اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدOn the Theoretical Possibility of Quantum Visual Information Transfer to the Human Brain
420
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
The feasibility of wave function collapse in the human brain has been the subject of vigorous scientific debates since the advent of quantum theory. Scientists like Von Neumann, London, Bauer and Wigner (initially) believed that wave function collapse occurs in the brain or is caused by the mind of the observer. It is a legitimate question to ask how human brain can receive subtle external visual quantum information intact when it must pass through very noisy and complex pathways from the eye to the brain? There are several approaches to investigate information processing in the brain, each of which presents a different set of conclusions. Penrose and Hameroff have hypothesized that there is quantum information processing inside the human brain whose material substrate involves microtubules and consciousness is the result of a collective wavefunction collapse occurring in these structures. Conversely, Tegmark stated that owing to thermal decoherence there cannot be any quantum processing in neurons of the brain and processing in the brain must be classical for cognitive processes. However, Rosa and Faber presented an argument for a middle way which shows that none of the previous authors are completely right and despite the presence of decoherence, it is still possible to consider the brain to be a quantum system. Additionally, Thaheld, has concluded that quantum states of photons do collapse in the human eye and there is no possibility for collapse of visual quantum states in the brain and thus there is no possibility for the quantum state reduction in the brain. In this paper we conclude that if we accept the main essence of the above approaches taken together, each of them can provide a different part of a teleportation mechanism.
قيم البحث
اقرأ أيضاً
The development of the algorithm of a neural network building by the corresponding parts of a DNA code is discussed.
Supervised deep convolutional neural networks (DCNNs) are currently one of the best computational models that can explain how the primate ventral visual stream solves object recognition. However, embodied cognition has not been considered in the exis
ting visual processing models. From the ecological standpoint, humans learn to recognize objects by interacting with them, allowing better classification, specialization, and generalization. Here, we ask if computational models under the embodied learning framework can explain mechanisms underlying object recognition in the primate visual system better than the existing supervised models? To address this question, we use reinforcement learning to train neural network models to play a 3D computer game and we find that these reinforcement learning models achieve neural response prediction accuracy scores in the early visual areas (e.g., V1 and V2) in the levels that are comparable to those accomplished by the supervised neural network model. In contrast, the supervised neural network models yield better neural response predictions in the higher visual areas, compared to the reinforcement learning models. Our preliminary results suggest the future direction of visual neuroscience in which deep reinforcement learning should be included to fill the missing embodiment concept.
A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI) and infer properties about the white matter microstructure. However, a head-to-head comparison of DW-
MRI models is critically missing in the field. To address this deficiency, we organized the White Matter Modeling Challenge during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed at identifying the DW-MRI models that best predict unseen DW data. in vivo DW-MRI data was acquired on the Connectom scanner at the A.A.Martinos Center (Massachusetts General Hospital) using gradients strength of up to 300 mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three-quarters of the whole dataset, and their models were ranked on their ability to predict the remaining unseen quarter of data. In this paper we provide both an overview and a more in-depth description of each evaluated model, report the challenge results, and infer trends about the model characteristics that were associated with high model ranking. This work provides a much needed benchmark for DW-MRI models. The acquired data and model details for signal prediction evaluation are provided online to encourage a larger scale assessment of diffusion models in the future.
We develop a theoretical framework for the exploration of quantum mechanical coherent population transfer phenomena, with the ultimate goal of constructing faithful models of devices for classical and quantum information processing applications. We b
egin by outlining a general formalism for weak-field quantum optics in the Schr{o}dinger picture, and we include a general phenomenological representation of Lindblad decoherence mechanisms. We use this formalism to describe the interaction of a single stationary multilevel atom with one or more propagating classical or quantum laser fields, and we describe in detail several manifestations and applications of electromagnetically induced transparency. In addition to providing a clear description of the nonlinear optical characteristics of electromagnetically transparent systems that lead to ``ultraslow light, we verify that -- in principle -- a multi-particle atomic or molecular system could be used as either a low power optical switch or a quantum phase shifter. However, we demonstrate that the presence of significant dephasing effects destroys the induced transparency, and that increasing the number of particles weakly interacting with the probe field only reduces the nonlinearity further. Finally, a detailed calculation of the relative quantum phase induced by a system of atoms on a superposition of spatially distinct Fock states predicts that a significant quasi-Kerr nonlinearity and a low entropy cannot be simultaneously achieved in the presence of arbitrary spontaneous emission rates. Within our model, we identify the constraints that need to be met for this system to act as a one-qubit and a two-qubit conditional phase gate.
We study the impact of the brain tractography false positives in the brain connectivity graphs. The representative input database for the analysis is the set of tractograms from the participants on the ISMRM-2015 Tractography Challenge. We propose 2
novel metrics to rank the quality of a tractogram when it is compared with known ground truth. The results of this study indicate that the estimation of graph communities is robust to high levels of overestimation in the connectivity.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
