Sequence sensitivity of neurons in cat primary auditory cortex

Coleman Laboratory, W.M. Keck Center for Integrative Neuroscience, University of California at San Francisco, San Francisco, CA 94143-0732, USA.
Cerebral Cortex (Impact Factor: 8.67). 01/2001; 10(12):1155-67. DOI: 10.1093/cercor/10.12.1155
Source: PubMed

ABSTRACT 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.

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Available from: Christoph E Schreiner, Aug 08, 2015
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    • "Bartlett & Wang, 2005; Brosch & Scheich, 2008; Sadagopan & Wang, 2009). With both fast-and slowly-decaying synaptic adaptation , 16% of columns in the model showed facilitation – a lower proportion than the 40–60% values found in single-unit measurements in primates (Brosch & Schreiner, 2000; Bartlett & Wang, 2005; Brosch & Scheich, 2008). Irrespective of the time constant of synaptic adaptation, the magnitude of facilitation peaked in the lower range (110–140%), resembling the results from primate studies . "
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    ABSTRACT: Incoming sounds are represented in the context of preceding events, and this requires a memory mechanism that integrates information over time. Here, it was demonstrated that response adaptation, the suppression of neural responses due to stimulus repetition, might reflect a computational solution that auditory cortex uses for temporal integration. Adaptation is observed in single-unit measurements as two-tone forward masking effects and as stimulus-specific adaptation (SSA). In non-invasive observations, the amplitude of the auditory N1m response adapts strongly with stimulus repetition, and it is followed by response recovery (the so-called mismatch response) to rare deviant events. The current computational simulations described the serial core-belt-parabelt structure of auditory cortex, and included synaptic adaptation, the short-term, activity-dependent depression of excitatory corticocortical connections. It was found that synaptic adaptation is sufficient for columns to respond selectively to tone pairs and complex tone sequences. These responses were defined as combination sensitive, thus reflecting temporal integration, when a strong response to a stimulus sequence was coupled with weaker responses both to the time-reversed sequence and to the isolated sequence elements. The temporal complexity of the stimulus seemed to be reflected in the proportion of combination-sensitive columns across the different regions of the model. Our results suggest that while synaptic adaptation produces facilitation and suppression effects, including SSA and the modulation of the N1m response, its functional significance may actually be in its contribution to temporal integration. This integration seems to benefit from the serial structure of auditory cortex. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
    European Journal of Neuroscience 03/2015; 41(5):615-30. DOI:10.1111/ejn.12820 · 3.67 Impact Factor
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    • "This phenomenon is similar to the U-shaped behaviour of the N1 component: despite the general reduction of N1 with increasing presentation rates, greatest repetition enhancement was usually observed with a very short temporal gap, e.g. 100 ms (Budd and Michie, 1994; Heinemann et al., 2011; Loveless and Hari, 1993; Wang et al., 2008a,b). In line with this, a single-and multi-neuron study being conducted in the primary auditory cortices of cats showed that the most effective temporal separation between a pair of tones for an enhancement effect was circa 100 ms (Brosch and Schreiner, 2000). One of the possible mechanisms that could have accounted for this enhancement is based on the disinhibition model (Loveless et al., 1989). "
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    ABSTRACT: Acoustic violations in temporal regularity have been traditionally indexed by mismatch negativity (MMN). However, recent studies have demonstrated that humans can detect auditory changes in physical sound features, such as frequency, location and intensity, in the first 50ms after sound onset. Our aim was to examine if temporal regularity violations could be detected in the middle latency range. We used an oddball paradigm with 290ms as standard stimulus onset asynchrony (SOA) and 200ms as deviant SOA. We also employed a control paradigm that comprised of seven SOAs including 200 and 290ms, in order to control for differences due to refractoriness. In the middle latency range, temporal regularity violations led to enhanced Pa and Nb responses, which behaved differently to the corresponding SOAs in the control condition. In the long latency range, temporal regularity violations led to similar behaviours in both oddball and control paradigms. These findings suggest that with a fast presentation rate, human brains are capable to detect temporal regularity violations in the middle latency range. Together with previous studies that found early change detection responses, the current study emphasises that the human brain can encode simple regularity violation as early as approximately 50ms post-stimulus onset.
    Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology 07/2013; 124(12). DOI:10.1016/j.clinph.2013.06.001 · 2.98 Impact Factor
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    • "Furthermore, especially neurons with a strong transient response tended to be more sharply tuned to specific frequencies or frequency ranges (see also Fig. 2B). As had been shown previously (Brosch and Schreiner 2000; Ulanovsky et al. 2004), the responses of individual neurons to individual frequencies differed, depending on the tone frequency that had been played immediately before. Figure 2A shows PSTHs of four sample neurons plotted as in Fig. 1B but conditioned on the direction of the preceding tone step. "
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    ABSTRACT: To process the rich temporal structure of their acoustic environment, organisms have to integrate information over time into an appropriate neural response. Previous studies have addressed the modulation of responses of auditory neurons to a current sound in dependence of the immediate stimulation history, but a quantitative analysis of this important computational process has been missing. In this study, we analyzed temporal integration of information in the spike output of 122 single neurons in primary auditory cortex (A1) of four awake ferrets in response to random tone sequences. We quantified the information contained in the responses about both current and preceding sounds in two ways: by estimating directly the mutual information between stimulus and response, and by training linear classifiers to decode information about the stimulus from the neural response. We found that 1) many neurons conveyed a significant amount of information not only about the current tone but also simultaneously about the previous tone, 2) the neural response to tone sequences was a nonlinear combination of responses to the tones in isolation, and 3) nevertheless, much of the information about current and previous tones could be extracted by linear decoders. Furthermore, our analysis of these experimental data shows that methods from information theory and the application of standard machine learning methods for extracting specific information yield quite similar results.
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