scholarly journals Abstract rules drive adaptation in the subcortical sensory pathway

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Alejandro Tabas ◽  
Glad Mihai ◽  
Stefan Kiebel ◽  
Robert Trampel ◽  
Katharina von Kriegstein
Keyword(s):  
Neural Coding ◽  
Sensory Input ◽  
Mechanistic Explanation ◽  
Auditory Pathway ◽  
Neural Representation ◽  
Adaptive Behaviour ◽  
Prior Beliefs ◽  
Local Statistics ◽  
Sensory Pathways ◽  
Sensory Pathway

The subcortical sensory pathways are the fundamental channels for mapping the outside world to our minds. Sensory pathways efficiently transmit information by adapting neural responses to the local statistics of the sensory input. The long-standing mechanistic explanation for this adaptive behaviour is that neural activity decreases with increasing regularities in the local statistics of the stimuli. An alternative account is that neural coding is directly driven by expectations of the sensory input. Here, we used abstract rules to manipulate expectations independently of local stimulus statistics. The ultra-high-field functional-MRI data show that abstract expectations can drive the response amplitude to tones in the human auditory pathway. These results provide first unambiguous evidence of abstract processing in a subcortical sensory pathway. They indicate that the neural representation of the outside world is altered by our prior beliefs even at initial points of the processing hierarchy.

scholarly journals Sound Repetition Rate in the Human Auditory Pathway: Representations in the Waveshape and Amplitude of fMRI Activation

2002 ◽  
Vol 88 (3) ◽  
pp. 1433-1450 ◽  
Author(s):  
Michael P. Harms ◽  
Jennifer R. Melcher
Keyword(s):  
Auditory Cortex ◽  
Inferior Colliculus ◽  
Repetition Rate ◽  
Neural Coding ◽  
Medial Geniculate Body ◽  
Auditory Pathway ◽  
Stream Segregation ◽  
Neural Representation ◽  
Systematic Change ◽  
Medial Geniculate

Sound repetition rate plays an important role in stream segregation, temporal pattern recognition, and the perception of successive sounds as either distinct or fused. This study was aimed at elucidating the neural coding of repetition rate and its perceptual correlates. We investigated the representations of rate in the auditory pathway of human listeners using functional magnetic resonance imaging (fMRI), an indicator of population neural activity. Stimuli were trains of noise bursts presented at rates ranging from low (1–2/s; each burst is perceptually distinct) to high (35/s; individual bursts are not distinguishable). There was a systematic change in the form of fMRI response rate-dependencies from midbrain to thalamus to cortex. In the inferior colliculus, response amplitude increased with increasing rate while response waveshape remained unchanged and sustained. In the medial geniculate body, increasing rate produced an increase in amplitude and a moderate change in waveshape at higher rates (from sustained to one showing a moderate peak just after train onset). In auditory cortex (Heschl's gyrus and the superior temporal gyrus), amplitude changed somewhat with rate, but a far more striking change occurred in response waveshape—low rates elicited a sustained response, whereas high rates elicited an unusual phasic response that included prominent peaks just after train onset and offset. The shift in cortical response waveshape from sustained to phasic with increasing rate corresponds to a perceptual shift from individually resolved bursts to fused bursts forming a continuous (but modulated) percept. Thus at high rates, a train forms a single perceptual “event,” the onset and offset of which are delimited by the on and off peaks of phasic cortical responses. While auditory cortex showed a clear, qualitative correlation between perception and response waveshape, the medial geniculate body showed less correlation (since there was less change in waveshape with rate), and the inferior colliculus showed no correlation at all. Overall, our results suggest a population neural representation of the beginning and the end of distinct perceptual events that is weak or absent in the inferior colliculus, begins to emerge in the medial geniculate body, and is robust in auditory cortex.

The mamillothalamic pathways: my first encounter with the thalamus

2017 ◽  
Author(s):  
Ray Guillery
Keyword(s):  
Sensory Input ◽  
Great Success ◽  
Cortical Lesions ◽  
Head Direction ◽  
Thalamic Nuclei ◽  
Sensory Pathways ◽  
Cortical Areas ◽  
Thalamic Relay ◽  
The Many

My thesis studies had stimulated an interest in the mamillothalamic pathways but also some puzzlement because we knew nothing about the nature of the messages passing along these pathways. Several laboratories were studying the thalamic relay of sensory pathways with great success during my post-doctoral years. Each sensory relay could be understood in terms of the appropriate sensory input, but we had no way of knowing the meaning of the mamillothalamic messages. I introduce these nuclei as an example of the many thalamic nuclei about whose input functions we still know little or nothing. Early clinical studies of mamillary lesions had suggested a role in memory formation, whereas evidence from cortical lesions suggested a role in emotional experiences. Studies of the smallest of the three nuclei forming these pathways then showed it to be concerned with sensing head direction, relevant but not sufficient for defining an animal’s position in space. More recent studies based on studies of cortical activity or cortical damage have provided a plethora of suggestions: as so often, the answers reported depend on the questions asked. That simple conclusion is relevant for all transthalamic pathways. The evidence introduced in Chapter 1, that thalamocortical messages have dual meanings, suggests that we need to rethink our questions. It may prove useful to look at the motor outputs of relevant cortical areas to get clues about some appropriate questions.

Neural Transformation of Dissonant Intervals in the Auditory Brainstem

2015 ◽  
Vol 32 (5) ◽  
pp. 445-459 ◽  
Author(s):  
Kyung Myun Lee ◽  
Erika Skoe ◽  
Nina Kraus ◽  
Richard Ashley
Keyword(s):  
Auditory System ◽  
Auditory Processing ◽  
Response Spectrum ◽  
Phase Locking ◽  
Auditory Pathway ◽  
Auditory Brainstem ◽  
Neural Representation ◽  
Auditory Brainstem Responses ◽  
Distortion Products ◽  
Stimulus Waveform

Acoustic periodicity is an important factor for discriminating consonant and dissonant intervals. While previous studies have found that the periodicity of musical intervals is temporally encoded by neural phase locking throughout the auditory system, how the nonlinearities of the auditory pathway influence the encoding of periodicity and how this effect is related to sensory consonance has been underexplored. By measuring human auditory brainstem responses (ABRs) to four diotically presented musical intervals with increasing degrees of dissonance, this study seeks to explicate how the subcortical auditory system transforms the neural representation of acoustic periodicity for consonant versus dissonant intervals. ABRs faithfully reflect neural activity in the brainstem synchronized to the stimulus while also capturing nonlinear aspects of auditory processing. Results show that for the most dissonant interval, which has a less periodic stimulus waveform than the most consonant interval, the aperiodicity of the stimulus is intensified in the subcortical response. The decreased periodicity of dissonant intervals is related to a larger number of nonlinearities (i.e., distortion products) in the response spectrum. Our findings suggest that the auditory system transforms the periodicity of dissonant intervals resulting in consonant and dissonant intervals becoming more distinct in the neural code than if they were to be processed by a linear auditory system.

scholarly journals Information limits on neural identification of colored surfaces in natural scenes

2004 ◽  
Vol 21 (3) ◽  
pp. 331-336 ◽  
Author(s):  
DAVID H. FOSTER ◽  
SÉRGIO M.C. NASCIMENTO ◽  
KINJIRO AMANO
Keyword(s):  
Neural Coding ◽  
Neural Representation ◽  
Natural Scenes ◽  
Surface Reflectance ◽  
Surface Color ◽  
Maximum Information ◽  
Information Theoretic ◽  
Urban Scenes ◽  
Rural And Urban ◽  
Information Theoretic Measures

If surfaces in a scene are to be distinguished by their color, their neural representation at some level should ideally vary little with the color of the illumination. Four possible neural codes were considered: von-Kries-scaled cone responses from single points in a scene, spatial ratios of cone responses produced by light reflected from pairs of points, and these quantities obtained with sharpened (opponent-cone) responses. The effectiveness of these codes in identifying surfaces was quantified by information-theoretic measures. Data were drawn from a sample of 25 rural and urban scenes imaged with a hyperspectral camera, which provided estimates of surface reflectance at 10-nm intervals at each of 1344 × 1024 pixels for each scene. In computer simulations, scenes were illuminated separately by daylights of correlated color temperatures 4000 K, 6500 K, and 25,000 K. Points were sampled randomly in each scene and identified according to each of the codes. It was found that the maximum information preserved under illuminant changes varied with the code, but for a particular code it was remarkably stable across the different scenes. The standard deviation over the 25 scenes was, on average, approximately 1 bit, suggesting that the neural coding of surface color can be optimized independent of location for any particular range of illuminants.

Local versus distributed: A poor taxonomy of neural coding strategies

2004 ◽  
Vol 27 (5) ◽  
pp. 700-702
Author(s):  
Michael W. Spratling
Keyword(s):  
Neural Coding ◽  
Neural Representation ◽  
Target Article ◽  
Coding Strategies

Page is to be congratulated for challenging some misconceptions about neural representation. However, his target article, and the commentaries to it, highlight that the terms “local” and “distributed” are open to misinterpretation. These terms provide a poor description of neural coding strategies and a better taxonomy might resolve some of the issues.

scholarly journals Modulation of the primary auditory thalamus when recognising speech in noise

2019 ◽  
Author(s):  
Paul Glad Mihai ◽  
Nadja Tschentscher ◽  
Katharina von Kriegstein
Keyword(s):  
Speech Signal ◽  
Medial Geniculate Body ◽  
Auditory Pathway ◽  
Constant Stimulus ◽  
Stimulus Properties ◽  
Bayesian Brain ◽  
Speech In Noise ◽  
Medial Geniculate ◽  
Sensory Pathway ◽  
Sensory Uncertainty

AbstractRecognising speech in background noise is a strenuous daily activity, yet most humans can master it. A mechanistic explanation of how the human brain deals with such sensory uncertainty is the Bayesian Brain Hypothesis. In this view, the brain uses a dynamic generative model to simulate the most likely trajectory of the speech signal. Such simulation account can explain why there is a task-dependent modulation of sensory pathway structures (i.e., the sensory thalami) for recognition tasks that require tracking of fast-varying stimulus properties (i.e., speech) in contrast to relatively constant stimulus properties (e.g., speaker identity) despite the same stimulus input. Here we test the specific hypothesis that this task-dependent modulation for speech recognition increases in parallel with the sensory uncertainty in the speech signal. In accordance with this hypothesis, we show—by using ultra-high-resolution functional magnetic resonance imaging in human participants—that the task-dependent modulation of the left primary sensory thalamus (ventral medial geniculate body, vMGB) for speech is particularly strong when recognizing speech in noisy listening conditions in contrast to situations where the speech signal is clear. Exploratory analyses showed that this finding was specific to the left vMGB; it was not present in the midbrain structure of the auditory pathway (left inferior colliculus, IC). The results imply that speech in noise recognition is supported by modifications at the level of the subcortical sensory pathway providing driving input to the auditory cortex.

scholarly journals Probabilistic representation in human visual cortex reflects uncertainty in serial decisions

2019 ◽  
Author(s):  
R.S. van Bergen ◽  
J.F.M. Jehee
Keyword(s):  
Visual Cortex ◽  
Neural Coding ◽  
Probability Distributions ◽  
Sensory Information ◽  
Population Level ◽  
Neural Representation ◽  
Serial Dependence ◽  
Population Activity ◽  
Human Visual Cortex ◽  
Sensory Uncertainty

AbstractHow does the brain represent the reliability of its sensory evidence? Here, we test whether sensory uncertainty is encoded in cortical population activity as the width of a probability distribution – a hypothesis that lies at the heart of Bayesian models of neural coding. We probe the neural representation of uncertainty by capitalizing on a well-known behavioral bias called serial dependence. Human observers of either sex reported the orientation of stimuli presented in sequence, while activity in visual cortex was measured with fMRI. We decoded probability distributions from population-level activity and found that serial dependence effects in behavior are consistent with a statistically advantageous sensory integration strategy, in which uncertain sensory information is given less weight. More fundamentally, our results suggest that probability distributions decoded from human visual cortex reflect the sensory uncertainty that observers rely on in their decisions, providing critical evidence for Bayesian theories of perception.

scholarly journals Learning shapes cortical dynamics to enhance integration of relevant sensory input

2021 ◽  
Author(s):  
Angus Chadwick ◽  
Adil Khan ◽  
Jasper Poort ◽  
Antonin Blot ◽  
Sonja Hofer ◽  
...  
Keyword(s):  
Neural Coding ◽  
Sensory Input ◽  
Temporal Dynamics ◽  
Network Dynamics ◽  
Sensory Information ◽  
Neural Population ◽  
Cortical Circuits ◽  
Population Activity ◽  
Cortical Dynamics ◽  
Network Output

Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity amongst neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input.

The pathways for perception

2017 ◽  
Author(s):  
Ray Guillery
Keyword(s):  
Action Potentials ◽  
Sensory Input ◽  
Sensory Receptors ◽  
Nerve Fibres ◽  
Standard View ◽  
Nerve Roots ◽  
Functional Roles ◽  
The World ◽  
Sensory Pathway ◽  
Dorsal Nerve

Chapter 2 outlines some of the evidence on which the seemingly strong standard view has been based. The early discovery that ventral nerve roots of the spinal cord provide a motor output and dorsal nerve roots provide a sensory input supported the dichotomy of the standard view. Then as each sensory pathway was traced to the thalamus for relay to the cortex, the separate inputs from the sensory receptors—visual, auditory, gustatory, and so on—could be seen as providing the cortex with a ‘view’ of the world. The nature of this view became strikingly clear once investigators could understand (read) the messages that pass along the nerve fibres on the basis of very brief changes in membrane potentials, the action potentials. However, many branches given off by sensory fibres on their way to the thalamus remain unexplained on the standard view. These are important for the integrative sensorimotor view and their precise functional roles need to be defined.

scholarly journals Representation of Dynamic Interaural Phase Difference in Auditory Cortex of Awake Rhesus Macaques

2009 ◽  
Vol 101 (4) ◽  
pp. 1781-1799 ◽  
Author(s):  
Brian H. Scott ◽  
Brian J. Malone ◽  
Malcolm N. Semple
Keyword(s):  
Auditory Cortex ◽  
Phase Difference ◽  
Rhesus Macaques ◽  
Discharge Rate ◽  
Auditory Pathway ◽  
Neural Representation ◽  
Characteristic Analysis ◽  
Binaural Cues ◽  
Interaural Phase ◽  
Interaural Phase Difference

Neurons in auditory cortex of awake primates are selective for the spatial location of a sound source, yet the neural representation of the binaural cues that underlie this tuning remains undefined. We examined this representation in 283 single neurons across the low-frequency auditory core in alert macaques, trained to discriminate binaural cues for sound azimuth. In response to binaural beat stimuli, which mimic acoustic motion by modulating the relative phase of a tone at the two ears, these neurons robustly modulate their discharge rate in response to this directional cue. In accordance with prior studies, the preferred interaural phase difference (IPD) of these neurons typically corresponds to azimuthal locations contralateral to the recorded hemisphere. Whereas binaural beats evoke only transient discharges in anesthetized cortex, neurons in awake cortex respond throughout the IPD cycle. In this regard, responses are consistent with observations at earlier stations of the auditory pathway. Discharge rate is a band-pass function of the frequency of IPD modulation in most neurons (73%), but both discharge rate and temporal synchrony are independent of the direction of phase modulation. When subjected to a receiver operator characteristic analysis, the responses of individual neurons are insufficient to account for the perceptual acuity of these macaques in an IPD discrimination task, suggesting the need for neural pooling at the cortical level.

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