Frequency Gradient with Respect to Temperature for Determination of Classification of the Phase Response Curve

The interesting task here is to study the frequency-current(f–I)curve and phase response curve(PRC),subject to neural temperature variations,because the f–I curve and PRC are important measurements to understand dynamical mechanisms of generation of neural oscillations,and the neural temperature is widely known to significantly affect the oscillations.Nevertheless,little is discussed about how the temperature affects the f–I curve and PRC.In this study,frequencies of the neural oscillations,modulated with the temperature variations,are quantified with an average of the PRC.The frequency gradient with respect to temperature derived here gives clear classifications of the PRC,regardless of the form.It is also indicated that frequency decreases with an increase in temperature,resulted from bifurcation switching of the saddle-homoclinic to the saddle-node on an invariant circle.

An Optimal Power-Law for Synchrony and Lognormally Synaptic Weighted Hub Networks

Details about the structure of a network model are revealed at the spontaneous spike activity level,in which the power-law of synchrony is optimized to that observed in the CA3 hippocampal slice cultures.The network model is subject to spike noise with exponentially distributed interspike intervals.The excitatory(E)and/or inhibitory(I)neurons interact through synapses whose weights show a log-normal distribution.The spike behavior observed in the network model with the appropriate log-normal distributed synaptic weights fits best to that observed in the experiment.The best-fit is then achieved with high activities of I neurons having a hub-like structure,in which the I neurons,subject to optimized spike noise,are intensively projected from low active E neurons.

Changes in Repetitive Firing Rate Related to Phase Response Curves for Andronov-Hopf Bifurcations

We study specific changes in repetitive firing in the two-dimensional Hindmarsh-Rose （2dHR） oscillatory sys- tem that undergoes a bifurcation transition from the supercritical Andronov-Hopf （All） type to the subcritical Andronov-Hopf （SAH） type. We identify dynamical mechanisms which are responsible for changes of the repeti- tive firing rate during the AH to SAH bifurcation transitions. These include frequency-shift functions in response to small perturbations of a timescale parameter, its multiplicative parameter, and an external input current in the 2dHR oscillatory system. The frequency-shift functions are explicitly represented as functions relating to the phase response curves （PRCs）. Then, we demonstrate that when the timescale is normal and relatively fast, the repetitive firing rate slightly increases and decreases respectively during the AH to SAH bifurcation transition with a change of the intrinsic parameter, whereas it decreases during the SAH to AH bifurcation transition with an increase in the timescale. By analyzing the three different frequency-shift functions, we show that such changes of the repetitive firing rate depend largely on changes of the PRC size. The PRC size for the SAH bifurcation shrinks to the PRC size for the AH bifurcation.

Phase Transition Study Meets Machine Learning

In recent years, machine learning(ML) techniques have emerged as powerful tools for studying many-body complex systems, and encompassing phase transitions in various domains of physics. This mini review provides a concise yet comprehensive examination of the advancements achieved in applying ML to investigate phase transitions, with a primary focus on those involved in nuclear matter studies.

On the Perturbation Correction Factor <i>p_cav</i>of the Markus Parallel-Plate Ion Chamber in Clinical Electron Beams

Purpose: All present dosimetry protocols recommend well-guarded parallelplate ion chambers for electron dosimetry. For the guard-less Markus chamber, an energy dependent fluence perturbation correction pcav is given. This perturbation correction was experimentally determined by van der Plaetsen by comparison of the read-out of a Markus and a NACP chamber, which was assumed to be “perturbation-free”. Aim of the present study is a Monte Carlo based reiteration of this experiment. Methods: Detailed models of four parallel-plate chambers (Roos, Markus, NACP and Advanced Markus) were designed using the Monte Carlo code EGSnrc and placed in a water phantom. For all chambers, the dose to the active volume filled with low density water was calculated for 13 clinical electron spectra (E0 = 6 - 21 MeV) and three energies of an Electra linear accelerator at the depth of maximum and at the reference depth under reference conditions. In all cases, the chamber’s reference point was positioned at the depth of measurement. Moreover, the dose to water DW was calculated in a small water voxel positioned at the same depth. Results: The calculated dose ratio DNACP/DMarkus, which according to van der Plaetsen reflects the fluence perturbation correction of the Markus chamber, deviates less from unity than the values given by van der Plaetsen, but exhibits similar energy dependence. The same holds for the dose ratios of the other well-guarded chambers. But, in comparison to water, the Markus chamber reveals the smallest overall perturbation correction which is nearly energy independent at both investigated depths. Conclusion: The simulations principally confirm the energy dependence of the dose ratio DNACP/DMarkus as published by van der Plaetsen. But, as shown by our simulations of the ratio DW/DMarkus, the conclusion drawn in all dosimetry protocols is questionable: in contrast to all well-guarded chambers, the guard-less Markus chamber reveals the smallest overall perturbation correction and also the smallest energy dependence.

Why Deep Neural Nets Cannot Ever Match Biological Intelligence and What To Do About It?

The recently introduced theory of practopoiesis offers an account on how adaptive intelligent systems are organized. According to that theory, biological agents adapt at three levels of organization and this structure applies also to our brains. This is referred to as tri-traversal theory of the organization of mind or for short, a T3-structure. To implement a similar T3-organization in an artificially intelligent agent, it is necessary to have multiple policies, as usually used as a concept in the theory of reinforcement learning. These policies have to form a hierarchy. We define adaptive practopoietic systems in terms of hierarchy of policies and calculate whether the total variety of behavior required by real-life conditions of an adult human can be satisfactorily accounted for by a traditional approach to artificial intelligence based on T2-agents, or whether a T3-agent is needed instead. We conclude that the complexity of real life can be dealt with appropriately only by a T3-agent. This means that the current approaches to artifidal intelligence, such as deep architectures of neural networks, will not suffice with fixed network architectures. Rather, they will need to be equipped with intelligent mechanisms that rapidly alter the architectures of those networks.