Eating behaviours are influenced by the integration of gustatory, olfactory and somatosensory signals, which all contribute to the perception of flavour. Although extensive research has explored the neural correlates ...
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Eating behaviours are influenced by the integration of gustatory, olfactory and somatosensory signals, which all contribute to the perception of flavour. Although extensive research has explored the neural correlates of taste in the gustatory cortex (GC), less is known about its role in encoding thermal information. This study investigates the encoding of oral thermal and chemosensory signals by GC neurons compared to the oral somatosensory cortex. In this study we recorded the spiking activity of more than 900 GC neurons and 500 neurons from the oral somatosensory cortex in mice allowed to freely lick small drops of gustatory stimuli or deionized water at varying non-nociceptive temperatures. We then developed and used a Bayesian-based analysis technique to assess neural classification scores based on spike rate and phase timing within the lick cycle. Our results indicate that GC neurons rely predominantly on rate information, although phase information is needed to achieve maximum accuracy, to effectively encode both chemosensory and thermosensory signals. GC neurons can effectively differentiate between thermal stimuli, excelling in distinguishing both large contrasts (14 vs. 36 degrees C) and, although less effectively, more subtle temperature differences. Finally a direct comparison of the decoding accuracy of thermosensory signals between the two cortices reveals that whereas the somatosensory cortex exhibited higher overall accuracy, the GC still encodes significant thermosensory information. These findings highlight the GC's dual role in processing taste and temperature, emphasizing the importance of considering temperature in future studies of taste processing.
In subjects implanted with intracranial electrodes, we use two different stories involving the same person (or place) to evaluate whether and to what extent context modulates human single-neuron responses. Nearly all ...
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In subjects implanted with intracranial electrodes, we use two different stories involving the same person (or place) to evaluate whether and to what extent context modulates human single-neuron responses. Nearly all neurons (97% during encoding and 100% during recall) initially responding to a person/place do not modulate their response with context. Likewise, nearly none (<1%) of the initially non-responsive neurons show conjunctive coding, responding to particular persons/places in a particular context during the tasks. In line with these findings, taking all neurons together it is possible to decode the person/place being depicted in each story, but not the particular story. Moreover, the neurons show consistent results across encoding and recall of the stories and during passive viewing of pictures. These results suggest a context invariant, non-conjunctive coding of memories at the single-neuron level in the human hippocampus and amygdala, in contrast to what has been described in other species.
Frontotemporal dementia (FTD) is the second most prevalent form of presenile dementia. Patients with FTD show prominent chemosensory symptoms such as abnormal detection and recognition thresholds for various gustatory...
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Frontotemporal dementia (FTD) is the second most prevalent form of presenile dementia. Patients with FTD show prominent chemosensory symptoms such as abnormal detection and recognition thresholds for various gustatory stimuli. The chemosensory symptoms of FTD may be related to damage of the gustatory insular cortex (GC) as the insular cortex is one of the primary targets in FTD disease progression. Little is known about how circuitry changes in GC lead to deficits in taste processing in FTD. Here we tested the hypothesis that gustatory deficits are present in a mouse model of FTD, and that they are related to abnormal patterns of neural activity in GC. We behaviorally evaluated a transgenic FTD mouse model overexpressing human TDP-43 with a Q331K mutation (TDP-43Q331K) in a brief access test and a taste-based two alternative forced choice (2AFC) task probing the ability to discriminate sucrose/NaCl mixtures. TDP-43Q331K mice showed abnormal sucrose consumption and an impaired ability to discriminate taste mixtures compared to non-transgenic control mice. To assess deficits in GC taste processing, we relied on electrophysiological recordings using chronically implanted tetrodes in alert TDP43Q331K and non-transgenic control mice. The proportion of taste-selective neurons in TDP-43Q331K mice decreased over time compared to control mice. Similarly, encoding of chemosensory information and processing of taste palatability were impaired in TDP-43Q331K mice compared to control mice. Overall, these results demonstrate taste-related symptoms in a mouse model of FTD and provide evidence for altered taste processing in GC of TDP-43Q331K mice compared to control mice.
The increasing administrative workload across industries significantly impacts efficiency, productivity, and resource allocation. This study explores the transformative potential of artificial intelligence (AI) in aut...
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The increasing administrative workload across industries significantly impacts efficiency, productivity, and resource allocation. This study explores the transformative potential of artificial intelligence (AI) in automating administrative processes, with a particular focus on healthcare, education, and finance. By leveraging technologies such as natural language processing (NLP), machine learning (ML), and robotic process automation (RPA), AI can streamline workflows, enhance data accuracy, and reduce human error. Key applications discussed include intelligent document processing, automated form filling, and task scheduling, which collectively minimize repetitive tasks and enable professionals to prioritize strategic decision-making and client interactions. By synthesizing cross-industry AI applications, identifying key governance challenges, and proposing a comprehensive policy roadmap, this study advances AI and policy discourses with actionable strategies for ethically scaling AI-driven automation in administrative workflows.
When the dimensionality of a neural circuit is substantially larger than the dimensionality of the variable it encodes, many different degenerate network states can produce the same output. In this review I will discu...
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When the dimensionality of a neural circuit is substantially larger than the dimensionality of the variable it encodes, many different degenerate network states can produce the same output. In this review I will discuss three different neural systems that are linked by this theme. The pyloric network of the lobster, the song control system of the zebra finch, and the odor encoding system of the locust, while different in design, all contain degeneracies between their internal parameters and the outputs they encode. Indeed, although the dynamics of song generation and odor identification are quite different, computationally, odor recognition can be thought of as running the song generation circuitry backwards. In both of these systems, degeneracy plays a vital role in mapping a sparse neural representation devoid of correlations onto external stimuli (odors or song structure) that are strongly correlated. I argue that degeneracy between input and output states is an inherent feature of many neural systems, which can be exploited as a fault-tolerant method of reliably learning, generating, and discriminating closely related patterns.
Hippocampal place cells have single, bell-shaped place fields in small environments. Recent experiments, however, reveal that, in large environments, place cells have multiple fields with heterogeneous shapes and size...
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Hippocampal place cells have single, bell-shaped place fields in small environments. Recent experiments, however, reveal that, in large environments, place cells have multiple fields with heterogeneous shapes and sizes. We show that this diversity is explained by a surprisingly simple mathematical model, in which place fields are generated by thresholding a realization of a random Gaussian process. The model captures the statistics of field arrangements and generates new quantitative predictions about the statistics of field shapes and topologies. These predictions are quantitatively verified in bats and rodents, in one, two, and three dimensions, in both small and large environments. These results imply that common mechanisms underlie the diverse statistics observed in different experiments and further suggest that synaptic projections to CA1 are predominantly random.
In the history of audio and acoustic signal processing, perceptual audio coding has certainly excelled as a bright success story by its ubiquitous deployment in virtually all digital media devices, such as computers, ...
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In rabbit depressor nerve fibers, an on-off firing pattern, period-1 firing, and integer multiple firing with quiescent state were observed as the static pressure level was increased. A bursting pattern with bursts at...
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In rabbit depressor nerve fibers, an on-off firing pattern, period-1 firing, and integer multiple firing with quiescent state were observed as the static pressure level was increased. A bursting pattern with bursts at the systolic phase of blood pressure, continuous firing, and bursting with burst at diastolic phase and quiescent state at systolic phase were observed as the mean level of the dynamic blood pressure was increased. For both static and dynamic pressures, the firing frequency of the first two firing patterns increased and of the last firing pattern decreased due to the quiescent state. If the quiescent state is disregarded, the spike frequency becomes an increasing trend. The instantaneous spike frequency of the systolic phase bursting, continuous firing, and diastolic phase bursting can reflect the temporal process of the systolic phase, whole procedure, and diastolic phase of the dynamic blood pressure signal, respectively. With increasing the static current corresponding to pressure level, the deterministic Hodgkin-Huxley (HH) model manifests a process from a resting state first to period-1 firing via a subcritical Hopf bifurcation and then to a resting state via a supercritical Hopf bifurcation, and the firing frequency increases. The on-off firing and integer multiple firing were here identified as noise-induced firing patterns near the subcritical and supercritical Hopf bifurcation points, respectively, using the stochastic HH model. The systolic phase bursting and diastolic phase bursting were identified as pressure-induced firings near the subcritical and supercritical Hopf bifurcation points, respectively, using an HH model with a dynamic signal. The firing, spike frequency, and instantaneous spike frequency observed in the experiment were simulated and explained using HH models. The results illustrate the dynamics of different firing patterns and the frequency and temporal coding mechanisms of aortic baroreceptor.
Studying neural coding through neural energy is a novel view. In this paper, based on previously proposed single neuron model, the correlation between the energy consumption and the parameters of the cortex networks (...
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Studying neural coding through neural energy is a novel view. In this paper, based on previously proposed single neuron model, the correlation between the energy consumption and the parameters of the cortex networks (amount of neurons, coupling strength, and transform delay) under an oscillational condition were researched. We found that energy distribution varies orderly as these parameters change, and it is closely related to the synchronous oscillation of the neural network. Besides, we compared this method with traditional method of relative coefficient, which shows energy method works equal to or better than the traditional one. It is novel that the synchronous activity and neural network parameters could be researched by assessing energy distribution and consumption. Therefore, the conclusion of this paper will refine the framework of neural coding theory and contribute to our understanding of the coding mechanism of the cerebral cortex. It provides a strong theoretical foundation of a novel neural coding theory energy coding.
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