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Neuropeptide Regulation of Ion Channels and Food Intake
Published in Tian-Le Xu, Long-Jun Wu, Nonclassical Ion Channels in the Nervous System, 2021
Ion channels tightly control the neuronal activity through transmembrane ion flux for depolarization or hyperpolarization. Changes in the protein structure of ion channels can drastically change how the neuron responds to extracellular signals such as neuropeptides. For instance, dysfunctional KATP channels, possibly from high-fat diets, would be unable to close under elevated glucose levels, resulting in constitutively active inhibition of the anorexigenic POMC neurons, leading to the development of obesity (Parton et al. 2007). Mutations in KATP channels are also associated with congenital diabetes and hyperinsulinism (Tinker et al. 2018). Tonically elevated PIP3, a signaling molecule naturally activated by insulin, acts as a sexually dimorphic inhibitor of POMC neurons via stimulation of KATP channels. Female mice have a larger weight gain than males when PIP3 is perpetually elevated (Plum et al. 2006). Kir6.2 is a key pore-forming subunit of KATP channels (Miki et al. 2001). The defective Kir6.2 prevents ATP blockade of KATP channels, which results in increased food intake and obesity due to loss of glucose sensitivity of Kir6.2-expressing hypothalamic neurons (Sohn 2013; Miki et al. 2001). Kir6.2 knockout mice also showed a blunted hypothalamic response to glucose loading and elevated hypothalamic NPY expression accompanied by hyperphagia, while they are resistant to obesity (Park et al. 2011).
Functional Neurology
Published in James Crossley, Functional Exercise and Rehabilitation, 2021
The action of neurons is altered at various points throughout the nervous system, a process referred to as neuromodulation. Modulation can either excite (increase) or inhibit (decrease) levels of transmission between neurons. Both stable changes in neuronal activity and the formation of new neurons, known as neurogenesis, are considered the foundations for learning and memory.
Systematic Class of Oscillator-Based Architecture Types
Published in Harald Maurer, Cognitive Science, 2021
Based on the "Wilson-Cowan Network Oscillator Model", the German physicist and neurophysiologist Peter König and the German physicist and psychiatrist Thomas B. Schillen have developed the "Self-organization Neuronal Oscillator Model".444 This model simulates the experimental situation of temporal coding of object features by the synchronization of the oscillatory neuronal activity of cortical assemblies in neurophysiology. To solve the binding problem, the model consists of one or more feature modules with coupled nonlinear oscillators in visual scene analysis. The system dynamics are determined by the following differential equations (Schillen and König 1994, 1991a, König and Schillen 1991): where αe or αi is a damping constant, wei and wei is the coupling strength, τ the delay time, ie(t) the external stimulus input, F is a non-linear Fermi output function with a slope σ and a treshold Θ445:
Consciousness in a Rotor? Science and Ethics of Potentially Conscious Human Cerebral Organoids
Published in AJOB Neuroscience, 2023
Federico Zilio, Andrea Lavazza
Lacking sufficient data and since the afore-mentioned theories often refer to slightly different concepts of consciousness, it is not possible to choose one theory while excluding the others. So, one might identify the commonalities and overlaps among the theories and formulate an inference to the best explanation. As for the type of consciousness, it is reasonable to think that HCOs could show a basic form of sentience rather than other types of consciousness (high-order, self-awareness, etc.), due to their dimension and stage of development. Thus, some features that are likely to be necessarily associated (but not sufficiently) with the presence of consciousness could be identified: specific neurophysiological development and differentiation of neural structures, the formation of widespread functional connectivity, the production of complex, non-stereotyped intrinsic neural activity, and the capacity to encode stimuli from different timescales together, beyond mere responsiveness to input. Current HCOs do not possess all these characteristics yet, but it is plausible to think that HCOs endowed with all of them could be grown in the future. Therefore, a view that considers features of different theories might be helpful in informing the neuroethical debate on HCOs.
Ellen R. Grass Lecture: The Future of Neurodiagnostics and Emergence of a New Science
Published in The Neurodiagnostic Journal, 2023
Electroencephalography (EEG) is the original and oldest functional brain measurement technology. Although many seemingly more advanced brain imaging technologies have emerged as powerful research tools for neuroscience, EEG remains the most direct measurement of neural activity that we have. Some have even referred to EEG as a window into the mind itself (Nunez and Srinivasan 2006). Interpretation and analysis of EEG data is essentially an information processing exercise. Traditionally, electrical potentials generated by groups of pyramidal neurons in the cortex are measured by sensors on the scalp. The voltages are converted to traces on a moving strip of paper or now to digital numbers that represent the value of the voltage. In either case, human eyes review and analyze the time series traces for patterns that are indicative of specific brain states or pathological function. Increasingly, computer algorithms are supplementing human analysis by screening for certain features, such as artifacts, or other patterns that humans may then review. Several trends are accelerating the evolution of computer analysis of EEG recordings.
Alpha synchronisation of acoustic responses in active listening is indicative of native language listening experience
Published in International Journal of Audiology, 2022
Alyssa Dyball, Nan Xu Rattanasone, Ronny Ibrahim, Mridula Sharma
One way to analyse induced activities is by using Time-frequency analyses (TFA). TFA works by analysing the electrophysiological data into its component frequencies such as alpha, theta, beta and gamma components across time, to isolate time points at which the neuronal oscillatory activity is strongest (Klimesch et al. 2000). Current understanding of neural oscillations suggests that neural activity within specific wavelengths is reflective of different cognitive processes. Alpha for example is most closely linked to directed attention (Klimesch et al. 1990). The induced activity can also be described as being either relatively increased (event-related synchronised) or decreased (event-related desynchronised) (Oostenveld et al. 2011). For example, increased synchronisation in the alpha-band is thought to reflect increased inhibition while desynchronisation in the alpha-band is reportedly reflective of focussed attention (Klimesch et al., 2012). Thus, TFA can assist in providing information on how the brain orchestrates the processing of linguistically relevant acoustic information.