivity-based coding scheme could also extend beyond the auditory domain.

This study investigates the degree of similarity of three different auditory cortical areas with respect to the coding of periodic stimuli. Simultaneous single- and multiunit recordings in response to periodic stimuli were made from primary auditory cortex (AI), anterior auditory field (AAF), and secondary auditory cortex (AII) in the cat to addresses the following questions: is there, within each cortical area, a difference in the temporal coding of periodic click trains, amplitude-modulated (AM) noise bursts, and AM tone bursts? Is there a difference in this coding between the three cortical fields? Is the coding based on the temporal modulation transfer function (tMTF) and on the all-order interspike-interval (ISI) histogram the same? Is the perceptual distinction between rhythm and roughness for AM stimuli related to a temporal versus spatial representation of AM frequency in auditory cortex? Are interarea differences in temporal response properties related to differences in frequency tuning? The results showed that: 1) AM stimuli produce much higher best modulation frequencies (BMFs) and limiting rates than periodic click trains. 2) For periodic click trains and AM noise, the BMFs and limiting rates were not significantly different for the three areas. However, for AM tones the BMF and limiting rates were about a factor 2 lower in AAF compared with the other areas. 3) The representation of stimulus periodicity in ISIs resulted in significantly lower mean BMFs and limiting rates compared with those estimated from the tMTFs. The difference was relatively small for periodic click trains but quite large for both AM stimuli, especially in AI and AII. 4) Modulation frequencies <20 Hz were represented in the ISIs, suggesting that rhythm is coded in auditory cortex in temporal fashion. 5) In general only a modest interdependence of spectral- and temporal-response properties in AI and AII was found. The BMFs were correlated positively with characteristic frequency in AAF. The limiting rate was positively correlated with the frequency-tuning curve bandwidth in AI and AII but not in AAF. Only in AAF was a correlation between BMF and minimum latency was found. Thus whereas differences were found in the frequency-tuning curve bandwidth and minimum response latencies among the three areas, the coding of periodic stimuli in these areas was fairly similar with the exception of the very poor representation of AM tones in AII. This suggests a strong parallel processing organization in auditory cortex.

Neurons in the brain form thousands of connections, or synapses, with one another, allowing signals to pass from one cell to the next. To activate a neuron, a high enough activating signal or ‘action potential’ must be reached. However, the accepted view of signal transmission assumes that the great majority of synapses are too weak to activate neurons. This means that often simultaneous inputs from many neurons are required to trigger a single neuron’s activation. However, such coordination is likely unreliable as neurons can react differently to the same stimulus depending on the circumstances. An alternative way of transmitting signals has been reported in turtle brains, where impulses from a single neuron can trigger activity across a network of connections. Furthermore, these responses are reliably repeatable, activating the same neurons in the same order. Riquelme et al. set out to understand the mechanism that underlies this type of neuron activation using a mathematical model based on data from the turtle brain. These data showed that the neural network in the turtle’s brain had many weak synapses but also a few, rare, strong synapses. Simulating this neural network showed that those rare, strong synapses promote the signal’s reliability by providing a consistent route for the signal to travel through the network. The numerous weak synapses, on the other hand, have a regulatory role in providing flexibility to how the activation spreads. This combination of strong and weak connections produces a system that can reliably promote or stop the signal flow depending on the context. Riquelme et al.’s work describes a potential mechanism for how signals might travel reliably through neural networks in the brain, based on data from turtles. Experimental work will need to address whether strong connections play a similar role in other animal species, including humans. In the future, these results may be used as the basis to design new systems for artificial intelligence, building on the success of neural networks.

Fasting and Postprandial Myoelectric Spiking Activity in the Human' Sigmoid Colon

Sensory function is mediated by interactions between external stimuli and intrinsic cortical dynamics that are evident in the modulation of evoked responses by cortical state. A number of recent studies across different modalities have demonstrated that the patterns of activity in neuronal populations can vary strongly between synchronized and desynchronized cortical states, i.e., in the presence or absence of intrinsically generated up and down states. Here we investigated the impact of cortical state on the population coding of tones and speech in the primary auditory cortex (A1) of gerbils, and found that responses were qualitatively different in synchronized and desynchronized cortical states. Activity in synchronized A1 was only weakly modulated by sensory input, and the spike patterns evoked by tones and speech were unreliable and constrained to a small range of patterns. In contrast, responses to tones and speech in desynchronized A1 were temporally precise and reliable across trials, and different speech tokens evoked diverse spike patterns with extremely weak noise correlations, allowing responses to be decoded with nearly perfect accuracy. Restricting the analysis of synchronized A1 to activity within up states yielded similar results, suggesting that up states are not equivalent to brief periods of desynchronization. These findings demonstrate that the representational capacity of A1 depends strongly on cortical state, and suggest that cortical state should be considered as an explicit variable in all studies of sensory processing.

Electrophysiological studies in mammal primary auditory cortex have demonstrated neuronal tuning and cortical spatial organization based upon spectral and temporal qualities of the stimulus including: its frequency, intensity, amplitude modulation and frequency modulation. Although communication and other behaviourally relevant sounds are usually complex, most response characterizations have used tonal stimuli. To better understand the mechanisms necessary to process complex sounds, we investigated neuronal responses to a specific class of broadband stimuli, auditory gratings or ripple stimuli, and compared the responses with single tone responses. Ripple stimuli consisted of 150-200 frequency components with the intensity of each component adjusted such that the envelope of the frequency spectrum is sinusoidal. It has been demonstrated that neurons are tuned to specific characteristics of those ripple stimulus including the intensity, the spacing of the peaks, and the location of the peaks and valleys (C. E. Schreiner and B. M. Calhoun, Auditory Neurosci., 1994; 1: 39-61). Although previous results showed that neuronal response strength varied with the intensity and the fundamental frequency of the stimulus, it is shown here that the relative response to different ripple spacings remains essentially constant with changes in the intensity and the fundamental frequency. These findings support a close relationship between pure-tone receptive fields and ripple transfer functions. However, variations of other stimulus characteristics, such as spectral modulation depth, result in non-linear alterations in the ripple transformation. The processing between the basilar membrane and the primary auditory cortex of broadband stimuli appears generally to be non-linear, although specific stimulus qualities, including the phase of the spectral envelope, are processed in a nearly linear manner.

Interaural level differences (ILDs) are the dominant cue for localizing the sources of high frequency sounds that differ in azimuth. Neurons in the primary auditory cortex (A1) respond differentially to ILDs of simple stimuli such as tones and noise bands, but the extent to which this applies to complex natural sounds, such as vocalizations, is not known. In sufentanil/N2O anesthetized marmosets, we compared the responses of 76 A1 neurons to three vocalizations (Ock, Tsik, and Twitter) and pure tones at cells' characteristic frequency. Each stimulus was presented with ILDs ranging from 20 dB favoring the contralateral ear to 20 dB favoring the ipsilateral ear to cover most of the frontal azimuthal space. The response to each stimulus was tested at three average binaural levels (ABLs). Most neurons were sensitive to ILDs of vocalizations and pure tones. For all stimuli, the majority of cells had monotonic ILD sensitivity functions favoring the contralateral ear, but we also observed ILD sensitivity functions that peaked near the midline and functions favoring the ipsilateral ear. Representation of ILD in A1 was better for pure tones and the Ock vocalization in comparison to the Tsik and Twitter calls; this was reflected by higher discrimination indices and greater modulation ranges. ILD sensitivity was heavily dependent on ABL: changes in ABL by ±20 dB SPL from the optimal level for ILD sensitivity led to significant decreases in ILD sensitivity for all stimuli, although ILD sensitivity to pure tones and Ock calls was most robust to such ABL changes. Our results demonstrate differences in ILD coding for pure tones and vocalizations, showing that ILD sensitivity in A1 to complex sounds cannot be simply extrapolated from that to pure tones. They also show A1 neurons do not show level-invariant representation of ILD, suggesting that such a representation of auditory space is likely to require population coding, and further processing at subsequent hierarchical stages.