Biological neural networks are well known for their capacity to process information with extremely low power consumption. Fields such as Artificial Intelligence, with high computational costs, are seeking for alternatives inspired in biological systems. An inspiring alternative is to implement hardware architectures that replicate the behavior of biological neurons but with the flexibility in programming capabilities of an electronic device, all combined with a relatively low operational cost. To advance in this quest, here we analyze the capacity of the HEENS hardware architecture to operate in a similar manner as an in vitro neuronal network grown in the laboratory. For that, we considered data of spontaneous activity in living neuronal cultures of about 400 neurons and compared their collective dynamics and functional behavior with those obtained from direct numerical simulations (in silico) and hardware implementations (in duris silico). The results show that HEENS is capable to mimic both the in vitro and in silico systems with high efficient-cost ratio, and on different network topological designs. Our work shows that compact low-cost hardware implementations are feasible, opening new avenues for future, highly efficient neuromorphic devices and advanced human-machine interfacing.

Cannabis use and cannabis use disorders have taken on a new social significance as a result of partial legalization. In 2021 a total of 4.5 million adults (8.8%) in Germany used the drug. The number of users as well as problematic use have risen in the last decade. Cannabis products with a high delta-9-tetrahydrocannabinol (THC) content and their regular use lead to changes in cannabinoid receptor distribution in the brain and to modifications in the structure and functionality of relevant neuronal networks. The consequences of cannabinoid use are particularly in the psychological functioning and can include intoxication, harmful use, dependence with withdrawal symptoms and cannabis-induced mental disorders. Changes in the diagnostics between ICD-10 and ICD-11 are presented. Interdisciplinary S3 guidelines on cannabis-related disorders are currently being developed and will be finalized shortly.
UNASSIGNED: Cannabiskonsum und Cannabiskonsumstörungen haben durch die Teillegalisierung eine neue gesellschaftliche Brisanz erreicht. Im Jahr 2021 nutzten 4,5 Mio. Erwachsene (8,8 %) in Deutschland die Droge. Die Zahl der Konsumenten und Konsumentinnen wie auch des problematischen Konsums ist angestiegen. Cannabisprodukte mit hohem δ‑9-Tetrahydrocannabinol(THC)-Gehalt sowie deren regelmäßiger Konsum führen zu Änderungen der Cannabinoidrezeptorverteilung im Gehirn und zu Modifikationen der Struktur und Funktionsfähigkeit relevanter neuronaler Netzwerke. Folgen des Konsums von Cannabinoiden liegen vor allem im psychischen Bereich und können Intoxikationen, schädlicher Gebrauch, Abhängigkeit mit Entzugssymptomen und cannabisinduzierte psychische Störungen sein. Änderungen der Diagnostik zwischen International Statistical Classification of Diseases and Related Health Problems 10 (ICD-10) und ICD-11 werden dargestellt. Eine interdisziplinäre S3-Leitinie „Cannabisbezogene Störungen“ wird aktuell entwickelt und in Kürze fertiggestellt.

Gi-coupled receptors, particularly cannabinoid receptors (CBRs), are considered perspective targets for treating brain pathologies, including epilepsy. However, the precise mechanism of the anticonvulsant effect of the CBR agonists remains unknown. We have found that WIN 55,212-2 (a CBR agonist) suppresses the synchronous oscillations of the intracellular concentration of Ca2+ ions (epileptiform activity) induced in the neurons of rat hippocampal neuron-glial cultures by bicuculline or NH4Cl. As we have demonstrated, the WIN 55,212-2 effect is mediated by CB1R receptors. The agonist suppresses Ca2+ inflow mediated by the voltage-gated calcium channels but does not alter the inflow mediated by NMDA, AMPA, and kainate receptors. We have also found that phospholipase C (PLC), protein kinase C (PKC), and G-protein-coupled inwardly rectifying K+ channels (GIRK channels) are involved in the molecular mechanism underlying the inhibitory action of CB1R activation against epileptiform activity. Thus, our results demonstrate that the antiepileptic action of CB1R agonists is mediated by different intracellular signaling cascades, including non-canonical PLC/PKC-associated pathways.

Biologically detailed models of brain circuitry are challenging to build and simulate due to the large number of neurons, their complex interactions, and the many unknown physiological parameters. Simplified mathematical models are more tractable, but harder to evaluate when too far removed from neuroanatomy/physiology. We propose that a multiscale model, coarse-grained (CG) while preserving local biological details, offers the best balance between biological realism and computability. This paper presents such a model. Generally, CG models focus on the interaction between groups of neurons-here termed \"pixels\"-rather than individual cells. In our case, dynamics are alternately updated at intra- and interpixel scales, with one informing the other, until convergence to equilibrium is achieved on both scales. An innovation is how we exploit the underlying biology: Taking advantage of the similarity in local anatomical structures across large regions of the cortex, we model intrapixel dynamics as a single dynamical system driven by \"external\" inputs. These inputs vary with events external to the pixel, but their ranges can be estimated a priori. Precomputing and tabulating all potential local responses speed up the updating procedure significantly compared to direct multiscale simulation. We illustrate our methodology using a model of the primate visual cortex. Except for local neuron-to-neuron variability (necessarily lost in any CG approximation) our model reproduces various features of large-scale network models at a tiny fraction of the computational cost. These include neuronal responses as a consequence of their orientation selectivity, a primary function of visual neurons.

UNASSIGNED: Hydronephrosis is essential in the diagnosis of renal colic. We automated the detection of hydronephrosis from ultrasound images to standardize the therapy and reduce the misdiagnosis of renal colic.
UNASSIGNED: Anonymously collected ultrasound images of human kidneys, both normal and hydronephrotic, were preprocessed for neural networks. Six \"state of the art\" models were trained and cross-validated for the detection of hydronephrosis, and two convolutional networks were used for kidney segmentation. In the testing phase, performance metrics included true positives, true negatives, false positives, false negatives, accuracy, and F1 score, while the evaluation of the segmentation task involved accuracy, precision, dice, jaccard, recall, and ASSD.
UNASSIGNED: A total of 523 sonographic kidney images (423 nonhydronephrotic and 100 hydronephrotic) were collected from three different ultrasound devices. After training on this dataset, all models were used to evaluate 200 new ultrasound kidney images (142 nonhydronephrotic and 58 hydronephrotic kidneys). The highest validation accuracy (98.5%) was achieved by the AlexNet model (GoogLeNet 97%, AlexNet_v2 96%, ResNet50 96%, ResNet101 97.5%, and ResNet152 95%). The deeplabv3_resnet50 and deeplabv3_resnet101 reached a dice coefficient of 94.74% and 94.48%, respectively, on the task of automated kidney segmentation. The study is limited by analyzing only hydronephrosis, but this specific focus enabled high detection accuracy.
UNASSIGNED: We show that our automated ultrasound deep learning model can be trained and used to interpret and segmentate ultrasound images from different sources with high accuracy. This method will serve as an automated tool in the diagnostic algorithm of acute renal failure in the future.
UNASSIGNED: Hydronephrosis is crucial in the diagnosis of renal colic. Recent advances in artificial intelligence allow automated detection of hydronephrosis in ultrasound images with high accuracy. These methods will help standardize the diagnosis and treatment renal colic.

This paper reviews Luria\'s model of the three functional units of the brain. To meet this objective, several issues were reviewed: the theory of functional systems and the contributions of phylogenesis and embryogenesis to the brain\'s functional organization. This review revealed several facts. In the first place, the relationship/integration of basic homeostatic needs with complex forms of behavior. Secondly, the multi-scale hierarchical and distributed organization of the brain and interactions between cells and systems. Thirdly, the phylogenetic role of exaptation, especially in basal ganglia and cerebellum expansion. Finally, the tripartite embryogenetic organization of the brain: rhinic, limbic/paralimbic, and supralimbic zones. Obviously, these principles of brain organization are in contradiction with attempts to establish separate functional brain units. The proposed new model is made up of two large integrated complexes: a primordial-limbic complex (Luria\'s Unit I) and a telencephalic-cortical complex (Luria\'s Units II and III). As a result, five functional units were delineated: Unit I. Primordial or preferential (brainstem), for life-support, behavioral modulation, and waking regulation; Unit II. Limbic and paralimbic systems, for emotions and hedonic evaluation (danger and relevance detection and contribution to reward/motivational processing) and the creation of cognitive maps (contextual memory, navigation, and generativity [imagination]); Unit III. Telencephalic-cortical, for sensorimotor and cognitive processing (gnosis, praxis, language, calculation, etc.), semantic and episodic (contextual) memory processing, and multimodal conscious agency; Unit IV. Basal ganglia systems, for behavior selection and reinforcement (reward-oriented behavior); Unit V. Cerebellar systems, for the prediction/anticipation (orthometric supervision) of the outcome of an action. The proposed brain units are nothing more than abstractions within the brain\'s simultaneous and distributed physiological processes. As function transcends anatomy, the model necessarily involves transition and overlap between structures. Beyond the classic approaches, this review includes information on recent systemic perspectives on functional brain organization. The limitations of this review are discussed.

Among the significant advances in the understanding of the organization of the neuronal networks that coordinate the body and brain, their complex nature is increasingly important, resulting from the interaction between the very large number of constituents strongly organized hierarchically and at the same time with \"self-emerging.\" This awareness drives us to identify the measures that best quantify the \"complexity\" that accompanies the continuous evolutionary dynamics of the brain. In this chapter, after an introductory section (Sect. 15.1), we examine how the Higuchi fractal dimension is able to perceive physiological processes (15.2), neurological (15.3) and psychiatric (15.4) disorders, and neuromodulation effects (15.5), giving a mention of other methods of measuring neuronal electrical activity in addition to electroencephalography, such as magnetoencephalography and functional magnetic resonance. Conscious that further progress will support a deeper understanding of the temporal course of neuronal activity because of continuous interaction with the environment, we conclude confident that the fractal dimension has begun to uncover important features of the physiology of brain activity and its alterations.

The introduction of fractal geometry to the neurosciences has been a major paradigm shift over the last decades as it has helped overcome approximations and limitations that occur when Euclidean and reductionist approaches are used to analyze neurons or the entire brain. Fractal geometry allows for quantitative analysis and description of the geometric complexity of the brain, from its single units to the neuronal networks.As illustrated in the second section of this book, fractal analysis provides a quantitative tool for the study of the morphology of brain cells (i.e., neurons and microglia) and its components (e.g., dendritic trees, synapses), as well as the brain structure itself (cortex, functional modules, neuronal networks). The self-similar logic which generates and shapes the different hierarchical systems of the brain and even some structures related to its \"container,\" that is, the cranial sutures on the skull, is widely discussed in the following chapters, with a link between the applications of fractal analysis to the neuroanatomy and basic neurosciences to the clinical applications discussed in the third section.

Neurons build vast gap junction-coupled networks (GJ-nets) that are permeable to ions or small molecules, enabling lateral signaling. Herein, we investigate (1) the effect of blinding diseases on GJ-nets in mouse retinas and (2) the impact of electrical stimulation on GJ permeability. GJ permeability was traced in the acute retinal explants of blind retinal degeneration 1 (rd1) mice using the GJ tracer neurobiotin. The tracer was introduced via the edge cut method into the GJ-net, and its spread was visualized in histological preparations (fluorescent tagged) using microscopy. Sustained stimulation was applied to modulate GJ permeability using a single large electrode. Our findings are: (1) The blind rd1 retinas displayed extensive intercellular coupling via open GJs. Three GJ-nets were identified: horizontal, amacrine, and ganglion cell networks. (2) Sustained stimulation significantly diminished the tracer spread through the GJs in all the cell layers, as occurs with pharmaceutical inhibition with carbenoxolone. We concluded that the GJ-nets of rd1 retinas remain coupled and functional after blinding disease and that their permeability is regulatable by sustained stimulation. These findings are essential for understanding molecular signaling in diseases over coupled networks and therapeutic approaches using electrical implants, such as eliciting visual sensations or suppressing cortical seizures.

Memory refers to the imprint accumulated in the brain by life experiences and represents the basis for humans to engage in advanced psychological activities such as thinking and imagination. Previously, research activities focused on memory have always targeted neurons. However, in addition to neurons, astrocytes are also involved in the encoding, consolidation, and extinction of memory. In particular, astrocytes are known to affect the recruitment and function of neurons at the level of local synapses and brain networks. Moreover, the involvement of astrocytes in memory and memory-related disorders, especially in Alzheimer\'s disease (AD) and post-traumatic stress disorder (PTSD), has been investigated extensively. In this review, we describe the unique contributions of astrocytes to synaptic plasticity and neuronal networks and discuss the role of astrocytes in different types of memory processing. In addition, we also explore the roles of astrocytes in the pathogenesis of memory-related disorders, such as AD, brain aging, PTSD and addiction, thus suggesting that targeting astrocytes may represent a potential strategy to treat memory-related neurological diseases. In conclusion, this review emphasizes that thinking from the perspective of astrocytes will provide new ideas for the diagnosis and therapy of memory-related neurological disorders.