Explore the vast range of titles available.

The present study builds on our previous study within the framework of Wyer and Collin's comprehension–elaboration theory of humor processing. In this study, an attempt is made to segregate the neural substrates of incongruity detection and incongruity resolution during the comprehension of verbal jokes. Although a number of fMRI studies have investigated the incongruity-resolution process, the differential neurological substrates of comprehension are still not fully understood. The present study utilized an event-related fMRI design incorporating three conditions (unfunny, nonsensical and funny) to examine distinct brain regions associated with the detection and resolution of incongruities. Stimuli in the unfunny condition contained no incongruities; stimuli in the nonsensical condition contained irresolvable incongruities; and stimuli in the funny condition contained resolvable incongruities. The results showed that the detection of incongruities was associated with greater activation in the right middle temporal gyrus and right medial frontal gyrus, and the resolution of incongruities with greater activation in the left superior frontal gyrus and left inferior parietal lobule. Further analysis based on participants' rating scores provided converging results. Our findings suggest a three-stage neural circuit model of verbal humor processing: incongruity detection and incongruity resolution during humor comprehension and inducement of the feeling of amusement during humor elaboration. ► This study sought to isolate the neural substrates underlying humor comprehension. ► Incongruity detection and resolution were segregated using a ‘nonsensical’ condition. ► Detection activated the right middle temporal gyrus and right medial frontal gyrus. ► Resolution activated the left superior frontal gyrus and left inferior parietal lobe. ► A neural circuit model of humor comprehension and elaboration processing is proposed.

Distinct inhibitory cell types participate in cognitively relevant nested brain rhythms, and particular changes in such rhythms are known to occur in disease states. Specifically, the coexpression of theta and gamma rhythms in the hippocampus is believed to represent a general coding scheme, but cellular-based generation mechanisms for these coupled rhythms are currently unclear. We develop a population rate model of the CA1 hippocampus that encompasses circuits of three inhibitory cell types (bistratified cells and parvalbumin [PV]-expressing and cholecystokinin [CCK]-expressing basket cells) and pyramidal cells to examine this. We constrain parameters and perform numerical and theoretical analyses. The theory, in combination with the numerical explorations, predicts circuit motifs and specific cell-type mechanisms that are essential for the coexistence of theta and gamma oscillations. We find that CCK-expressing basket cells initiate the coupled rhythms and regularize theta, and PV-expressing basket cells enhance both theta and gamma rhythms. Pyramidal and bistratified cells govern the generation of theta rhythms, and PV-expressing basket and pyramidal cells play dominant roles in controlling theta frequencies. Our circuit motifs for the theta-gamma coupled rhythm generation could be applicable to other brain regions. There are many different types of inhibitory cells in our brains that are differentially affected in disease. Concomitantly, coupled rhythms change in particular ways with disease. To help understand cell-type-specific changes in coupled rhythms, we develop a mathematical network model that is both respective of the cell type and also amenable to analyses. We focus on theta-gamma coupled rhythms in the hippocampus and include three different inhibitory cell types in our model circuits. By combining a theoretical analysis and numerical explorations, we find distinct contributions of these inhibitory cell types to coupled rhythms and predict motifs that are essential for the expression of theta-gamma coupled rhythms. Moving forward, we can leverage our model insights to help unravel cell-type contributions in disease states.

Inhibitory control plays an important role in controlling behaviors, and its impairment is a characteristic feature of schizophrenia. Such inhibitory control has been examined through the the stop-signal task, wherein participants are asked to suppress a planned movement when a stop signal appears. In this research, we constructed a two-layer spiking neural circuit model to study how N-methyl-D-aspartate receptor (NMDAR) hypofunction, a potential pathological mechanism in schizophrenia, impacts the inhibitory control ability in the stop-signal task. To find the possible NMDAR hypofunction effects in schizophrenia, all NMDA-mediated synapses in the model were set to be NMDAR hypofunction at different levels. Our findings revealed that the performances of the stop-signal task were close to the experimental results in schizophrenia when NMDAR hypofunction was present in the neurons of two populations that controlled the “go” process and the “stop” process of the stop-signal task, implying that the execution and inhibition of behaviors were both impaired in schizophrenia. Under a certain degree of NMDAR hypofunction, the circuit model is able to replicate the stop-signal task performances observed in individuals with schizophrenia. In addition, we have observed a predictable outcome indicating that NMDAR hypofunction can lower the saccadic threshold in the stop-signal task. These results provide a mechanical explanation for the impairment of inhibitory control in schizophrenia.

Bridging brain-scale circuit dynamics and organism-scale behavior is a central challenge in neuroscience. It requires the concurrent development of minimal behavioral and neural circuit models that can quantitatively capture basic sensorimotor operations. Here, we focus on light-seeking navigation in zebrafish larvae. Using a virtual reality assay, we first characterize how motor and visual stimulation sequences govern the selection of discrete swim-bout events that subserve the fish navigation in the presence of a distant light source. These mechanisms are combined into a comprehensive Markov-chain model of navigation that quantitatively predicts the stationary distribution of the fish’s body orientation under any given illumination profile. We then map this behavioral description onto a neuronal model of the ARTR, a small neural circuit involved in the orientation-selection of swim bouts. We demonstrate that this visually-biased decision-making circuit can capture the statistics of both spontaneous and contrast-driven navigation. All animals with the ability to move use sensory signals to help them navigate towards areas that seem better than their current location. Such areas might contain desirable things like food and mates, or they might allow an animal to escape from threats such as predators. But how the brain gives rise to this navigation behavior is unclear. Karpenko et al. have now obtained insights into the underlying mechanism by studying a behavior in zebrafish larvae called phototaxis. Phototaxis is the tendency to move in response to light. The advantage of using zebrafish larvae to study this behavior is that their brains are small and semi-transparent. This makes it possible to record the activity of almost every neuron. As a result, an individual’s brain activity can be mapped on to their behavior more precisely than in most other species. To probe how visual cues influence fish behavior, Karpenko et al. exposed individual fish to a carefully controlled virtual light source and then tracked their movements with a camera. The fish used two strategies to move towards the light. They selected their next movement based partly on the difference in the amount of light reaching each of their eyes, and partly on the change in overall brightness with each swim movement. Karpenko et al. used this information to build a numerical model of fish phototaxis, and to show how a simple brain circuit could generate this behavior. Species whose brains differ in size and structure may nevertheless develop similar strategies to perform similar tasks. By quantifying a generic behavior in a simple animal model, this study could provide insights into comparable behaviors in other species. In addition, the study suggests a simple mechanism for how animals select actions on the basis of sensory signals, which may also be relevant to other species and other tasks.

The Hodgkin and Huxley equations have served as the benchmark model in electrophysiology since 1950s. But it suffers from four major drawbacks. Firstly, it is only phenomenological not mechanistic. Secondly, it fails to exhibit the all-or-nothing firing mechanism for action potential generation. Thirdly, it lacks a theory for ion channel opening and closing activation across the cell membrane. Fourthly, it does not count for the phenomenon of voltage-gating which is vitally important for action potential generation. In this paper, a mathematical model for excitable membranes is constructed by introducing circuit characteristics for ion pump exchange, ion channel activation, and voltage-gating. It is demonstrated that the model is capable of re-establishing the Nernst resting potentials, explicitly exhibiting the all-or-nothing firing mechanism, and most important of all, filling the long-lasting theoretical gap by a unified theory on ion channel activation and voltage-gating. It is also demonstrated that the new model has one half fewer parameters but fits significantly better to experiment than the HH model does. The new model can be considered as an alternative template for neurons and excitable membranes when one looks for simpler models for mathematical studies and for forming large networks with fewer parameters.

Beside the promising application potential of nanotechnologies in engineering, the use of nanomaterials in medicine is growing. New therapies employing innovative nanocarrier systems to increase specificity and efficacy of drug delivery schemes are already in clinical trials. However the influence of the nanoparticles themselves is still unknown in medical applications, especially for complex interactions in neural systems. The aim of this study was to investigate in vitro effects of coated silver nanoparticles (cAgNP) on the excitability of single neuronal cells and to integrate those findings into an in silico model to predict possible effects on neuronal circuits. We first performed patch clamp measurements to investigate the effects of nanosized silver particles, surrounded by an organic coating, on excitability of single cells. We then determined which parameters were altered by exposure to those nanoparticles using the Hodgkin-Huxley model of the sodium current. As a third step, we integrated those findings into a well-defined neuronal circuit of thalamocortical interactions to predict possible changes in network signaling due to the applied cAgNP, in silico. We observed rapid suppression of sodium currents after exposure to cAgNP in our in vitro recordings. In numerical simulations of sodium currents we identified the parameters likely affected by cAgNP. We then examined the effects of such changes on the activity of networks. In silico network modeling indicated effects of local cAgNP application on firing patterns in all neurons in the circuit. Our sodium current simulation shows that suppression of sodium currents by cAgNP results primarily by a reduction in the amplitude of the current. The network simulation shows that locally cAgNP-induced changes result in changes in network activity in the entire network, indicating that local application of cAgNP may influence the activity throughout the network.

Background The neuromuscular junction (NMJ) is the synapse between motor neurons and skeletal muscle and controlls movement. Impaired synaptic transmission and NMJ degeneration has been observed during healthy ageing and is also implicated in several neuromuscular diseases. On account of the high energy demands of being distally located and large sized, NMJs are enriched with mitochondria. This enrichment is dependent on transport of mitochondria across the axon to the NMJ. Methods We first established a human 3D neuromuscular assembloid model to study in-vitro NMJs, by fusing human stem cell derived spinal cord organoids and primary skeletal muscle spheroids. To determine whether enhancing axonal mitochondrial transport modulates NMJ formation and maintenance, we generated a CRISPR-Cas9 meditated knockout of syntaphilin in human stem cells. Results Firstly, we characterised the neuromuscular assembloid model which showed functional innervated NMJs as measured by juxtaposed neurofilament + axons and α-bungarotoxin + acetylcholine receptors. Secondly, we showed that spinal cord selective genetic ablation of syntaphilin – an axonally localised mitochondrial anchor protein – resulted in increased mitochondrial motility in motor neurons, and consequently increased axonal density and NMJ formation. Conclusion This proof-of-concept study demonstrated that enhancing mitochondrial mobility could provide a therapeutic target to prevent NMJ degeneration.

Retinal ganglion cells (RGCs) are neurons that transmit visual information from the retina to the brain. Their degeneration, as seen in glaucoma and other optic neuropathies, leads to irreversible vision loss. As mature human RGCs are difficult to access, most of their studies rely on rodent models, which do not fully recapitulate human retinal biology. Human pluripotent stem cells (hPSCs) provide a promising source for generating RGCs in vitro, supporting disease modelling, drug screening, and future cell replacement therapies. This review outlines key markers that define RGC identity, maturation stages, and subtype diversity. We summarise recent advances in the differentiation of hPSCs towards RGCs, their functional characterisation, and their applications in disease modelling, drug screening, and transplantation.

A biophysical explanation of the reduced excitability in neurons exposed to a constant magnetic field is based on an extended neuronal membrane model. In the presence of a constant magnetic field, reduced excitability is manifested as an increase in the excitation threshold and a decrease in the frequency of action potentials. The proposed explanation for the reduced excitability rests on the well-known Hall effect. The separation of charges resulting from the Lorentz force exerted on moving intracellular ions leads to the formation of a Hall electric field in a direction perpendicular to that of action-potential transmission. Consequently, the ion current for discharging the membrane capacitance is reduced in the presence of a magnetic field, thereby limiting initiation of the action potential. The validity of the proposed biophysical explanation is justified analytically and verified by simulations based on the Hodgkin and Huxley model for the electrical excitability of a neuron. Based on derivation of the current segregation ratio α characterizing the reduction in the stimulating current from first principles, the equivalent circuit model of the neuronal membrane is extended to account for the reduced excitability of neurons exposed to a constant magnetic field.

\"Neuromorphic electronic engineering takes its inspiration from the functioning of nervous systems to build more power efficient electronic sensors and processors. Event-based neuromorphic systems are inspired by the brain's efficient data-driven communication design, which is key to its quick responses and remarkable capabilities. This cross-disciplinary text establishes how circuit building blocks are combined in architectures to construct complete systems. These include vision and auditory sensors as well as neuronal processing and learning circuits that implement models of nervous systems.Techniques for building multi-chip scalable systems are considered throughout the book, including methods for dealing with transistor mismatch, extensive discussions of communication and interfacing, and making systems that operate in the real world. The book also provides historical context that helps relate the architectures and circuits to each other and that guides readers to the extensive literature. Chapters are written by founding experts and have been extensively edited for overall coherence.This pioneering text is an indispensable resource for practicing neuromorphic electronic engineers, advanced electrical engineering and computer science students and researchers interested in neuromorphic systems.Key features: Summarises the latest design approaches, applications, and future challenges in the field of neuromorphic engineering. Presents examples of practical applications of neuromorphic design principles. Covers address-event communication, retinas, cochleas, locomotion, learning theory, neurons, synapses, floating gate circuits, hardware and software infrastructure, algorithms, and future challenges\"--