The remarkable, yet not extraordinary, human brain as a scaled-up primate brain and its associated cost

Operating Mechanism Principles and Advancements for Halide Perovskite-Based Memristors and Neuromorphic Devices

With the advent of the generation of artificial intelligence (AI) based on big data-processing technologies, next-generation memristor and memristive neuromorphic devices have been actively studied with great interest to overcome the von Neumann bottleneck limits. Among various candidates, halide perovskites (HPs) have been in the spotlight as potential candidates for these devices due to their unique switching characteristics with low energy consumption and flexible integration compatibility across various sources for scalability. We outline the characteristics and operating principles of HP-based memristors and their neuromorphic devices. We explain filamentary- and interface-type switching according to the type of conducting pathway occurring inside the active HP layer and the operating mechanisms depending on the species that make up this conducting pathway. We summarize the types and mechanisms of current changes beneficial for neuromorphic device applications and finally organize various suggested analysis tools and physical models to enable experimental determination of switching mechanisms from various perspectives.

Neuroscientific insights about computer vision models: a concise review

The development of biologically-inspired computational models has been the focus of study ever since the artificial neuron was introduced by McCulloch and Pitts in 1943. However, a scrutiny of literature reveals that most attempts to replicate the highly efficient and complex biological visual system have been futile or have met with limited success. The recent state-of the-art computer vision models, such as pre-trained deep neural networks and vision transformers, may not be biologically inspired per se. Nevertheless, certain aspects of biological vision are still found embedded, knowingly or unknowingly, in the architecture and functioning of these models. This paper explores several principles related to visual neuroscience and the biological visual pathway that resonate, in some manner, in the architectural design and functioning of contemporary computer vision models. The findings of this survey can provide useful insights for building futuristic bio-inspired computer vision models. The survey is conducted from a historical perspective, tracing the biological connections of computer vision models starting with the basic artificial neuron to modern technologies such as deep convolutional neural network (CNN) and spiking neural networks (SNN). One spotlight of the survey is a discussion on biologically plausible neural networks and bio-inspired unsupervised learning mechanisms adapted for computer vision tasks in recent times.

The minimal computational substrate of fluid intelligence

The quantification of cognitive powers rests on identifying a behavioural task that depends on them. Such dependence cannot be assured, for the powers a task invokes cannot be experimentally controlled or constrained a priori, resulting in unknown vulnerability to failure of specificity and generalisability. Evaluating a compact version of Raven's Advanced Progressive Matrices (RAPM), a widely used clinical test of fluid intelligence, we show that LaMa, a self-supervised artificial neural network trained solely on the completion of partially masked images of natural environmental scenes, achieves representative human-level test scores a prima vista, without any task-specific inductive bias or training. Compared with cohorts of healthy and focally lesioned participants, LaMa exhibits human-like variation with item difficulty, and produces errors characteristic of right frontal lobe damage under degradation of its ability to integrate global spatial patterns. LaMa's narrow training and limited capacity suggest matrix-style tests may be open to computationally simple solutions that need not necessarily invoke the substrates of reasoning.

Meta-analysis of the make-up and properties of in vitro models of the healthy and diseased blood-brain barrier

In vitro models of the human blood-brain barrier (BBB) are increasingly used to develop therapeutics that can cross the BBB for treating diseases of the central nervous system. Here we report a meta-analysis of the make-up and properties of transwell and microfluidic models of the healthy BBB and of BBBs in glioblastoma, Alzheimer's disease, Parkinson's disease and inflammatory diseases. We found that the type of model, the culture method (static or dynamic), the cell types and cell ratios, and the biomaterials employed as extracellular matrix are all crucial to recapitulate the low permeability and high expression of tight-junction proteins of the BBB, and to obtain high trans-endothelial electrical resistance. Specifically, for models of the healthy BBB, the inclusion of endothelial cells and pericytes as well as physiological shear stresses (~10-20 dyne cm-2) are necessary, and when astrocytes are added, astrocytes or pericytes should outnumber endothelial cells. We expect this meta-analysis to facilitate the design of increasingly physiological models of the BBB.

Electrical Signaling Beyond Neurons

Neural action potentials (APs) are difficult to interpret as signal encoders and/or computational primitives. Their relationships with stimuli and behaviors are obscured by the staggering complexity of nervous systems themselves. We can reduce this complexity by observing that "simpler" neuron-less organisms also transduce stimuli into transient electrical pulses that affect their behaviors. Without a complicated nervous system, APs are often easier to understand as signal/response mechanisms. We review examples of nonneural stimulus transductions in domains of life largely neglected by theoretical neuroscience: bacteria, protozoans, plants, fungi, and neuron-less animals. We report properties of those electrical signals-for example, amplitudes, durations, ionic bases, refractory periods, and particularly their ecological purposes. We compare those properties with those of neurons to infer the tasks and selection pressures that neurons satisfy. Throughout the tree of life, nonneural stimulus transductions time behavioral responses to environmental changes. Nonneural organisms represent the presence or absence of a stimulus with the presence or absence of an electrical signal. Their transductions usually exhibit high sensitivity and specificity to a stimulus, but are often slow compared to neurons. Neurons appear to be sacrificing the specificity of their stimulus transductions for sensitivity and speed. We interpret cellular stimulus transductions as a cell's assertion that it detected something important at that moment in time. In particular, we consider neural APs as fast but noisy detection assertions. We infer that a principal goal of nervous systems is to detect extremely weak signals from noisy sensory spikes under enormous time pressure. We discuss neural computation proposals that address this goal by casting neurons as devices that implement online, analog, probabilistic computations with their membrane potentials. Those proposals imply a measurable relationship between afferent neural spiking statistics and efferent neural membrane electrophysiology.

Memory-efficient neurons and synapses for spike-timing-dependent-plasticity in large-scale spiking networks

This paper addresses the challenges posed by frequent memory access during simulations of large-scale spiking neural networks involving synaptic plasticity. We focus on the memory accesses performed during a common synaptic plasticity rule since this can be a significant factor limiting the efficiency of the simulations. We propose neuron models that are represented by only three state variables, which are engineered to enforce the appropriate neuronal dynamics. Additionally, memory retrieval is executed solely by fetching postsynaptic variables, promoting a contiguous memory storage and leveraging the capabilities of burst mode operations to reduce the overhead associated with each access. Different plasticity rules could be implemented despite the adopted simplifications, each leading to a distinct synaptic weight distribution (i.e., unimodal and bimodal). Moreover, our method requires fewer average memory accesses compared to a naive approach. We argue that the strategy described can speed up memory transactions and reduce latencies while maintaining a small memory footprint.

Neural Tube Organoids: A Novel System to Study Developmental Timing

The neural tube (NT) is a transient structure formed during embryogenesis which develops into the brain and spinal cord. While mouse models have been commonly used in place of human embryos to study NT development, species-specific differences limit their applicability. One major difference is developmental timing, with NT formation from the neural plate in 16 days in humans compared to 4 days in mice, as well as differences in the time taken to form neuronal subtypes and complete neurogenesis. Neural tube organoids (NTOs) represent a new way to study NT development in vitro. While mouse and human NTOs have been shown to recapitulate the major developmental events of NT formation; it is unknown whether species-specific developmental timing, also termed allochrony, is also recapitulated. This review summarises current research using both mouse and human NTOs and compares developmental timing events in order to assess if allochrony is maintained in organoids.

Active forgetting and neuropsychiatric diseases

Recent and pioneering animal research has revealed the brain utilizes a variety of molecular, cellular, and network-level mechanisms used to forget memories in a process referred to as "active forgetting". Active forgetting increases behavioral flexibility and removes irrelevant information. Individuals with impaired active forgetting mechanisms can experience intrusive memories, distressing thoughts, and unwanted impulses that occur in neuropsychiatric diseases. The current evidence indicates that active forgetting mechanisms degrade, or mask, molecular and cellular memory traces created in synaptic connections of "engram cells" that are specific for a given memory. Combined molecular genetic/behavioral studies using Drosophila have uncovered a complex system of cellular active-forgetting pathways within engram cells that is regulated by dopamine neurons and involves dopamine-nitric oxide co-transmission and reception, endoplasmic reticulum Ca2+ signaling, and cytoskeletal remodeling machinery regulated by small GTPases. Some of these molecular cellular mechanisms have already been found to be conserved in mammals. Interestingly, some pathways independently regulate forgetting of distinct memory types and temporal phases, suggesting a multi-layering organization of forgetting systems. In mammals, active forgetting also involves modulation of memory trace synaptic strength by altering AMPA receptor trafficking. Furthermore, active-forgetting employs network level mechanisms wherein non-engram neurons, newly born-engram neurons, and glial cells regulate engram synapses in a state and experience dependent manner. Remarkably, there is evidence for potential coordination between the network and cellular level forgetting mechanisms. Finally, subjects with several neuropsychiatric diseases have been tested and shown to be impaired in active forgetting. Insights obtained from research on active forgetting in animal models will continue to enrich our understanding of the brain dysfunctions that occur in neuropsychiatric diseases.

The Influence of Intestinal Microbiota on BDNF Levels

The regulation of neurogenesis, the complex process of producing and differentiating new brain tissue cells, is influenced by a complex interaction of internal and external factors. Over the past decade, extensive research has been conducted on neurotrophins and their key role in adult neurogenesis, as well as their impact on diseases such as depression. Among neurotrophins, the brain-derived neurotrophic factor (BDNF) has been the subject of comprehensive studies on adult neurogenesis, and scientific evidence supports its necessity for neurogenesis in the subventricular zone of the hippocampus. A novel area of research is the emerging role of gut microbiota as a significant contributor to neurogenesis and neurotrophin production. Studies have shown that reduced BDNF levels can lead to mood disorders, which are observed in intestinal dysbiosis, characterized by an imbalance in the composition and quantity of the intestinal microbiota. There is evidence in the literature that there is a link between brain function and gut microbiota. Physical activity, and especially the regularity and intensity of exercise, is important in relation to the level of BDNF and the intestinal microbiota. Probiotics, prebiotics and physical activity may have a positive effect on the intestinal microbiota, and therefore also on the level of the brain-derived neurotrophic factor.

Electrophysiological insights with brain organoid models: a brief review

Brain organoid is a three-dimensional (3D) tissue derived from stem cells such as induced pluripotent stem cells (iPSCs) embryonic stem cells (ESCs) that reflect real human brain structure. It replicates the complexity and development of the human brain, enabling studies of the human brain in vitro. With emerging technologies, its application is various, including disease modeling and drug screening. A variety of experimental methods have been used to study structural and molecular characteristics of brain organoids. However, electrophysiological analysis is necessary to understand their functional characteristics and complexity. Although electrophysiological approaches have rapidly advanced for monolayered cells, there are some limitations in studying electrophysiological and neural network characteristics due to the lack of 3D characteristics. Herein, electrophysiological measurement and analytical methods related to neural complexity and 3D characteristics of brain organoids are reviewed. Overall, electrophysiological understanding of brain organoids allows us to overcome limitations of monolayer in vitro cell culture models, providing deep insights into the neural network complex of the real human brain and new ways of disease modeling. [BMB Reports 2024; 57(7): 311-317].

Chaos and COSMOS-Considerations on QSM methods with multiple and single orientations and effects from local anisotropy

PURPOSE

Field-to-susceptibility inversion in quantitative susceptibility mapping (QSM) is ill-posed and needs numerical stabilization through either regularization or oversampling by acquiring data at three or more object orientations. Calculation Of Susceptibility through Multiple Orientations Sampling (COSMOS) is an established oversampling approach and regarded as QSM gold standard. It achieves a well-conditioned inverse problem, requiring rotations by 0°, 60° and 120° in the yz-plane. However, this is impractical in vivo, where head rotations are typically restricted to a range of ±25°. Non-ideal sampling degrades the conditioning with residual streaking artifacts whose mitigation needs further regularization. Moreover, susceptibility anisotropy in white matter is not considered in the COSMOS model, which may introduce additional bias. The current work presents a thorough investigation of these effects in primate brain.

METHODS

Gradient-recalled echo (GRE) data of an entire fixed chimpanzee brain were acquired at 7 T (350 μm resolution, 10 orientations) including ideal COSMOS sampling and realistic rotations in vivo. Comparisons of the results included ideal COSMOS, in-vivo feasible acquisitions with 3-8 orientations and single-orientation iLSQR QSM.

RESULTS

In-vivo feasible and optimal COSMOS yielded high-quality susceptibility maps with increased SNR resulting from averaging multiple acquisitions. COSMOS reconstructions from non-ideal rotations about a single axis required additional L2-regularization to mitigate residual streaking artifacts.

CONCLUSION

In view of unconsidered anisotropy effects, added complexity of the reconstruction, and the general challenge of multi-orientation acquisitions, advantages of sub-optimal COSMOS schemes over regularized single-orientation QSM appear limited in in-vivo settings.

Genomic, molecular, and cellular divergence of the human brain

While many core biological processes are conserved across species, the human brain has evolved with unique capacities. Current understanding of the neurobiological mechanisms that endow human traits as well as associated vulnerabilities remains limited. However, emerging data have illuminated species divergence in DNA elements and genome organization, in molecular, morphological, and functional features of conserved neural cell types, as well as temporal differences in brain development. Here, we summarize recent data on unique features of the human brain and their complex implications for the study and treatment of brain diseases. We also consider key outstanding questions in the field and discuss the technologies and foundational knowledge that will be required to accelerate understanding of human neurobiology.

Comparative Analysis of Human-Chimpanzee Divergence in Brain Connectivity and its Genetic Correlates

Chimpanzees (Pan troglodytes) are humans' closest living relatives, making them the most directly relevant comparison point for understanding human brain evolution. Zeroing in on the differences in brain connectivity between humans and chimpanzees can provide key insights into the specific evolutionary changes that might have occured along the human lineage. However, conducting comparisons of brain connectivity between humans and chimpanzees remains challenging, as cross-species brain atlases established within the same framework are currently lacking. Without the availability of cross-species brain atlases, the region-wise connectivity patterns between humans and chimpanzees cannot be directly compared. To address this gap, we built the first Chimpanzee Brainnetome Atlas (ChimpBNA) by following a well-established connectivity-based parcellation framework. Leveraging this new resource, we found substantial divergence in connectivity patterns across most association cortices, notably in the lateral temporal and dorsolateral prefrontal cortex between the two species. Intriguingly, these patterns significantly deviate from the patterns of cortical expansion observed in humans compared to chimpanzees. Additionally, we identified regions displaying connectional asymmetries that differed between species, likely resulting from evolutionary divergence. Genes associated with these divergent connectivities were found to be enriched in cell types crucial for cortical projection circuits and synapse formation. These genes exhibited more pronounced differences in expression patterns in regions with higher connectivity divergence, suggesting a potential foundation for brain connectivity evolution. Therefore, our study not only provides a fine-scale brain atlas of chimpanzees but also highlights the connectivity divergence between humans and chimpanzees in a more rigorous and comparative manner and suggests potential genetic correlates for the observed divergence in brain connectivity patterns between the two species. This can help us better understand the origins and development of uniquely human cognitive capabilities.

Human Brain In Vitro Model for Pathogen Infection-Related Neurodegeneration Study

Recent advancements in stem cell biology and tissue engineering have revolutionized the field of neurodegeneration research by enabling the development of sophisticated in vitro human brain models. These models, including 2D monolayer cultures, 3D organoids, organ-on-chips, and bioengineered 3D tissue models, aim to recapitulate the cellular diversity, structural organization, and functional properties of the native human brain. This review highlights how these in vitro brain models have been used to investigate the effects of various pathogens, including viruses, bacteria, fungi, and parasites infection, particularly in the human brain cand their subsequent impacts on neurodegenerative diseases. Traditional studies have demonstrated the susceptibility of different 2D brain cell types to infection, elucidated the mechanisms underlying pathogen-induced neuroinflammation, and identified potential therapeutic targets. Therefore, current methodological improvement brought the technology of 3D models to overcome the challenges of 2D cells, such as the limited cellular diversity, incomplete microenvironment, and lack of morphological structures by highlighting the need for further technological advancements. This review underscored the significance of in vitro human brain cell from 2D monolayer to bioengineered 3D tissue model for elucidating the intricate dynamics for pathogen infection modeling. These in vitro human brain cell enabled researchers to unravel human specific mechanisms underlying various pathogen infections such as SARS-CoV-2 to alter blood-brain-barrier function and Toxoplasma gondii impacting neural cell morphology and its function. Ultimately, these in vitro human brain models hold promise as personalized platforms for development of drug compound, gene therapy, and vaccine. Overall, we discussed the recent progress in in vitro human brain models, their applications in studying pathogen infection-related neurodegeneration, and future directions.

Presynaptic perspective: Axonal transport defects in neurodevelopmental disorders

Disruption of synapse assembly and maturation leads to a broad spectrum of neurodevelopmental disorders. Presynaptic proteins are largely synthesized in the soma, where they are packaged into precursor vesicles and transported into distal axons to ensure precise assembly and maintenance of presynapses. Due to their morphological features, neurons face challenges in the delivery of presynaptic cargos to nascent boutons. Thus, targeted axonal transport is vital to build functional synapses. A growing number of mutations in genes encoding the transport machinery have been linked to neurodevelopmental disorders. Emerging lines of evidence have started to uncover presynaptic mechanisms underlying axonal transport defects, thus broadening the view of neurodevelopmental disorders beyond postsynaptic mechanisms. In this review, we discuss presynaptic perspectives of neurodevelopmental disorders by focusing on impaired axonal transport and disturbed assembly and maintenance of presynapses. We also discuss potential strategies for restoring axonal transport as an early therapeutic intervention.

Language is primarily a tool for communication rather than thought

Language is a defining characteristic of our species, but the function, or functions, that it serves has been debated for centuries. Here we bring recent evidence from neuroscience and allied disciplines to argue that in modern humans, language is a tool for communication, contrary to a prominent view that we use language for thinking. We begin by introducing the brain network that supports linguistic ability in humans. We then review evidence for a double dissociation between language and thought, and discuss several properties of language that suggest that it is optimized for communication. We conclude that although the emergence of language has unquestionably transformed human culture, language does not appear to be a prerequisite for complex thought, including symbolic thought. Instead, language is a powerful tool for the transmission of cultural knowledge; it plausibly co-evolved with our thinking and reasoning capacities, and only reflects, rather than gives rise to, the signature sophistication of human cognition.

MRI morphometry of the anterior and posterior cerebellar vermis and its relationship to sensorimotor and cognitive functions in children

INTRODUCTION

The human cerebellum emerges as a posterior brain structure integrating neural networks for sensorimotor, cognitive, and emotional processing across the lifespan. Developmental studies of the cerebellar anatomy and function are scant. We examine age-dependent MRI morphometry of the anterior cerebellar vermis, lobules I-V and posterior neocortical lobules VI-VII and their relationship to sensorimotor and cognitive functions.

METHODS

Typically developing children (TDC; n=38; age 9-15) and healthy adults (HAC; n=31; 18-40) participated in high-resolution MRI. Rigorous anatomically informed morphometry of the vermis lobules I-V and VI-VII and total brain volume (TBV) employed manual segmentation computer-assisted FreeSurfer Image Analysis Program [http://surfer.nmr.mgh.harvard.edu]. The neuropsychological scores (WASI-II) were normalized and related to volumes of anterior, posterior vermis, and TBV.

RESULTS

TBVs were age independent. Volumes of I-V and VI-VII were significantly reduced in TDC. The ratio of VI-VII to I-V (∼60%) was stable across age-groups; I-V correlated with visual-spatial-motor skills; VI-VII with verbal, visual-abstract and FSIQ.

CONCLUSIONS

In TDC neither anterior I-V nor posterior VI-VII vermis attained adult volumes. The "inverted U" developmental trajectory of gray matter peaking in adolescence does not explain this finding. The hypothesis of protracted development of oligodendrocyte/myelination is suggested as a contributor to TDC's lower cerebellar vermis volumes.

Using a comprehensive atlas and predictive models to reveal the complexity and evolution of brain-active regulatory elements

Most genetic variants associated with psychiatric disorders are located in noncoding regions of the genome. To investigate their functional implications, we integrate epigenetic data from the PsychENCODE Consortium and other published sources to construct a comprehensive atlas of candidate brain cis-regulatory elements. Using deep learning, we model these elements' sequence syntax and predict how binding sites for lineage-specific transcription factors contribute to cell type-specific gene regulation in various types of glia and neurons. The elements' evolutionary history suggests that new regulatory information in the brain emerges primarily via smaller sequence mutations within conserved mammalian elements rather than entirely new human- or primate-specific sequences. However, primate-specific candidate elements, particularly those active during fetal brain development and in excitatory neurons and astrocytes, are implicated in the heritability of brain-related human traits. Additionally, we introduce PsychSCREEN, a web-based platform offering interactive visualization of PsychENCODE-generated genetic and epigenetic data from diverse brain cell types in individuals with psychiatric disorders and healthy controls.

Enhanced Multiscale Human Brain Imaging by Semi-supervised Digital Staining and Serial Sectioning Optical Coherence Tomography

A major challenge in neuroscience is to visualize the structure of the human brain at different scales. Traditional histology reveals micro- and meso-scale brain features, but suffers from staining variability, tissue damage and distortion that impedes accurate 3D reconstructions. Here, we present a new 3D imaging framework that combines serial sectioning optical coherence tomography (S-OCT) with a deep-learning digital staining (DS) model. We develop a novel semi-supervised learning technique to facilitate DS model training on weakly paired images. The DS model performs translation from S-OCT to Gallyas silver staining. We demonstrate DS on various human cerebral cortex samples with consistent staining quality. Additionally, we show that DS enhances contrast across cortical layer boundaries. Furthermore, we showcase geometry-preserving 3D DS on cubic-centimeter tissue blocks and visualization of meso-scale vessel networks in the white matter. We believe that our technique offers the potential for high-throughput, multiscale imaging of brain tissues and may facilitate studies of brain structures.

An Ecological and Neural Argument for Developing Pursuit-Based Cognitive Enrichment for Sea Lions in Human Care

While general enrichment strategies for captive animals attempt to elicit variable and species-typical behaviors, approaches to cognitive enrichment have been disappointingly one-size-fits-all. In this commentary, we address the potential benefit of tailoring cognitive enrichment to the "cognitive niche" of the species, with a particular focus on a reasonably well-studied marine carnivore, the sea lion. Sea lions likely share some cognitive evolutionary pressures with primates, including complex social behavior. Their foraging ecology, however, like that of many terrestrial carnivores, is based on the rapid and behaviorally flexible pursuit of avoidant prey. Unlike terrestrial carnivores, sea lions carry out this pursuit in a truly fluid three-dimensional field, computing and executing sensorimotor transformations from any solid angle to any other. The cognitive demands of flexible prey pursuit are unlikely to be fully elicited by typical stationary puzzle box style foraging enrichment devices or screen-based interactive games. With this species, we recommend exploring more water-based movement activities generally, and complex pursuit challenges specifically.

Memristor-induced hyperchaos, multiscroll and extreme multistability in fractional-order HNN: Image encryption and FPGA implementation

Fractional-order differentiation (FOD) can record information from the past, present, and future. Compared with integer-order systems, FOD systems have higher complexity and more accurate ability to describe the real world. In this paper, two types of fractional-order memristors are proposed and one type is proved to have extreme multistability, local activity, and non-volatility. By using memristors to simulate the autapse of a neuron and to describe the phenomenon of electromagnetic induction caused by electromagnetic radiation, we establish a new 5D FOD memristive HNN (FOMHNN). Through dynamic simulation, rich dynamic behaviors are found, such as hyperchaos, multiscroll, extreme multistability, and "overclocking" behavior caused by order reduction. To the best of our knowledge, this is the first time that such rich dynamic behaviors are found in FOMHNN simultaneously. Based on this FOMHNN, a very efficient and secure image encryption scheme is designed. Security analysis shows that the encrypted Lena image has extremely low adjacent pixel correlation and high randomness, with information entropy of 7.9995. Despite discarding diffusion and scrambling, it has excellent plaintext sensitivity, with NCPR = 99.6095% and UACI = 33.4671%. Finally, this paper implements the proposed FOMHNN and image encryption on field programmable gate array (FPGA). To our knowledge, the related work of fully hardware implementation of fractional-order neural networks and image encryption schemes based on this is rare.

Spike rate dependent synaptic characteristics in lamellar, multilayered alpha-MoO3 based two-terminal devices - efficient way to control the synaptic amplification

Brain-inspired computing systems require a rich variety of neuromorphic devices using multi-functional materials operating at room temperature. Artificial synapses which can be operated using optical and electrical stimuli are in high demand. In this regard, layered materials have attracted a lot of attention due to their tunable energy gap and exotic properties. In the current study, we report the growth of layered MoO3 using the chemical vapor deposition (CVD) technique. MoO3 has an energy gap of 3.22 eV and grows with a large aspect ratio, as seen through optical and scanning electron microscopy. We used transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy for complete characterisation. The two-terminal devices using platinum (Pt/MoO3/Pt) exhibit superior memory with the high-resistance state (HRS) and low-resistance state (LRS) differing by a large resistance (∼MΩ). The devices also show excellent synaptic characteristics. Both optical and electrical pulses can be utilised to stimulate the synapse. Consistent learning (potentiation) and forgetting (depression) curves are measured. Transition from long term depression to long term potentiation can be achieved using the spike frequency dependent pulsing scheme. We have found that the amplification of postsynaptic current can be tuned using such frequency dependent spikes. This will help us to design neuromorphic devices with the required synaptic amplification.

Possible roles of deep cortical neurons and oligodendrocytes in the neural basis of human sociality

Sociality is an instinctive property of organisms that live in relation to others and is a complex characteristic of higher order brain functions. However, the evolution of the human brain to acquire higher order brain functions, such as sociality, and the neural basis for executing these functions and their control mechanisms are largely unknown. Several studies have attempted to evaluate how human sociality was acquired during the course of evolution and the mechanisms controlling sociality from a neurodevelopment viewpoint. This review discusses these findings in the context of human brain evolution and the pathophysiology of autism spectrum disorder (ASD). Comparative genomic studies of postmortem primate brains have demonstrated human-specific regulatory mechanisms underlying higher order brain functions, providing evidence for the contribution of oligodendrocytes to human brain function. Functional analyses of the causative genes of ASD in animal models have demonstrated that the neural basis of social behavior is associated with layer 6 (L6) of the neocortex and oligodendrocytes. These findings demonstrate that both neurons and oligodendrocytes contribute to the neural basis and molecular mechanisms underlying human brain evolution and social functioning. This review provides novel insights into sociability and the corresponding neural bases of brain disorders and evolution.

Betz cells of the primary motor cortex

Betz cells, named in honor of Volodymyr Betz (1834-1894), who described them as "giant pyramids" in the primary motor cortex of primates and other mammalian species, are layer V extratelencephalic projection (ETP) neurons that directly innervate α-motoneurons of the brainstem and spinal cord. Despite their large volume and circumferential dendritic architecture, to date, no single molecular criterion has been established that unequivocally distinguishes adult Betz cells from other layer V ETP neurons. In primates, transcriptional signatures suggest the presence of at least two ETP neuron clusters that contain mature Betz cells; these are characterized by an abundance of axon guidance and oxidative phosphorylation transcripts. How neurodevelopmental programs drive the distinct positional and morphological features of Betz cells in humans remains unknown. Betz cells display a distinct biphasic firing pattern involving early cessation of firing followed by delayed sustained acceleration in spike frequency and magnitude. Few cell type-specific transcripts and electrophysiological characteristics are conserved between rodent layer V ETP neurons of the motor cortex and primate Betz cells. This has implications for the modeling of disorders that affect the motor cortex in humans, such as amyotrophic lateral sclerosis (ALS). Perhaps vulnerability to ALS is linked to the evolution of neural networks for fine motor control reflected in the distinct morphomolecular architecture of the human motor cortex, including Betz cells. Here, we discuss histological, molecular, and functional data concerning the position of Betz cells in the emerging taxonomy of neurons across diverse species and their role in neurological disorders.

Cognition of Time and Thinking Beyond

A common research protocol in cognitive neuroscience is to train subjects to perform deliberately designed experiments while recording brain activity, with the aim of understanding the brain mechanisms underlying cognition. However, how the results of this protocol of research can be applied in technology is seldom discussed. Here, I review the studies on time processing of the brain as examples of this research protocol, as well as two main application areas of neuroscience (neuroengineering and brain-inspired artificial intelligence). Time processing is a fundamental dimension of cognition, and time is also an indispensable dimension of any real-world signal to be processed in technology. Therefore, one may expect that the studies of time processing in cognition profoundly influence brain-related technology. Surprisingly, I found that the results from cognitive studies on timing processing are hardly helpful in solving practical problems. This awkward situation may be due to the lack of generalizability of the results of cognitive studies, which are under well-controlled laboratory conditions, to real-life situations. This lack of generalizability may be rooted in the fundamental unknowability of the world (including cognition). Overall, this paper questions and criticizes the usefulness and prospect of the abovementioned research protocol of cognitive neuroscience. I then give three suggestions for future research. First, to improve the generalizability of research, it is better to study brain activity under real-life conditions instead of in well-controlled laboratory experiments. Second, to overcome the unknowability of the world, we can engineer an easily accessible surrogate of the object under investigation, so that we can predict the behavior of the object under investigation by experimenting on the surrogate. Third, the paper calls for technology-oriented research, with the aim of technology creation instead of knowledge discovery.

An energy costly architecture of neuromodulators for human brain evolution and cognition

In comparison to other species, the human brain exhibits one of the highest energy demands relative to body metabolism. It remains unclear whether this heightened energy demand uniformly supports an enlarged brain or if specific signaling mechanisms necessitate greater energy. We hypothesized that the regional distribution of energy demands will reveal signaling strategies that have contributed to human cognitive development. We measured the energy distribution within the brain functional connectome using multimodal brain imaging and found that signaling pathways in evolutionarily expanded regions have up to 67% higher energetic costs than those in sensory-motor regions. Additionally, histology, transcriptomic data, and molecular imaging independently reveal an up-regulation of signaling at G-protein-coupled receptors in energy-demanding regions. Our findings indicate that neuromodulator activity is predominantly involved in cognitive functions, such as reading or memory processing. This study suggests that an up-regulation of neuromodulator activity, alongside increased brain size, is a crucial aspect of human brain evolution.

A Comparative Perspective on the Cerebello-Cerebral System and Its Link to Cognition

The longstanding idea that the cerebral cortex is the main neural correlate of human cognition can be elaborated by comparative analyses along the vertebrate phylogenetic tree that support the view that the cerebello-cerebral system is suited to support non-motor functions more generally. In humans, diverse accounts have illustrated cerebellar involvement in cognitive functions. Although the neocortex, and its transmodal association cortices such as the prefrontal cortex, have become disproportionately large over primate evolution specifically, human neocortical volume does not appear to be exceptional relative to the variability within primates. Rather, several lines of evidence indicate that the exceptional volumetric increase of the lateral cerebellum in conjunction with its connectivity with the cerebral cortical system may be linked to non-motor functions and mental operation in primates. This idea is supported by diverging cerebello-cerebral adaptations that potentially coevolve with cognitive abilities across other vertebrates such as dolphins, parrots, and elephants. Modular adaptations upon the vertebrate cerebello-cerebral system may thus help better understand the neuroevolutionary trajectory of the primate brain and its relation to cognition in humans. Lateral cerebellar lobules crura I-II and their reciprocal connections to the cerebral cortical association areas appear to have substantially expanded in great apes, and humans. This, along with the notable increase in the ventral portions of the dentate nucleus and a shift to increased relative prefrontal-cerebellar connectivity, suggests that modular cerebellar adaptations support cognitive functions in humans. In sum, we show how comparative neuroscience provides new avenues to broaden our understanding of cerebellar and cerebello-cerebral functions in the context of cognition.

Allosteric Competition and Inhibition in AMPA Receptors

Excitatory neurotransmission is principally mediated by AMPA-subtype ionotropic glutamate receptors (AMPARs). Dysregulation of AMPARs is the cause of many neurological disorders and how therapeutic candidates such as negative allosteric modulators inhibit AMPARs is unclear. Here, we show that non-competitive inhibition desensitizes AMPARs to activation and prevents positive allosteric modulation. We dissected the noncompetitive inhibition mechanism of action by capturing AMPARs bound to glutamate and the prototypical negative allosteric modulator, GYKI-52466, with cryo-electron microscopy. Noncompetitive inhibition by GYKI-52466, which binds in the transmembrane collar region surrounding the ion channel, negatively modulates AMPARs by decoupling glutamate binding in the ligand binding domain from the ion channel. Furthermore, during allosteric competition between negative and positive modulators, negative allosteric modulation by GKYI-52466 outcompetes positive allosteric modulators to control AMPAR function. Our data provide a new framework for understanding allostery of AMPARs and foundations for rational design of therapeutics targeting AMPARs in neurological diseases.

Phylogenetic comparative analysis of the cerebello-cerebral system in 34 species highlights primate-general expansion of cerebellar crura I-II

The reciprocal connections between the cerebellum and the cerebrum have been suggested to simultaneously play a role in brain size increase and to support a broad array of brain functions in primates. The cerebello-cerebral system has undergone marked functionally relevant reorganization. In particular, the lateral cerebellar lobules crura I-II (the ansiform) have been suggested to be expanded in hominoids. Here, we manually segmented 63 cerebella (34 primate species; 9 infraorders) and 30 ansiforms (13 species; 8 infraorders) to understand how their volumes have evolved over the primate lineage. Together, our analyses support proportional cerebellar-cerebral scaling, whereas ansiforms have expanded faster than the cerebellum and cerebrum. We did not find different scaling between strepsirrhines and haplorhines, nor between apes and non-apes. In sum, our study shows primate-general structural reorganization of the ansiform, relative to the cerebello-cerebral system, which is relevant for specialized brain functions in an evolutionary context.

Neurogenesis in primates versus rodents and the value of non-human primate models

Neurogenesis, the process of generating neurons from neural stem cells, occurs during both embryonic and adult stages, with each stage possessing distinct characteristics. Dysfunction in either stage can disrupt normal neural development, impair cognitive functions, and lead to various neurological disorders. Recent technological advancements in single-cell multiomics and gene-editing have facilitated investigations into primate neurogenesis. Here, we provide a comprehensive overview of neurogenesis across rodents, non-human primates, and humans, covering embryonic development to adulthood and focusing on the conservation and diversity among species. While non-human primates, especially monkeys, serve as valuable models with closer neural resemblance to humans, we highlight the potential impacts and limitations of non-human primate models on both physiological and pathological neurogenesis research.

Neural energy computations based on Hodgkin-Huxley models bridge abnormal neuronal activities and energy consumption patterns of major depressive disorder

Limited by the current experimental techniques and neurodynamical models, the dysregulation mechanisms of decision-making related neural circuits in major depressive disorder (MDD) are still not clear. In this paper, we proposed a neural coding methodology using energy to further investigate it, which has been proven to strongly complement the neurodynamical methodology. We augmented the previous neural energy calculation method, and applied it to our VTA-NAc-mPFC neurodynamical H-H models. We particularly focused on the peak power and energy consumption of abnormal ion channel (ionic) currents under different concentrations of dopamine input, and investigated the abnormal energy consumption patterns for the MDD group. The results revealed that the energy consumption of medium spiny neurons (MSNs) in the NAc region were lower in the MDD group than that of the normal control group despite having the same firing frequencies, peak action potentials, and average membrane potentials in both groups. Dopamine concentration was also positively correlated with the energy consumption of the pyramidal neurons, but the patterns of different interneuron types were distinct. Additionally, the ratio of mPFC's energy consumption to total energy consumption of the whole network in MDD group was lower than that in normal control group, revealing that the mPFC region in MDD group encoded less neural information, which matched the energy consumption patterns of BOLD-fMRI results. It was also in line with the behavioral characteristics that MDD patients demonstrated in the form of reward insensitivity during decision-making tasks. In conclusion, the model in this paper was the first neural network energy computational model for MDD, which showed success in explaining its dynamical mechanisms with an energy consumption perspective. To build on this, we demonstrated that energy consumption levels can be used as a potential indicator for MDD, which also showed a promising pipeline to use an energy methodology for studying other neuropsychiatric disorders.

The technology, opportunities, and challenges of Synthetic Biological Intelligence

Integrating neural cultures developed through synthetic biology methods with digital computing has enabled the early development of Synthetic Biological Intelligence (SBI). Recently, key studies have emphasized the advantages of biological neural systems in some information processing tasks. However, neither the technology behind this early development, nor the potential ethical opportunities or challenges, have been explored in detail yet. Here, we review the key aspects that facilitate the development of SBI and explore potential applications. Considering these foreseeable use cases, various ethical implications are proposed. Ultimately, this work aims to provide a robust framework to structure ethical considerations to ensure that SBI technology can be both researched and applied responsibly.

Anatomical correlates and nomenclature of the chiropteran endocranial cast

Bats form a diverse group of mammals that are highly specialized in active flight and ultrasound echolocation. These specializations rely on adaptations that reflect on their morphoanatomy and have been tentatively linked to brain morphology and volumetry. Despite their small size and fragility, bat crania and natural braincase casts ("endocasts") have been preserved in the fossil record, which allows for investigating brain evolution and inferring paleobiology. Advances in imaging techniques have allowed virtual extraction of internal structures, assuming that the shape of the endocast reflects soft organ morphology. However, there is no direct correspondence between the endocast and internal structures because meninges and vascular tissues mark the inner braincase together with the brain they surround, resulting in a mosaic morphology of the endocast. The hypothesis suggesting that the endocast reflects the brain in terms of both external shape and volume has drastic implications when addressing brain evolution, but it has been rarely discussed. To date, only a single study addressed the correspondence between the brain and braincase in bats. Taking advantage of the advent of imaging techniques, we reviewed the anatomical, neuroanatomical, and angiological literature and compare this knowledge available on bat's braincase anatomy with anatomical observations using a sample of endocranial casts representing most modern bat families. Such comparison allows to propose a Chiroptera-scale nomenclature for future descriptions and comparisons among bat endocasts. Describing the imprints of the tissues surrounding the brain also allows to address to what extent brain features can be blurred or hidden (e.g., hypophysis, epiphysis, colliculi, flocculus). Furthermore, this approach encourages further study to formally test the proposed hypotheses.

Influence of HIV infection on cognition and overall intelligence in HIV-infected individuals: advances and perspectives

It is now well understood that HIV-positive individuals, even those under effective ART, tend to develop a spectrum of cognitive, motor, and/or mood conditions which are contemporarily referred to as HIV-associated neurocognitive disorder (HAND), and which is directly related to HIV-1 infection and HIV-1 replication in the central nervous system (CNS). As HAND is known to induce difficulties associated with attention, concentration, and memory, it is thus legitimate and pertinent to speculate upon the possibility that HIV infection may well influence human cognition and intelligence. We therefore propose herein to review the concept of intelligence, the concept of cells of intelligence, the influence of HIV on these particular cells, and the evidence pointing to differences in observed intelligence quotient (IQ) scores between HIV-positive and HIV-negative individuals. Additionally, cumulative research evidence continues to draw attention to the influence of the gut on human intelligence. Up to now, although it is known that HIV infection profoundly alters both the composition and diversity of the gut microbiota and the structural integrity of the gut, the influence of the gut on intelligence in the context of HIV infection remains poorly described. As such, we also provide herein a review of the different ways in which HIV may influence human intelligence via the gut-brain axis. Finally, we provide a discourse on perspectives related to HIV and human intelligence which may assist in generating more robust evidence with respect to this issue in future studies. Our aim is to provide insightful knowledge for the identification of novel areas of investigation, in order to reveal and explain some of the enigmas related to HIV infection.

The effective enhancement of information in 3D small-world networks of biological neuronal cells

The cardiovascular system, the kidney, or the brain, are examples of complex systems - where the properties of the systems arise because of the layout of cells in those systems. One way to characterize systems is using networks, where elements and interactions of the systems are represented as nodes and links of a graph. Network's topology can be, in turn, measured by the small-world coefficient. Small world networks feature increased clustering and shorter paths compared to random or periodic networks of the same size. This suggests that systems with small world attributes can also efficiently transport signals, nutrients, or information within their body. While several reports in literature have illustrated that real biological systems are small-world, yet little is known about how information varies as a function of the small-world-ness (sw) of three dimensional graphs. Here, we used a model of the brain to estimate quantitatively the information processed in 3D networks. In the model, nodes of the graph are neuronal units capable to receive, integrate and transmit signals to other neurons of the system in parallel. The information encoded in the signals was then extracted using the techniques of information theory. In simulations where the topology of networks of400nodes was varied over large intervals, we found that in the0-9swrange information scales linearly with the small world coefficient, with a five-fold increase. Results of the paper and review of the existing literature on model organisms suggest that a small-world architecture may offer an evolutionary advantage to biological systems.

Tracing a new path in the field of AI and robotics: mimicking human intelligence through chemistry. Part II: systems chemistry

Inspired by some traits of human intelligence, it is proposed that wetware approaches based on molecular, supramolecular, and systems chemistry can provide valuable models and tools for novel forms of robotics and AI, being constituted by soft matter and fluid states as the human nervous system and, more generally, life, is. Bottom-up mimicries of intelligence range from the molecular world to the multicellular level, i.e., from the Ångström (10 - 10 meters) to the micrometer scales (10 - 6 meters), and allows the development of unconventional chemical robotics. Whereas conventional robotics lets humans explore and colonise otherwise inaccessible environments, such as the deep oceanic abysses and other solar system planets, chemical robots will permit us to inspect and control the microscopic molecular and cellular worlds. This article suggests that systems made of properly chosen molecular compounds can implement all those modules that are the fundamental ingredients of every living being: sensory, processing, actuating, and metabolic networks. Autonomous chemical robotics will be within reach when such modules are compartmentalised and assembled. The design of a strongly intertwined web of chemical robots, with or without the involvement of living matter, will give rise to collective forms of intelligence that will probably reproduce, on a minimal scale, some sophisticated performances of the human intellect and will implement forms of "general AI." These remarkable achievements will require a productive interdisciplinary collaboration among chemists, biotechnologists, computer scientists, engineers, physicists, neuroscientists, cognitive scientists, and philosophers to be achieved. The principal purpose of this paper is to spark this revolutionary collaborative scientific endeavour.

Altered cortical thickness and structural covariance networks in upper limb amputees: A graph theoretical analysis

BACKGROUND

The extensive functional and structural remodeling that occurs in the brain after amputation often results in phantom limb pain (PLP). These closely related phenomena are still not fully understood.

METHODS

Using magnetic resonance imaging (MRI) and graph theoretical analysis (GTA), we explored how alterations in brain cortical thickness (CTh) and structural covariance networks (SCNs) in upper limb amputees (ULAs) relate to PLP. In all, 45 ULAs and 45 healthy controls (HCs) underwent structural MRI. Regional network properties, including nodal degree, betweenness centrality (BC), and node efficiency, were analyzed with GTA. Similarly, global network properties, including global efficiency (Eglob), local efficiency (Eloc), clustering coefficient (Cp), characteristic path length (Lp), and the small-worldness index, were evaluated.

RESULTS

Compared with HCs, ULAs had reduced CThs in the postcentral and precentral gyri contralateral to the amputated limb; this decrease in CTh was negatively correlated with PLP intensity in ULAs. ULAs showed varying degrees of change in node efficiency in regional network properties compared to HCs (p < 0.005). There were no group differences in Eglob, Eloc, Cp, and Lp properties (all p > 0.05). The real-worldness SCN of ULAs showed a small-world topology ranging from 2% to 34%, and the area under the curve of the small-worldness index in ULAs was significantly different compared to HCs (p < 0.001).

CONCLUSION

These results suggest that the topological organization of human CNS functional networks is altered after amputation of the upper limb, providing further support for the cortical remapping theory of PLP.

Mapping acute neuroinflammation in vivo with diffusion-MRI in rats given a systemic lipopolysaccharide challenge

It is becoming increasingly apparent that neuroinflammation plays a critical role in an array of neurological and psychiatric disorders. Recent studies have demonstrated the potential of diffusion MRI (dMRI) to characterize changes in microglial density and morphology associated with neuroinflammation, but these were conducted mostly ex vivo and/or in extreme, non-physiological animal models. Here, we build upon these studies by investigating the utility of well-established dMRI methods to detect neuroinflammation in vivo in a more clinically relevant animal model of sickness behavior. We show that diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) indicate widespread increases in diffusivity in the brains of rats given a systemic lipopolysaccharide challenge (n = 20) vs. vehicle-treated controls (n = 12). These diffusivity changes correlated with histologically measured changes in microglial morphology, confirming the sensitivity of dMRI to neuroinflammatory processes. This study marks a further step towards establishing a noninvasive indicator of neuroinflammation, which would greatly facilitate early diagnosis and treatment monitoring in various neurological and psychiatric diseases.

Analysis of clinical failure of anti-tau and anti-synuclein antibodies in neurodegeneration using a quantitative systems pharmacology model

Misfolded proteins in Alzheimer's disease and Parkinson's disease follow a well-defined connectomics-based spatial progression. Several anti-tau and anti-alpha synuclein (aSyn) antibodies have failed to provide clinical benefit in clinical trials despite substantial target engagement in the experimentally accessible cerebrospinal fluid (CSF). The proposed mechanism of action is reducing neuronal uptake of oligomeric protein from the synaptic cleft. We built a quantitative systems pharmacology (QSP) model to quantitatively simulate intrasynaptic secretion, diffusion and antibody capture in the synaptic cleft, postsynaptic membrane binding and internalization of monomeric and oligomeric tau and aSyn proteins. Integration with a physiologically based pharmacokinetic (PBPK) model allowed us to simulate clinical trials of anti-tau antibodies gosuranemab, tilavonemab, semorinemab, and anti-aSyn antibodies cinpanemab and prasineuzumab. Maximal target engagement for monomeric tau was simulated as 45% (semorinemab) to 99% (gosuranemab) in CSF, 30% to 99% in ISF but only 1% to 3% in the synaptic cleft, leading to a reduction of less than 1% in uptake of oligomeric tau. Simulations for prasineuzumab and cinpanemab suggest target engagement of free monomeric aSyn of only 6-8% in CSF, 4-6% and 1-2% in the ISF and synaptic cleft, while maximal target engagement of aggregated aSyn was predicted to reach 99% and 80% in the synaptic cleft with similar effects on neuronal uptake. The study generates optimal values of selectivity, sensitivity and PK profiles for antibodies. The study identifies a gradient of decreasing target engagement from CSF to the synaptic cleft as a key driver of efficacy, quantitatively identifies various improvements for drug design and emphasizes the need for QSP modelling to support the development of tau and aSyn antibodies.

A C. elegans neuron both promotes and suppresses motor behavior to fine tune motor output

How neural circuits drive behavior is a central question in neuroscience. Proper execution of motor behavior requires precise coordination of many neurons. Within a motor circuit, individual neurons tend to play discrete roles by promoting or suppressing motor output. How exactly neurons function in specific roles to fine tune motor output is not well understood. In C. elegans, the interneuron RIM plays important yet complex roles in locomotion behavior. Here, we show that RIM both promotes and suppresses distinct features of locomotion behavior to fine tune motor output. This dual function is achieved via the excitation and inhibition of the same motor circuit by electrical and chemical neurotransmission, respectively. Additionally, this bi-directional regulation contributes to motor adaptation in animals placed in novel environments. Our findings reveal that individual neurons within a neural circuit may act in opposing ways to regulate circuit dynamics to fine tune behavioral output.

The neurological and non-neurological roles of the primary microcephaly-associated protein ASPM

Primary microcephaly (MCPH), is a neurological disorder characterized by small brain size that results in numerous developmental problems, including intellectual disability, motor and speech delays, and seizures. Hitherto, over 30 MCPH causing genes (MCPHs) have been identified. Among these MCPHs, MCPH5, which encodes abnormal spindle-like microcephaly-associated protein (ASPM), is the most frequently mutated gene. ASPM regulates mitotic events, cell proliferation, replication stress response, DNA repair, and tumorigenesis. Moreover, using a data mining approach, we have confirmed that high levels of expression of ASPM correlate with poor prognosis in several types of tumors. Here, we summarize the neurological and non-neurological functions of ASPM and provide insight into its implications for the diagnosis and treatment of MCPH and cancer.

High angular resolution susceptibility imaging and estimation of fiber orientation distribution functions in primate brain

Uncovering brain-tissue microstructure including axonal characteristics is a major neuroimaging research focus. Within this scope, anisotropic properties of magnetic susceptibility in white matter have been successfully employed to estimate primary axonal trajectories using mono-tensorial models. However, anisotropic susceptibility has not yet been considered for modeling more complex fiber structures within a voxel, such as intersecting bundles, or an estimation of orientation distribution functions (ODFs). This information is routinely obtained by high angular resolution diffusion imaging (HARDI) techniques. In applications to fixed tissue, however, diffusion-weighted imaging suffers from an inherently low signal-to-noise ratio and limited spatial resolution, leading to high demands on the performance of the gradient system in order to mitigate these limitations. In the current work, high angular resolution susceptibility imaging (HARSI) is proposed as a novel, phase-based methodology to estimate ODFs. A multiple gradient-echo dataset was acquired in an entire fixed chimpanzee brain at 61 orientations by reorienting the specimen in the magnetic field. The constant solid angle method was adapted for estimating phase-based ODFs. HARDI data were also acquired for comparison. HARSI yielded information on whole-brain fiber architecture, including identification of peaks of multiple bundles that resembled features of the HARDI results. Distinct differences between both methods suggest that susceptibility properties may offer complementary microstructural information. These proof-of-concept results indicate a potential to study the axonal organization in post-mortem primate and human brain at high resolution.

Understanding computational dialogue understanding

In this paper, we first explain why human-like dialogue understanding is so difficult for artificial intelligence (AI). We discuss various methods for testing the understanding capabilities of dialogue systems. Our review of the development of dialogue systems over five decades focuses on the transition from closed-domain to open-domain systems and their extension to multi-modal, multi-party and multi-lingual dialogues. From being somewhat of a niche topic of AI research for the first 40 years, it has made newspaper headlines in recent years and is now being discussed by political leaders at events such as the World Economic Forum in Davos. We ask whether large language models are super-parrots or a milestone towards human-like dialogue understanding and how they relate to what we know about language processing in the human brain. Using ChatGPT as an example, we present some limitations of this approach to dialogue systems. Finally, we present some lessons learned from our 40 years of research in this field about system architecture principles: symmetric multi-modality, no presentation without representation and anticipation feedback loops. We conclude with a discussion of grand challenges such as satisfying conversational maxims and the European Language Equality Act through massive digital multi-linguality-perhaps enabled by interactive machine learning with human trainers. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.

The Relationship between Cognition and Brain Size or Neuron Number

The comparative approach is a powerful way to explore the relationship between brain structure and cognitive function. Thus far, the field has been dominated by the assumption that a bigger brain somehow means better cognition. Correlations between differences in brain size or neuron number between species and differences in specific cognitive abilities exist, but these correlations are very noisy. Extreme differences exist between clades in the relationship between either brain size or neuron number and specific cognitive abilities. This means that correlations become weaker, not stronger, as the taxonomic diversity of sampled groups increases. Cognition is the outcome of neural networks. Here we propose that considering plausible neural network models will advance our understanding of the complex relationships between neuron number and different aspects of cognition. Computational modelling of networks suggests that adding pathways, or layers, or changing patterns of connectivity in a network can all have different specific consequences for cognition. Consequently, models of computational architecture can help us hypothesise how and why differences in neuron number might be related to differences in cognition. As methods in connectomics continue to improve and more structural information on animal brains becomes available, we are learning more about natural network structures in brains, and we can develop more biologically plausible models of cognitive architecture. Natural animal diversity then becomes a powerful resource to both test the assumptions of these models and explore hypotheses for how neural network structure and network size might delimit cognitive function.

Genes associated with cognitive ability and HAR show overlapping expression patterns in human cortical neuron types

GWAS have identified numerous genes associated with human cognition but their cell type expression profiles in the human brain are unknown. These genes overlap with human accelerated regions (HARs) implicated in human brain evolution and might act on the same biological processes. Here, we investigated whether these gene sets are expressed in adult human cortical neurons, and how their expression relates to neuronal function and structure. We find that these gene sets are preferentially expressed in L3 pyramidal neurons in middle temporal gyrus (MTG). Furthermore, neurons with higher expression had larger total dendritic length (TDL) and faster action potential (AP) kinetics, properties previously linked to intelligence. We identify a subset of genes associated with TDL or AP kinetics with predominantly synaptic functions and high abundance of HARs.

The temporal balance between self-renewal and differentiation of human neural stem cells requires the amyloid precursor protein

Neurogenesis in the developing human cerebral cortex occurs at a particularly slow rate owing in part to cortical neural progenitors preserving their progenitor state for a relatively long time, while generating neurons. How this balance between the progenitor and neurogenic state is regulated, and whether it contributes to species-specific brain temporal patterning, is poorly understood. Here, we show that the characteristic potential of human neural progenitor cells (NPCs) to remain in a progenitor state as they generate neurons for a prolonged amount of time requires the amyloid precursor protein (APP). In contrast, APP is dispensable in mouse NPCs, which undergo neurogenesis at a much faster rate. Mechanistically, APP cell-autonomously contributes to protracted neurogenesis through suppression of the proneurogenic activator protein-1 transcription factor and facilitation of canonical WNT signaling. We propose that the fine balance between self-renewal and differentiation is homeostatically regulated by APP, which may contribute to human-specific temporal patterns of neurogenesis.

The single-cell and spatial transcriptional landscape of human gastrulation and early brain development

The emergence of the three germ layers and the lineage-specific precursor cells orchestrating organogenesis represent fundamental milestones during early embryonic development. We analyzed the transcriptional profiles of over 400,000 cells from 14 human samples collected from post-conceptional weeks (PCW) 3 to 12 to delineate the dynamic molecular and cellular landscape of early gastrulation and nervous system development. We described the diversification of cell types, the spatial patterning of neural tube cells, and the signaling pathways likely involved in transforming epiblast cells into neuroepithelial cells and then into radial glia. We resolved 24 clusters of radial glial cells along the neural tube and outlined differentiation trajectories for the main classes of neurons. Lastly, we identified conserved and distinctive features across species by comparing early embryonic single-cell transcriptomic profiles between humans and mice. This comprehensive atlas sheds light on the molecular mechanisms underlying gastrulation and early human brain development.

Evidence of Metabolic Dysfunction in Amyotrophic Lateral Sclerosis (ALS) Patients and Animal Models

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that affects motor neurons, leading to muscle weakness, paralysis, and eventual death. Research from the past few decades has appreciated that ALS is not only a disease of the motor neurons but also a disease that involves systemic metabolic dysfunction. This review will examine the foundational research of understanding metabolic dysfunction in ALS and provide an overview of past and current studies in ALS patients and animal models, spanning from full systems to various metabolic organs. While ALS-affected muscle tissue exhibits elevated energy demand and a fuel preference switch from glycolysis to fatty acid oxidation, adipose tissue in ALS undergoes increased lipolysis. Dysfunctions in the liver and pancreas contribute to impaired glucose homeostasis and insulin secretion. The central nervous system (CNS) displays abnormal glucose regulation, mitochondrial dysfunction, and increased oxidative stress. Importantly, the hypothalamus, a brain region that controls whole-body metabolism, undergoes atrophy associated with pathological aggregates of TDP-43. This review will also cover past and present treatment options that target metabolic dysfunction in ALS and provide insights into the future of metabolism research in ALS.

Bio-inspired artificial synapse for neuromorphic computing based on NiO nanoparticle thin film

The unprecedented need for data processing in the modern technological era has created opportunities in neuromorphic devices and computation. This is primarily due to the extensive parallel processing done in our human brain. Data processing and logical decision-making at the same physical location are an exciting aspect of neuromorphic computation. For this, establishing reliable resistive switching devices working at room temperature with ease of fabrication is important. Here, a reliable analog resistive switching device based on Au/NiO nanoparticles/Au is discussed. The application of positive and negative voltage pulses of constant amplitude results in enhancement and reduction of synaptic current, which is consistent with potentiation and depression, respectively. The change in the conductance resulting in such a process can be fitted well with double exponential growth and decay, respectively. Consistent potentiation and depression characteristics reveal that non-ideal voltage pulses can result in a linear dependence of potentiation and depression. Long-term potentiation (LTP) and Long-term depression (LTD) characteristics have been established, which are essential for mimicking the biological synaptic applications. The NiO nanoparticle-based devices can also be used for controlled synaptic enhancement by optimizing the electric pulses, displaying typical learning-forgetting-relearning characteristics.

Relating enhancer genetic variation across mammals to complex phenotypes using machine learning

Protein-coding differences between species often fail to explain phenotypic diversity, suggesting the involvement of genomic elements that regulate gene expression such as enhancers. Identifying associations between enhancers and phenotypes is challenging because enhancer activity can be tissue-dependent and functionally conserved despite low sequence conservation. We developed the Tissue-Aware Conservation Inference Toolkit (TACIT) to associate candidate enhancers with species' phenotypes using predictions from machine learning models trained on specific tissues. Applying TACIT to associate motor cortex and parvalbumin-positive interneuron enhancers with neurological phenotypes revealed dozens of enhancer-phenotype associations, including brain size-associated enhancers that interact with genes implicated in microcephaly or macrocephaly. TACIT provides a foundation for identifying enhancers associated with the evolution of any convergently evolved phenotype in any large group of species with aligned genomes.