Encoding frequency contrast in primate auditory cortex

Malone, Brian J.; Scott, Brian H.; Semple, Malcolm N.

doi:10.1152/jn.00878.2013

Cited by 10 publications

(20 citation statements)

References 72 publications

(76 reference statements)

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“…Two adult male monkeys (Macaca mulatta, designated X and Z) participated in these experiments. The methods of animal training, stimulus delivery, and physiological recording have been described previously (Malone et al 2002(Malone et al , 2014Scott et al , 2009Scott et al , 2011. All procedures were in accordance with the Society for Neuroscience guiding principles on the care and use of animals and approved by the Institutional Animal Care and Use Committee of New York University.…”

Section: Methodsmentioning

confidence: 99%

“…Data analysis was performed using MATLAB (MathWorks, Natick, MA). We quantified how effectively cortical responses to stimuli of different durations could be discriminated with peristimulus time histogram (PSTH)-based pattern classifiers (Foffani and Moxon 2004), as in prior reports (Malone et al , 2013(Malone et al , 2014. Because all tones are "aligned" to a common onset time (i.e., 0 ms), classifying tone duration is equivalent to classifying the time of tone offset.…”

Section: Methodsmentioning

confidence: 99%

“…The remaining 33 neurons were recorded in the belt fields immediately adjacent to the core (11 medial, 14 lateral, and 8 caudo-medial). Delineation of field boundaries for this data-set has been described previously (Scott et al, 2011), and as in other studies derived from this data set (e.g., Malone et al 2007Malone et al , 2010Malone et al , 2014, our sample is biased toward neurons with strong responses to pure-tone stimuli; neurons later determined to lie in the belt exhibited tone responses typical of those in the core.…”

Section: Methodsmentioning

confidence: 99%

“…Categorization by response types also allowed us to quantify the efficacy and efficiency of different encoding strategies. We emphasize that these are response types, not cell types, since the shapes of PSTHs vary with stimulus parameters such as frequency (Malone et al 2014) and amplitude . Thus, the distribution of response types we report here is specific to the tone parameters comprising the current dataset: tones presented at each neuron's best frequency and level.…”

Section: Categorization Of Cortical Response Typesmentioning

confidence: 99%

“…We have shown that the response profiles (i.e., the temporal distributions of spikes within the interval encompassing the stimulus presentation) provides more information about tone level ) and tone frequency (Malone et al 2014;Moshitch et al 2006) than does average firing rate. This raises two important questions when thinking about scene segmentation in the context of spike train decoding.…”

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confidence: 99%

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Diverse cortical codes for scene segmentation in primate auditory cortex

Malone

Scott

Semple

2015

Journal of Neurophysiology

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The temporal coherence of amplitude fluctuations is a critical cue for segmentation of complex auditory scenes. The auditory system must accurately demarcate the onsets and offsets of acoustic signals. We explored how and how well the timing of onsets and offsets of gated tones are encoded by auditory cortical neurons in awake rhesus macaques. Temporal features of this representation were isolated by presenting otherwise identical pure tones of differing durations. Cortical response patterns were diverse, including selective encoding of onset and offset transients, tonic firing, and sustained suppression. Spike train classification methods revealed that many neurons robustly encoded tone duration despite substantial diversity in the encoding process. Excellent discrimination performance was achieved by neurons whose responses were primarily phasic at tone offset and by those that responded robustly while the tone persisted. Although diverse cortical response patterns converged on effective duration discrimination, this diversity significantly constrained the utility of decoding models referenced to a spiking pattern averaged across all responses or averaged within the same response category. Using maximum likelihood-based decoding models, we demonstrated that the spike train recorded in a single trial could support direct estimation of stimulus onset and offset. Comparisons between different decoding models established the substantial contribution of bursts of activity at sound onset and offset to demarcating the temporal boundaries of gated tones. Our results indicate that relatively few neurons suffice to provide temporally precise estimates of such auditory "edges," particularly for models that assume and exploit the heterogeneity of neural responses in awake cortex.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Categorization Of Cortical Response Typesmentioning

confidence: 99%

mentioning

confidence: 99%

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Diverse cortical codes for scene segmentation in primate auditory cortex

Malone

Scott

Semple

2015

Journal of Neurophysiology

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Extracellular voltage thresholds for maximizing information extraction in primate auditory cortex: implications for a brain computer interface

Bigelow

Malone

2021

J. Neural Eng.

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Objective. Research by Oby (2016 J. Neural. Eng. 13 036009) demonstrated that the optimal threshold for extracting information from visual and motor cortices may differ from the optimal threshold for identifying single neurons via spike sorting methods. The optimal threshold for extracting information from auditory cortex has yet to be identified, nor has the optimal temporal scale for representing auditory cortical activity. Here, we describe a procedure to jointly optimize the extracellular threshold and bin size with respect to the decoding accuracy achieved by a linear classifier for a diverse set of auditory stimuli. Approach. We used linear multichannel arrays to record extracellular neural activity from the auditory cortex of awake squirrel monkeys passively listening to both simple and complex sounds. We executed a grid search of the coordinate space defined by the voltage threshold (in units of standard deviation) and the bin size (in units of milliseconds), and computed decoding accuracy at each point. Main results. The optimal threshold for information extraction was consistently near two standard deviations below the voltage trace mean, which falls significantly below the range of three to five standard deviations typically used as inputs to spike sorting algorithms in basic research and in brain-computer interface (BCI) applications. The optimal binwidth was minimized at the optimal voltage threshold, particularly for acoustic stimuli dominated by temporally dynamic features, indicating that permissive thresholding permits readout of cortical responses with temporal precision on the order of a few milliseconds. Significance. The improvements in decoding accuracy we observed for optimal readout parameters suggest that standard thresholding methods substantially underestimate the information present in auditory cortical spiking patterns. The fact that optimal thresholds were relatively low indicates that local populations of cortical neurons exhibit high temporal coherence that could be leveraged in service of future auditory BCI applications.

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Experience-dependent coding of frequency-modulated trajectories by offsets in auditory cortex

Chong

Dunlap

Kacsoh

et al. 2019

Preprint

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Frequency modulations are an inherent feature of many behaviorally relevant sounds, including vocalizations and music. Changing trajectories in a sound's frequency often conveys meaningful information, which can be used to differentiate sound categories, as in the case of intonations in tonal languages. However, it is not clear what features of the neural responses in what parts of the auditory cortical pathway might be more important for conveying information about behaviorally relevant frequency modulations, and how these responses change with experience. Here we uncover tuning to subtle variations in frequency trajectories in mouse auditory cortex. Surprisingly, we found that auditory cortical responses could be modulated by variations in a pure tone trajectory as small as 1/24th of an octave. Offset spiking accounted for a significant portion of tuned responses to subtle frequency modulation. Offset responses that were present in the adult A2, but not those in Core auditory cortex, were plastic in a way that enhanced the representation of an acquired behaviorally relevant sound category, which we illustrate with the maternal mouse paradigm for natural communication sound learning. By using this ethologically inspired sound-feature tuning paradigm to drive auditory responses in higher-order neurons, our results demonstrate that auditory cortex can track much finer frequency modulations than previously appreciated, which allows A2 offset responses in particular to attune to the pitch trajectories that distinguish behaviorally relevant, natural sound categories. Frequency trajectory conveys meaning across many sounds, from environmental noises to vocalizations and music. In human speech, pitch modulation conveys expressive and linguistic meaning, especially in tonal languages [1][2][3][4]. In music, emotional meaning can be conveyed through pitch modulation [5]. In many species, such as birds, rodents, monkeys, dogs, and even some marine animals, the temporal features of a vocalization, including its pitch trajectory, vary by context, and can express the intention as well as identity of a vocalizer [6][7][8][9][10]. Intonation-sensitive brain areas have been reported in the human auditory cortex using subdural electrophysiology [11], and the ability to process and recognize pitch trajectory in vocalizations can be shaped by experience [12][13][14]. However, the temporal or spatial resolution of methods used in human studies are coarse compared to the rate at which neurons in the auditory system respond, as well as to the time scale of many intonations, leaving still unclear the neural mechanisms for tuning to the frequency modulations in natural sounds.Frequency modulation encoding at the single neuron level in Core auditory cortex has been studied with directional sweeps or long duration sinusoidal modulations [15][16][17][18][19][20]. Offset responses are particularly selective for linear sweep direction [21], while entrained periodic firing can encode the onsets of the multiple cycles in up to 20 Hz sinusoida...

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Encoding frequency contrast in primate auditory cortex

Cited by 10 publications

References 72 publications

Diverse cortical codes for scene segmentation in primate auditory cortex

Diverse cortical codes for scene segmentation in primate auditory cortex

Extracellular voltage thresholds for maximizing information extraction in primate auditory cortex: implications for a brain computer interface

Experience-dependent coding of frequency-modulated trajectories by offsets in auditory cortex

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