Choline Transporter regulates olfactory habituation via a neuronal triad of excitatory, inhibitory and mushroom body neurons

Choline is an essential component of Acetylcholine (ACh) biosynthesis pathway which requires high-affinity Choline transporter (ChT) for its uptake into the presynaptic terminals of cholinergic neurons. Previously, we had reported a predominant expression of ChT in memory processing and storing region of the Drosophila brain called mushroom bodies (MBs). It is unknown how ChT contributes to the functional principles of MB operation. Here, we demonstrate the role of ChT in Habituation, a non-associative form of learning. Odour driven habituation traces are laid down in ChT dependent manner in antennal lobes (AL), projection neurons (PNs), and MBs. We observed that reduced habituation due to knock-down of ChT in MBs causes hypersensitivity towards odour, suggesting that ChT also regulates incoming stimulus suppression. Importantly, we show for the first time that ChT is not unique to cholinergic neurons but is also required in inhibitory GABAergic neurons to drive habituation behaviour. Our results support a model in which ChT regulates both habituation and incoming stimuli through multiple circuit loci via an interplay between excitatory and inhibitory neurons. Strikingly, the lack of ChT in MBs shows characteristics similar to the major reported features of Autism spectrum disorders (ASD), including attenuated habituation, sensory hypersensitivity as well as defective GABAergic signalling. Our data establish the role of ChT in habituation and suggest that its dysfunction may contribute to neuropsychiatric disorders like ASD.

Context-Based Facilitation of Semantic Access Follows Both Logarithmic and Linear Functions of Stimulus Probability

Stimuli are easier to process when the preceding context (e.g., a sentence, in the case of a word) makes them predictable. However, it remains unclear whether context-based facilitation arises due to predictive preactivation of a limited set of relatively probable upcoming stimuli (with facilitation then linearly related to probability) or, instead, arises because the system maintains and updates a probability distribution across all items, as posited by accounts (e.g., surprisal theory) assuming a logarithmic function between predictability and processing effort. To adjudicate between these accounts, we measured the N400 component, an index of semantic access, evoked by sentence-final words of varying probability, including unpredictable words, which are never generated in human production norms. Word predictability was measured using both cloze probabilities and a state-of-the-art machine learning language model (GPT-2). We reanalyzed five datasets (n=138) to first demonstrate and then replicate that context-based facilitation on the N400 is graded and dissociates even among words with cloze probabilities at or near 0, as a function of very small differences in model-estimated predictability. Furthermore, we established that the relationship between word predictability and context-based facilitation on the N400 is neither purely linear nor purely logarithmic but instead combines both functions. We argue that such a composite function reveals properties of the mapping between words and semantic features and how feature- and word- related information is activated during on-line processing. Overall, the results provide powerful evidence for the role of internal models in shaping how the brain apprehends incoming stimulus information.

Choline Transporter regulates olfactory habituation via a neuronal triad of excitatory, inhibitory and mushroom body neurons

Choline is an essential component of Acetylcholine (ACh) biosynthesis pathway which requires high-affinity Choline transporter (ChT) for its uptake into the presynaptic terminals of cholinergic neurons. Previously, we reported a predominant expression of ChT in memory processing and storing region of Drosophila brain called mushroom bodies (MB). It is unknown how ChT contributes to the functional principles of MB operation. Here, we demonstrate the role of ChT in non-associative form of learning, Habituation. Odour driven habituation traces are laid down in ChT dependent manner in antennal lobes (AL), projection neurons (PN) and MB. We observed that reduced habituation due to knockdown of ChT in MB causes hypersensitivity towards odour, suggesting that ChT also regulates incoming stimulus suppression. Importantly, we show for the first time that ChT is not unique to cholinergic neurons but is also required in inhibitory GABAergic neurons to drive habituation behaviour. Our results support a model in which ChT regulates both habituation and incoming stimuli through multiple circuit loci via an interplay between excitatory and inhibitory neurons. Strikingly, the lack of ChT in MB recapitulates major features of Autism spectrum disorders (ASD) including attenuated habituation, sensory hypersensitivity as well as defective GABAergic signalling. Our data establish the role of ChT in habituation learning and suggest that its dysfunction may contribute to neuropsychiatric disorders like ASD.

Evaluating the Potential Gain of Auditory and Audiovisual Speech-Predictive Coding Using Deep Learning

Sensory processing is increasingly conceived in a predictive framework in which neurons would constantly process the error signal resulting from the comparison of expected and observed stimuli. Surprisingly, few data exist on the accuracy of predictions that can be computed in real sensory scenes. Here, we focus on the sensory processing of auditory and audiovisual speech. We propose a set of computational models based on artificial neural networks (mixing deep feedforward and convolutional networks), which are trained to predict future audio observations from present and past audio or audiovisual observations (i.e., including lip movements). Those predictions exploit purely local phonetic regularities with no explicit call to higher linguistic levels. Experiments are conducted on the multispeaker LibriSpeech audio speech database (around 100 hours) and on the NTCD-TIMIT audiovisual speech database (around 7 hours). They appear to be efficient in a short temporal range (25–50 ms), predicting 50% to 75% of the variance of the incoming stimulus, which could result in potentially saving up to three-quarters of the processing power. Then they quickly decrease and almost vanish after 250 ms. Adding information on the lips slightly improves predictions, with a 5% to 10% increase in explained variance. Interestingly the visual gain vanishes more slowly, and the gain is maximum for a delay of 75 ms between image and predicted sound.

Two distinct feedback codes in V1 for ‘real’ and ‘imaginary’ internal experiences

Visual illusions and visual imagery are conscious sensory events that lack a corresponding physical input. But while everyday mental imagery feels distinct from incoming stimulus input, visual illusions, like hallucinations, are under limited volitional control and appear indistinguishable from physical reality. Illusions are thought to arise from lower-level processes within sensory cortices. In contrast, imagery involves a wide network of brain areas that recruit early visual cortices for the sensory representation of the imagined stimulus. Here, we combine laminar fMRI brain imaging with psychophysical methods and multivariate pattern analysis to investigate in human participants how seemingly ‘real’ and imaginary non-physical experiences are processed in primary visual cortex (V1). We find that the content of mental imagery is only decodable in deep layers, whereas illusory content is only decodable at superficial depths. This suggests that feedback to the different layers may serve distinct functions: low-level feedback to superficial layers might be responsible for shaping perception-like experiences, while deep-layer feedback might serve the formation of a more malleable ‘inner’ world, separate from ongoing perception.

How predictive can be predictions in the neurocognitive processing of auditory and audiovisual speech? A deep learning study

Sensory processing is increasingly conceived in a predictive framework in which neurons would constantly process the error signal resulting from the comparison of expected and observed stimuli. Surprisingly, few data exist on the amount of predictions that can be computed in real sensory scenes. Here, we focus on the sensory processing of auditory and audiovisual speech. We propose a set of computational models based on artificial neural networks (mixing deep feed-forward and convolutional networks) which are trained to predict future audio observations from 25 ms to 250 ms past audio or audiovisual observations (i.e. including lip movements). Experiments are conducted on the multispeaker NTCD-TIMIT audiovisual speech database. Predictions are efficient in a short temporal range (25-50 ms), predicting 40 to 60 % of the variance of the incoming stimulus, which could result in potentially saving up to 2/3 of the processing power. Then they quickly decrease to vanish after 100 ms. Adding information on the lips slightly improves predictions, with a 5 to 10 % increase in explained variance.Interestingly the visual gain vanishes more slowly, and the gain is maximum for a delay of 75 ms between image and predicted sound.

I like coffee with cream anddog?Change in an implicit probabilistic representation captures meaning processing in the brain

The N400 component of the event-related brain potential has aroused much interest because it is thought to provide an online measure of meaning processing in the brain. Yet, the underlying process remains incompletely understood and actively debated. Here, we present a computationally explicit account of this process and the emerging representation of sentence meaning. We simulate N400 amplitudes as the change induced by an incoming stimulus in an implicit and probabilistic representation of meaning captured by the hidden unit activation pattern in a neural network model of sentence comprehension, and we propose that the process underlying the N400 also drives implicit learning in the network. The model provides a unified account of 16 distinct findings from the N400 literature and connects human language processing with successful deep learning approaches to language processing.

Efficient Information Contents Flow Down from Memory to Predict the Identity of Faces

In the sciences of cognition, an influential idea is that the brain makes predictions about incoming sensory information to reduce inherent ambiguity. In the visual hierarchy, this implies that information content originating in memory–the identity of a face–propagates down to disambiguate incoming stimulus information. However, understanding this powerful prediction-for-recognition mechanism will remain elusive until we uncover the content of the information propagating down from memory. Here, we address this foundational limitation with a task ubiquitous to humans–familiar face identification. We developed a unique computer graphics platform that combines a generative model of random face identity information with the subjectivity of perception. In 14 individual participants, we reverse engineered the predicted information contents propagating down from memory to identify 4 familiar faces. In a follow-up validation, we used the predicted face information to synthesize the identity of new faces and confirmed the causal role of the predictions in face identification. We show these predictions comprise both local 3D surface patches, such as a particularly thin and pointy nose combined with a square chin and a prominent brow, or more global surface characteristics, such as a longer or broader face. Further analyses reveal that the predicted contents are efficient because they represent objective features that maximally distinguish each identity from a model norm. Our results reveal the contents that propagate down the visual hierarchy from memory, showing this coding scheme is efficient and compatible with norm-based coding, with implications for mechanistic accounts of brain and machine intelligence.

Beda Makanan, Beda Kemampuan Perhatian : Studi Eksperimen tentang pengaruh Glycemic Index Caution Terhadap Kemampuan Deteksi Sinyal

In some nations have different eating patterns with other nations. There is consuming rice, sago, wheat and so on. Several studies have shown that meal consumption and types of food affect a person’s cognition process. One of them is attentions. Based on Jenkins’s study (1980) is known carbohydrate itself is divided by the speed of revamped into glucose in the body is divided into two, namely carbohydrates are quickly revamped into glucose or carbohydrate and high Glycemic index carbohydrates are slowly revamped into glucose or carbohydrate low glycemic index. When a person consumes carbohydrate at a rate of speed of revamped into a different glucose certainly it will affect the brain’s attention system. Attention is the ability to filter out some of the incoming stimulus of mental activity and focus on one the most important stimulus. Capability is very useful for human attention, because attention is the first gate of someone cognition processes. The research uses Crossover Experiment Design, and involves 20 graduate students as experiment subject. They are divided into 2 groups (high glycemic; rice, and low glycemic food; ubi). Subjects selected by controlling a few factor, such as, they has normal Intelligence, They has not severe disease and they are not overweight. To measure the attention ability used Attention Distraction. The results showed that the group fed high-glycemic carbohydrates (rice), have higher attention capacity compared with the provision of low glycemic carbohydrates (sweet potatoes). These results are consistent in the treatment of crossover.

THE USE OF NONPARAMETRIC METHODS OF STATIONARY POINT PROCESSES IN THE STUDY OF COMPLEX INTERACTIONS IN THE NEUROMUSCULAR SYSTEM

In this paper we study the complex interactions involved in the incoming stimulus, from a gamma (γ) and/or an alpha (α) motoneuron, and the outgoing response from the muscle spindle transmitted by the Ia sensory afferent neuron to the spinal cord. The most interesting case is the γ and α coactivation to the function of the muscle spindle, while the effect from a single (γ or α) motoneuron is also presented as a comparison. The mathematical background of this analysis is based on the theory of stationary point processes. A kernel type method of estimating second- and third-order conditional densities is used. Under certain conditions the asymptotic distributions of these estimates are Normal and the construction of 95% approximate confidence intervals is feasible. The application of these asymptotic results to the system of the muscle spindle enables us to detect and interpret its excitatory and/or inhibitory behavior.