scholarly journals Decoding Complex Sounds Using Broadband Population Recordings from Secondary Auditory Cortex of Macaques

2019 ◽  
Author(s):  
Christopher Heelan ◽  
Jihun Lee ◽  
Ronan O’Shea ◽  
David M. Brandman ◽  
Wilson Truccolo ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Superior Temporal Gyrus ◽  
General Purpose ◽  
Neural Population ◽  
Neural Decoding ◽  
Population Activity ◽  
Complex Sounds ◽  
General Purpose Processor ◽  
Spatially Distributed ◽  
Encoding Strategies

AbstractDirect electronic communication with sensory areas of the neocortex is a challenging ambition for brain-computer interfaces. Here, we report the first successful neural decoding of English words with high intelligibility from intracortical spike-based neural population activity recorded from the secondary auditory cortex of macaques. We acquired 96-channel full-broadband population recordings using intracortical microelectrode arrays in the rostral and caudal parabelt regions of the superior temporal gyrus (STG). We leveraged a new neural processing toolkit to investigate the choice of decoding algorithm, neural preprocessing, audio representation, channel count, and array location on neural decoding performance. The results illuminated a view of the auditory cortex as a spatially distributed network and a general purpose processor of complex sounds. The presented spike-based machine learning neural decoding approach may further be useful in informing future encoding strategies to deliver direct auditory percepts to the brain as specific patterns of microstimulation.

scholarly journals Decoding speech from spike-based neural population recordings in secondary auditory cortex of non-human primates

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Christopher Heelan ◽  
Jihun Lee ◽  
Ronan O’Shea ◽  
Laurie Lynch ◽  
David M. Brandman ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Electronic Communication ◽  
Microelectrode Arrays ◽  
Superior Temporal Gyrus ◽  
Neural Population ◽  
Neural Decoding ◽  
Population Activity ◽  
Computer Interfaces ◽  
Encoding Strategies ◽  
The Brain

AbstractDirect electronic communication with sensory areas of the neocortex is a challenging ambition for brain-computer interfaces. Here, we report the first successful neural decoding of English words with high intelligibility from intracortical spike-based neural population activity recorded from the secondary auditory cortex of macaques. We acquired 96-channel full-broadband population recordings using intracortical microelectrode arrays in the rostral and caudal parabelt regions of the superior temporal gyrus (STG). We leveraged a new neural processing toolkit to investigate the choice of decoding algorithm, neural preprocessing, audio representation, channel count, and array location on neural decoding performance. The presented spike-based machine learning neural decoding approach may further be useful in informing future encoding strategies to deliver direct auditory percepts to the brain as specific patterns of microstimulation.

Sound frequency representation in primary auditory cortex is level tolerant for moderately loud, complex sounds

2011 ◽  
Vol 106 (2) ◽  
pp. 1016-1027 ◽  
Author(s):  
Martin Pienkowski ◽  
Jos J. Eggermont
Keyword(s):  
Auditory Cortex ◽  
Field Potential ◽  
Primary Auditory Cortex ◽  
Sound Level ◽  
Sound Frequency ◽  
Frequency Resolution ◽  
Neural Population ◽  
Complex Sounds ◽  
Individual Unit ◽  
Nonlinear Processing

The distribution of neuronal characteristic frequencies over the area of primary auditory cortex (AI) roughly reflects the tonotopic organization of the cochlea. However, because the area of AI activated by any given sound frequency increases erratically with sound level, it has generally been proposed that frequency is represented in AI not with a rate-place code but with some more complex, distributed code. Here, on the basis of both spike and local field potential (LFP) recordings in the anesthetized cat, we show that the tonotopic representation in AI is much more level tolerant when mapped with spectrotemporally dense tone pip ensembles rather than with individually presented tone pips. That is, we show that the tuning properties of individual unit and LFP responses are less variable with sound level under dense compared with sparse stimulation, and that the spatial frequency resolution achieved by the AI neural population at moderate stimulus levels (65 dB SPL) is better with densely than with sparsely presented sounds. This implies that nonlinear processing in the central auditory system can compensate (in part) for the level-dependent coding of sound frequency in the cochlea, and suggests that there may be a functional role for the cortical tonotopic map in the representation of complex sounds.

scholarly journals Influence of Context and Behavior on Stimulus Reconstruction From Neural Activity in Primary Auditory Cortex

2009 ◽  
Vol 102 (6) ◽  
pp. 3329-3339 ◽  
Author(s):  
Nima Mesgarani ◽  
Stephen V. David ◽  
Jonathan B. Fritz ◽  
Shihab A. Shamma
Keyword(s):  
Auditory Cortex ◽  
Cortical Neurons ◽  
Receptive Fields ◽  
Primary Auditory Cortex ◽  
Neural Population ◽  
Stimulus Context ◽  
Sensory Stimuli ◽  
Complex Sounds ◽  
Population Responses ◽  
Stimulus Reconstruction

Population responses of cortical neurons encode considerable details about sensory stimuli, and the encoded information is likely to change with stimulus context and behavioral conditions. The details of encoding are difficult to discern across large sets of single neuron data because of the complexity of naturally occurring stimulus features and cortical receptive fields. To overcome this problem, we used the method of stimulus reconstruction to study how complex sounds are encoded in primary auditory cortex (AI). This method uses a linear spectro-temporal model to map neural population responses to an estimate of the stimulus spectrogram, thereby enabling a direct comparison between the original stimulus and its reconstruction. By assessing the fidelity of such reconstructions from responses to modulated noise stimuli, we estimated the range over which AI neurons can faithfully encode spectro-temporal features. For stimuli containing statistical regularities (typical of those found in complex natural sounds), we found that knowledge of these regularities substantially improves reconstruction accuracy over reconstructions that do not take advantage of this prior knowledge. Finally, contrasting stimulus reconstructions under different behavioral states showed a novel view of the rapid changes in spectro-temporal response properties induced by attentional and motivational state.

Neural Population Coding of Natural Sounds in Non-flying Mammals

2017 ◽  
Author(s):  
Israel Nelken
Keyword(s):  
Auditory Cortex ◽  
Auditory System ◽  
Cortical Neurons ◽  
Dominant Role ◽  
Primary Auditory Cortex ◽  
Population Coding ◽  
Neural Population ◽  
Good Effect ◽  
Natural Sounds ◽  
Complex Sounds

Understanding the principles by which sensory systems represent natural stimuli is one of the holy grails of neuroscience. In the auditory system, the study of the coding of natural sounds has a particular prominence. Indeed, the relationships between neural responses to simple stimuli (usually pure tone bursts)—often used to characterize auditory neurons—and complex sounds (in particular natural sounds) may be complex. Many different classes of natural sounds have been used to study the auditory system. Sound families that researchers have used to good effect in this endeavor include human speech, species-specific vocalizations, an “acoustic biotope” selected in one way or another, and sets of artificial sounds that mimic important features of natural sounds. Peripheral and brainstem representations of natural sounds are relatively well understood. The properties of the peripheral auditory system play a dominant role, and further processing occurs mostly within the frequency channels determined by these properties. At the level of the inferior colliculus, the highest brainstem station, representational complexity increases substantially due to the convergence of multiple processing streams. Undoubtedly, the most explored part of the auditory system, in term of responses to natural sounds, is the primary auditory cortex. In spite of over 50 years of research, there is still no commonly accepted view of the nature of the population code for natural sounds in the auditory cortex. Neurons in the auditory cortex are believed by some to be primarily linear spectro-temporal filters, by others to respond to conjunctions of important sound features, or even to encode perceptual concepts such as “auditory objects.” Whatever the exact mechanism is, many studies consistently report a substantial increase in the variability of the response patterns of cortical neurons to natural sounds. The generation of such variation may be the main contribution of auditory cortex to the coding of natural sounds.

scholarly journals Coding of repetitive transients by auditory cortex on posterolateral superior temporal gyrus in humans: an intracranial electrophysiology study

2013 ◽  
Vol 109 (5) ◽  
pp. 1283-1295 ◽  
Author(s):  
Kirill V. Nourski ◽  
John F. Brugge ◽  
Richard A. Reale ◽  
Christopher K. Kovach ◽  
Hiroyuki Oya ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Superior Temporal Gyrus ◽  
Auditory Responses ◽  
Neurosurgical Patients ◽  
Spatially Distributed ◽  
High Gamma ◽  
Click Train ◽  
Stimulus Features ◽  
Surrounding Areas ◽  
Insert Earphones

Evidence regarding the functional subdivisions of human auditory cortex has been slow to converge on a definite model. In part, this reflects inadequacies of current understanding of how the cortex represents temporal information in acoustic signals. To address this, we investigated spatiotemporal properties of auditory responses in human posterolateral superior temporal (PLST) gyrus to acoustic click-train stimuli using intracranial recordings from neurosurgical patients. Subjects were patients undergoing chronic invasive monitoring for refractory epilepsy. The subjects listened passively to acoustic click-train stimuli of varying durations (160 or 1,000 ms) and rates (4–200 Hz), delivered diotically via insert earphones. Multicontact subdural grids placed over the perisylvian cortex recorded intracranial electrocorticographic responses from PLST and surrounding areas. Analyses focused on averaged evoked potentials (AEPs) and high gamma (70–150 Hz) event-related band power (ERBP). Responses to click trains featured prominent AEP waveforms and increases in ERBP. The magnitude of AEPs and ERBP typically increased with click rate. Superimposed on the AEPs were frequency-following responses (FFRs), most prominent at 50-Hz click rates but still detectable at stimulus rates up to 200 Hz. Loci with the largest high gamma responses on PLST were often different from those sites that exhibited the strongest FFRs. The data indicate that responses of non-core auditory cortex of PLST represent temporal stimulus features in multiple ways. These include an isomorphic representation of periodicity (as measured by the FFR), a representation based on increases in non-phase-locked activity (as measured by high gamma ERBP), and spatially distributed patterns of activity.

scholarly journals Efficient decoding of large-scale neural population responses with Gaussian-process multiclass regression

2021 ◽  
Author(s):  
C. Daniel Greenidge ◽  
Benjamin Scholl ◽  
Jacob Yates ◽  
Jonathan W. Pillow
Keyword(s):  
Gaussian Process ◽  
Large Scale ◽  
Multinomial Logistic Regression ◽  
Neural Population ◽  
High Dimensional ◽  
Neural Decoding ◽  
Population Activity ◽  
Population Codes ◽  
Low Dimensional ◽  
Dimensional Variable

Neural decoding methods provide a powerful tool for quantifying the information content of neural population codes and the limits imposed by correlations in neural activity. However, standard decoding methods are prone to overfitting and scale poorly to high-dimensional settings. Here, we introduce a novel decoding method to overcome these limitations. Our approach, the Gaussian process multi-class decoder (GPMD), is well-suited to decoding a continuous low-dimensional variable from high-dimensional population activity, and provides a platform for assessing the importance of correlations in neural population codes. The GPMD is a multinomial logistic regression model with a Gaussian process prior over the decoding weights. The prior includes hyperparameters that govern the smoothness of each neuron's decoding weights, allowing automatic pruning of uninformative neurons during inference. We provide a variational inference method for fitting the GPMD to data, which scales to hundreds or thousands of neurons and performs well even in datasets with more neurons than trials. We apply the GPMD to recordings from primary visual cortex in three different species: monkey, ferret, and mouse. Our decoder achieves state-of-the-art accuracy on all three datasets, and substantially outperforms independent Bayesian decoding, showing that knowledge of the correlation structure is essential for optimal decoding in all three species.

scholarly journals Dimensionality reduction for neural population decoding

2021 ◽  
Author(s):  
Charles R Heller ◽  
Stephen V David
Keyword(s):  
Dimensionality Reduction ◽  
Large Scale ◽  
Population Coding ◽  
Neural Population ◽  
Neural Decoding ◽  
Population Activity ◽  
Neural Recordings ◽  
Analytical Tools ◽  
Population Decoding

Rapidly developing technology for large scale neural recordings has allowed researchers to measure the activity of hundreds to thousands of neurons at single cell resolution in vivo. Neural decoding analyses are a widely used tool used for investigating what information is represented in this complex, high-dimensional neural population activity. Most population decoding methods assume that correlated activity between neurons has been estimated accurately. In practice, this requires large amounts of data, both across observations and across neurons. Unfortunately, most experiments are fundamentally constrained by practical variables that limit the number of times the neural population can be observed under a single stimulus and/or behavior condition. Therefore, new analytical tools are required to study neural population coding while taking into account these limitations. Here, we present a simple and interpretable method for dimensionality reduction that allows neural decoding metrics to be calculated reliably, even when experimental trial numbers are limited. We illustrate the method using simulations and compare its performance to standard approaches for dimensionality reduction and decoding by applying it to single-unit electrophysiological data collected from auditory cortex.

Design and Implementation of Low Energy Wireless Network Nodes based on Hardware Compression Acceleration

2019 ◽  
Vol 12 ◽  
Author(s):  
Hui Yang ◽  
Anand Nayyar
Keyword(s):  
Energy Consumption ◽  
Data Compression ◽  
Energy Saving ◽  
Optimization Design ◽  
Hardware Acceleration ◽  
Transmission Efficiency ◽  
General Purpose ◽  
Storage Space ◽  
General Purpose Processor ◽  
Compression Time

: In the fast development of information, the information data is increasing in geometric multiples, and the speed of information transmission and storage space are required to be higher. In order to reduce the use of storage space and further improve the transmission efficiency of data, data need to be compressed. processing. In the process of data compression, it is very important to ensure the lossless nature of data, and lossless data compression algorithms appear. The gradual optimization design of the algorithm can often achieve the energy-saving optimization of data compression. Similarly, The effect of energy saving can also be obtained by improving the hardware structure of node. In this paper, a new structure is designed for sensor node, which adopts hardware acceleration, and the data compression module is separated from the node microprocessor.On the basis of the ASIC design of the algorithm, by introducing hardware acceleration, the energy consumption of the compressed data was successfully reduced, and the proportion of energy consumption and compression time saved by the general-purpose processor was as high as 98.4 % and 95.8 %, respectively. It greatly reduces the compression time and energy consumption.

Tonotopic representation of missing fundamental complex sounds in the human auditory cortex

2003 ◽  
Vol 18 (2) ◽  
pp. 432-440 ◽  
Author(s):  
Takako Fujioka ◽  
Bernhard Ross ◽  
Hidehiko Okamoto ◽  
Yasuyuki Takeshima ◽  
Ryusuke Kakigi ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Complex Sounds ◽  
Human Auditory Cortex ◽  
Missing Fundamental

Temporal Encoding of the Voice Onset Time Phonetic Parameter by Field Potentials Recorded Directly From Human Auditory Cortex

1999 ◽  
Vol 82 (5) ◽  
pp. 2346-2357 ◽  
Author(s):  
Mitchell Steinschneider ◽  
Igor O. Volkov ◽  
M. Daniel Noh ◽  
P. Charles Garell ◽  
Matthew A. Howard
Keyword(s):  
Auditory Cortex ◽  
Voice Onset Time ◽  
Onset Time ◽  
Stop Consonant ◽  
Superior Temporal Gyrus ◽  
Response Patterns ◽  
Stop Consonants ◽  
Field Potentials ◽  
Heschl’S Gyrus ◽  
Heschl's Gyrus

Voice onset time (VOT) is an important parameter of speech that denotes the time interval between consonant onset and the onset of low-frequency periodicity generated by rhythmic vocal cord vibration. Voiced stop consonants (/b/, /g/, and /d/) in syllable initial position are characterized by short VOTs, whereas unvoiced stop consonants (/p/, /k/, and t/) contain prolonged VOTs. As the VOT is increased in incremental steps, perception rapidly changes from a voiced stop consonant to an unvoiced consonant at an interval of 20–40 ms. This abrupt change in consonant identification is an example of categorical speech perception and is a central feature of phonetic discrimination. This study tested the hypothesis that VOT is represented within auditory cortex by transient responses time-locked to consonant and voicing onset. Auditory evoked potentials (AEPs) elicited by stop consonant-vowel (CV) syllables were recorded directly from Heschl's gyrus, the planum temporale, and the superior temporal gyrus in three patients undergoing evaluation for surgical remediation of medically intractable epilepsy. Voiced CV syllables elicited a triphasic sequence of field potentials within Heschl's gyrus. AEPs evoked by unvoiced CV syllables contained additional response components time-locked to voicing onset. Syllables with a VOT of 40, 60, or 80 ms evoked components time-locked to consonant release and voicing onset. In contrast, the syllable with a VOT of 20 ms evoked a markedly diminished response to voicing onset and elicited an AEP very similar in morphology to that evoked by the syllable with a 0-ms VOT. Similar response features were observed in the AEPs evoked by click trains. In this case, there was a marked decrease in amplitude of the transient response to the second click in trains with interpulse intervals of 20–25 ms. Speech-evoked AEPs recorded from the posterior superior temporal gyrus lateral to Heschl's gyrus displayed comparable response features, whereas field potentials recorded from three locations in the planum temporale did not contain components time-locked to voicing onset. This study demonstrates that VOT at least partially is represented in primary and specific secondary auditory cortical fields by synchronized activity time-locked to consonant release and voicing onset. Furthermore, AEPs exhibit features that may facilitate categorical perception of stop consonants, and these response patterns appear to be based on temporal processing limitations within auditory cortex. Demonstrations of similar speech-evoked response patterns in animals support a role for these experimental models in clarifying selected features of speech encoding.

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