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Auditory deviance detection is a function of the auditory system that allows reduction of the processing demand for repetitive stimuli while stressing unpredictable ones, which are potentially more informative. Deviance detection has been extensively studied in humans using the oddball paradigm, which evokes an event-related potential known as mismatch negativity (MMN). The same stimulation paradigms are used in animal studies that aim to elucidate the neuronal mechanisms underlying deviance detection. In order to understand the circuitry responsible for deviance detection in the auditory cortex (AC), it is necessary to determine the properties of excitatory and inhibitory neurons separately. Measuring the spike widths of neurons recorded extracellularly from the anaesthetized rat AC, we classified them as fast spiking or regular spiking units. These two neuron types are generally considered as putative inhibitory or excitatory, respectively. In response to an oddball paradigm, we found that both types of units showed similar amounts of deviance detection overall. When considering each AC field separately, we found that only in A1 fast spiking neurons showed higher deviance detection levels than regular spiking neurons, while in the rest of the fields there was no such distinction. Interpreting these responses in the context of the predictive coding framework, we found that the responses of both types of units reflect mainly prediction error signaling (i.e., genuine deviance detection) rather than repetition suppression.
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http://dx.doi.org/10.1016/j.heares.2020.107997 | DOI Listing |
Front Neural Circuits
September 2025
Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
Introduction: Understanding how neural networks process complex patterns of information is crucial for advancing both neuroscience and artificial intelligence. To investigate fundamental principles of neural computation, we examined whether dissociated neuronal cultures, one of the most primitive living neural networks, exhibit regularity sensitivity beyond mere stimulus-specific adaptation and deviance detection.
Methods: We recorded activity to oddball electrical stimulation paradigms from dissociated rat cortical neurons cultured on high-resolution CMOS microelectrode arrays.
bioRxiv
August 2025
RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, Department of Psychology, University of Oslo, 0373 Oslo, Norway.
Humans extract regularities from the environment to form expectations that guide perception and optimize behavior. Although the prefrontal cortex (PFC) is central to this process, the relative contributions of orbitofrontal (OFC) and lateral PFC (LPFC) remain unclear. Here, we show that the brain tracks sound regularities in an auditory deviance detection task to predict when a target deviant will occur.
View Article and Find Full Text PDFPNAS Nexus
August 2025
Department of Entomology, The Connecticut Agricultural Experiment Station, 123 Huntington St., New Haven, CT 06511, USA.
Predicting and projecting risk of West Nile virus (WNV) to humans in areas without mosquito surveillance data is a key limitation of many WNV surveillance programs. To better inform risk of WNV, we analyzed 20 years (2001-2020) of point-level mosquito surveillance data from Connecticut (CT), United States, using machine learning methods to determine the most informative weather variables and land cover classes associated with monthly collections and WNV detections in mosquitoes. All training models were assessed based on explained deviance, root mean square error, and parsimony of included variables then optimized using a backward selection process.
View Article and Find Full Text PDFbioRxiv
August 2025
Center for Neural Science, New York University, New York, 10003, NY, United States.
The brain continuously generates predictions about the external world. When stimulus X is presented repeatedly, the brain predicts that the next one is also X. A deviant stimulus Y elicits a stronger sensory response than the baseline, reflecting the amplification of an unexpected stimulus.
View Article and Find Full Text PDFAnn N Y Acad Sci
August 2025
Cognitive Brain Research Unit, Department of Psychology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Musical training has been associated with enhanced auditory processing, including superior preattentive sound discrimination. However, the neural mechanisms underlying these enhancements remain unclear. This study used electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to investigate auditory deviance detection in musically trained and untrained 16-20-year-old participants.
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