Synthetic Brain

4. Ross Ashby: Psychiatry, Synthetic Brains, and Cybernetics

Memory and imagination

This paper is elaborate the model of humanoid robot interacts with human being and perform various operation as per the command given by the human being. A humanoid robot having Synthetic brain can able to do Interaction, communication, Object detection, information acquisition about any object, response to voice command, chatting logically with human beings. Object detection will be done by this robot for that purpose there is use image processing concept (HAAR Technique), And to make the system intelligent that is whenever system interact, communicate, chat with human it gives proper response, question / answers there is integrates artificial intelligence and DFA / NFA automata and Prolog language concept for answering logically over the complex and relevant strings or data.

Template estimation plays a crucial role in computational anatomy since it provides reference frames for performing statistical analysis of the underlying anatomical population variability. While building models for template estimation, variability in sites and image acquisition protocols need to be accounted for. To account for such variability, we propose a generative template estimation model that makes simultaneous inference of both bias fields in individual images, deformations for image registration, and variance hyperparameters. In contrast, existing maximum a posterori based methods need to rely on either bias-invariant similarity measures or robust image normalization. Results on synthetic and real brain MRI images demonstrate the capability of the model to capture heterogeneity in intensities and provide a reliable template estimation from registration.

Translational brain research using Magnetic Resonance Imaging (MRI) is becoming increasingly popular as animal models are an essential part of scientific studies and more ultra-high-field scanners are becoming available. Some disadvantages of MRI are the availability of MRI scanners and the time required for a full scanning session. Privacy laws and the 3Rs ethics rule also make it difficult to create large datasets for training deep learning models. To overcome these challenges, an adaptation of the alpha Generative Adversarial Networks (GANs) architecture was used to test its ability to generate realistic 3D MRI scans of the rat brain in silico. As far as the authors are aware, this was the first time a GAN-based approach was used to generate synthetic MRI data of the rat brain. The generated scans were evaluated using various quantitative metrics, a Turing test, and a segmentation test. The last two tests proved the realism and applicability of the generated scans to real problems. Therefore, by using the proposed new normalisation layer and loss functions, it was possible to improve the realism of the generated rat MRI scans, and it was shown that using the generated data improved the segmentation model more than using the conventional data augmentation.

Introduction Periorbital and midfacial filling with cosmetic injectables has become ubiquitous, with the primary targets being the central, anterior cheek and the medial tear trough. However, these may notbe themost importantareas to fill, and to achieve an excellent result requires a balanced strategy of injecting multiple areas rather than only 1 or 2 areas. I find it better to partially correct multiple shadow points on the face that impart aging rather than to try to ameliorate only 1 or 2 principal shadowsperfectly. I liken it to remodeling a house: If the bathroom is fixed up nicely, the kitchen now looks old. I notice the same thing in the face: If I fix the tear troughwell but not other surrounding shadows, I find that the result looks unbalancedor the individual simply does not look better. What I mean by better is the conclusion drawn by my synthetic right brain that reads whether someone looks more attractive rather that of my left brain, which tells mewhether a line or hole is properly filled (Figure 1). It is critical to engage both sides of the brain when designing the face, as illustrated in the Video.

Image registration methods based on Thirion’s demons method update displacement field by the image gradient obtained by integer order derivate. However, the fractional order derivate is superior to integral order derivate for computing image gradient under weak texture or smooth regions. To obtain high accurate image registration, we propose a new fractional order derivate based Log-Demons with driving force. We design a new fractional order derivate convolution mask based on Grunwald-Letnikov (GL) definition to get accurate image gradient. Then, we integrate fractional order derivate into Log-Demons with driving force. The experiments on synthetic and MRI brain images validate that the use of fractional order derivate to compute gradient not only improves the registration accuracy but also speeds up the registration process.

Diffusion coupling model based on neural oscillation synchronization theory, which can extract different targets with different gray levels from one, has been an important way for realizing image segmentation. This paper describes the diffusion coupling model and its algorithm and process for image segmentation. Simulation programs for supplementing this algorithm were compiled based on MATLAB platform. Experimental results on synthetic images, infrared images and brain magnetic resonance images show that, by setting reasonable algorithm parameters, the target image extraction and segmentation of different elements can be achieved. This image segmentation algorithm exhibits wide applications in medical image diagnosis, satellite image processing, traffic control, biometrics and others.

BackgroundBrain volume measurement serves as a critical approach for assessing brain health status. Considering the close biological connection between the eyes and brain, this study aims to investigate the feasibility of estimating brain volume through retinal fundus imaging integrated with clinical metadata, and to offer a cost-effective approach for assessing brain health.MethodsBased on clinical information, retinal fundus images, and neuroimaging data derived from a multicenter, population-based cohort study, the KaiLuan Study, we proposed a cross-modal correlation representation (CMCR) network to elucidate the intricate co-degenerative relationships between the eyes and brain for 755 subjects. Specifically, individual clinical information, which has been followed up for as long as 12 years, was encoded as a prompt to enhance the accuracy of brain volume estimation. Independent internal validation and external validation were performed to assess the robustness of the proposed model. Root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) metrics were employed to quantitatively evaluate the quality of synthetic brain images derived from retinal imaging data.ResultsThe proposed framework yielded average RMSE, PSNR, and SSIM values of 98.23, 35.78 dB, and 0.64, respectively, which significantly outperformed 5 other methods: multi-channel Variational Autoencoder (mcVAE), Pixel-to-Pixel (Pixel2pixel), transformer-based U-Net (TransUNet), multi-scale transformer network (MT-Net), and residual vision transformer (ResViT). The two- (2D) and three-dimensional (3D) visualization results showed that the shape and texture of the synthetic brain images generated by the proposed method most closely resembled those of actual brain images. Thus, the CMCR framework accurately captured the latent structural correlations between the fundus and the brain. The average difference between predicted and actual brain volumes was 61.36 cm3, with a relative error of 4.54%. When all of the clinical information (including age and sex, daily habits, cardiovascular factors, metabolic factors, and inflammatory factors) was encoded, the difference was decreased to 53.89 cm3, with a relative error of 3.98%. Based on the synthesized brain MR images from retinal fundus images, the volumes of brain tissues could be estimated with high accuracy.ConclusionsThis study provides an innovative, accurate, and cost-effective approach to characterize brain health status through readily accessible retinal fundus images.Trial registration No. NCT05453877 (https://clinicaltrials.gov/).Supplementary InformationThe online version contains supplementary material available at 10.1186/s40779-025-00630-2.

These days, many traditional end-user applications are said to “run fast enough” on existing machines, so the search continues for novel applications that can leverage the new capabilities of our evolving hardware. Foremost of these potential applications are those that are clustered around information processing capabilities that humans have today but are lacking in computers. The fact that brains can perform these computations serves as an existence proof that these applications are realizable. At the same time, we often discover that the human nervous system, with its 80 billion neurons, on some metrics, is more powerful and energy-efficient than today's machines. Both of these aspects make this class of applications a desirable target for an architectural benchmark suite, because there is evidence that these applications are both useful and computationally challenging. This paper details CortexSuite, a Synthetic Brain Benchmark Suite, which seeks to capture this workload. We classify and identify benchmarks within CortexSuite by analogy to the human neural processing function. We use the major lobes of the cerebral cortex as a model for the organization and classification of data processing algorithms. To be clear, our goal is not to emulate the brain at the level of the neuron, but rather to collect together synthetic, man-made algorithms that have similar function and have met with success in the real world. We consulted six world-class machine learning and computer vision researchers, who collectively hold 83,091 citations across their distinct subareas, asking them to identify newly emerging computationally-intensive algorithms or applications that are going to have a large impact over the next ten years. This is coupled with datasets that reflect the philosophy of practical use algorithms and are coded in “clean C” so as to make them accessible, analyzable, and usable for parallel and approximate compiler and architecture researchers alike.

Deep learning (DL) techniques, which do not need complex pre-processing and feature analysis, are used in many areas of medicine and achieve promising results. On the other hand, in medical studies, a limited dataset decreases the abstraction ability of the DL model. In this context, we aimed to produce synthetic brain images including three tumor types (glioma, meningioma, and pituitary), unlike traditional data augmentation methods, and classify them with DL. This study proposes a tumor classification model consisting of a Dense Convolutional Network (DenseNet121)-based DL model to prevent forgetting problems in deep networks and delay information flow between layers. By comparing models trained on two different datasets, we demonstrated the effect of synthetic images generated by Cycle Generative Adversarial Network (CycleGAN) on the generalization of DL. One model is trained only on the original dataset, while the other is trained on the combined dataset of synthetic and original images. Synthetic data generated by CycleGAN improved the best accuracy values for glioma, meningioma, and pituitary tumor classes from 0.9633, 0.9569, and 0.9904 to 0.9968, 0.9920, and 0.9952, respectively. The developed model using synthetic data obtained a higher accuracy value than the related studies in the literature. Additionally, except for pixel-level and affine transform data augmentation, synthetic data has been generated in the figshare brain dataset for the first time.

More than 30 million individuals worldwide are living with Alzheimer’s Disease. To further the current understanding on this neurodegenerative disease, we developed a technique to create amyloid peptide clusters in synthetic, brain-like membranes, which mimic the senile plaques found in the brains of Alzheimer's patients. I compared the molecular functioning of homotaurine, a peptic anti-aggregant that binds to amyloid peptides directly, and curcumin, a non-peptic molecule that can inhibit aggregation by changing membrane properties. By using microscopy, x-ray diffraction, and UV-vis spectroscopy, we found that both curcumin and homotaurine significantly reduce the number of small, nanoscopic amyloid aggregates and the corresponding β- and cross-β sheet signals. This research shows that membrane active drugs can be as efficient as peptide targeting drugs in inhibiting amyloid aggregation in-vitro [1]. The findings can open new pathways for the developments of drugs to slow down first occurrence and progression of the disease. [1] Xingyuan Zou, Sebastian Himbert, Janos Juhasz, Samantha Ros, Harald D. H. Stover, and Maikel C. Rheinstädter, “Curcumin and homotaurine suppress amyloid-b25-35 aggregation in synthetic brain membranes”, under review with ACS Chemical Neuroscience, Manuscript ID: cn-2021-00057r

In this paper, we propose an automated approach based on the geometric median shift algorithm over Riemannian manifolds, for the brain tumor detection and segmentation in magnetic resonance images (MRI). This approach is based on the geometric median, geodesic distance. We propose the median shift to overcome the limitation of mean which is not necessary a point in a set. The geodesic distance can describe data points distributed on a manifold, compared to the Euclidean distance, and produce efficient results for image analysis. Coupled with k-means algorithm, the proposed framework can cluster the brain image into tree regions (gray matter, white matter and cerebrospinal fluid) and abnormalities regions. We applied this approach to clustering the brain tissues and brain tumor segmentation, which is validated on a synthetic brain MRI. The obtained results using two datasets show the efficiency of the used algorithm validated qualitatively by the measurement of Dice Similarity Coefficient.

Alzheimer's disease is a neurodegenerative condition marked by the formation and aggregation of amyloid-β (Aβ) peptides. There exists, to this day, no cure or effective prevention for the disease; however, there is evidence that a healthy diet and certain food products can slow down first occurrence and progression. To investigate if food ingredients can interact with peptide aggregates, synthetic membranes that contained aggregates consisting of cross-β sheets of the membrane active fragment A areprepared. The impact of resveratrol, found in grapes, caffeine, the main active ingredient in coffee, β-carotene, found in orange fruits and vegetables, and epigallocatechin gallate (EGCG), a component of green tea, on the size and volume fraction of Aβ aggregates is studied using optical and fluorescence microscopy, X-ray diffraction, UV-vis spectroscopy, and molecular dynamics simulations. All compounds are membrane active and spontaneously partitioned in the synthetic brain membranes. While resveratrol and caffeine lead to membrane thickening and reduced membrane fluidity, β-carotene and EGCG preserve or increasefluidity. Resveratrol and caffeine do not reduce the volume fraction of peptide aggregates while β-carotene significantly reduces plaque size. Interestingly, EGCG dissolves peptide aggregates and significantly decreases the corresponding cross-β and β-sheet signals.

The reliability of relaxation time measures in synthetic magnetic resonance images (MRIs) of homemade phantoms were validated, and the diagnostic suitability of synthetic imaging was compared to that of conventional MRIs for detecting ischemic lesions. Phantoms filled with aqueous cupric-sulfate (CuSO4) were designed to mimic spin-lattice (T1) and spin-spin (T2) relaxation properties and were used to compare their accuracies and stabilities between synthetic and conventional scans of various brain tissues. To validate the accuracy of synthetic imaging in ischemic stroke diagnoses, the synthetic and clinical scans of 18 patients with ischemic stroke were compared, and the quantitative contrast-to-noise ratios (CNRs) were measured, using the Friedman test to determine significance in differences. Results using the phantoms showed no significant differences in the interday and intersession synthetic quantitative T1 and T2 values. However, between synthetic and referenced T1 and T2 values, differences were larger for longer relaxation times, showing that image intensities in synthetic scans are relatively inaccurate in the cerebrospinal fluid (CSF). Similarly, CNRs in CSF regions of stroke patients were significantly different on synthetic T2-weighted and T2-fluid-attenuated inversion recovery images. In contrast, differences in stroke lesions were insignificant between the two. Therefore, interday and intersession synthetic T1 and T2 values are highly reliable, and discrepancies in synthetic T1 and T2 relaxation times and image contrasts in CSF regions do not affect stroke lesion diagnoses. Additionally, quantitative relaxation times from synthetic images allow better estimations of ischemic stroke onset time, consequently increasing confidence in synthetic MRIs as diagnostic tools for ischemic stroke.