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In this paper, we describe the design of Neurogrid, a neuromorphic system for simulating large-scale neural models in real time. Neuromorphic systems realize the function of biological neural systems by emulating their structure. Designers of such systems face three major design choices: 1) whether to emulate the four neural elementsaxonal arbor, synapse, dendritic tree, and somawith dedicated or shared electronic circuits; 2) whether to implement these electronic circuits in an analog or digital manner; and 3) whether to interconnect arrays of these silicon neurons with a mesh or a tree network. The choices we made were: 1) we emulated all neural elements except the soma with shared electronic circuits; this choice maximized the number of synaptic connections; 2) we realized all electronic circuits except those for axonal arbors in an analog manner; this choice maximized energy efficiency; and 3) we interconnected neural arrays in a tree network; this choice maximized throughput. These three choices made it possible to simulate a million neurons with billions of synaptic connections in real timefor the first timeusing 16 Neurocores integrated on a board that consumes three watts.

Inspired by the brain’s structure, we have developed an efficient, scalable, and flexible non-von Neumann architecture that leverages contemporary silicon technology. To demonstrate, we built a 5.4-billion-transistor chip with 4096 neurosynaptic cores interconnected via an intrachip network that integrates 1 million programmable spiking neurons and 256 million configurable synapses. Chips can be tiled in two dimensions via an interchip communication interface, seamlessly scaling the architecture to a cortexlike sheet of arbitrary size. The architecture is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification. With 400-pixel-by-240-pixel video input at 30 frames per second, the chip consumes 63 milliwatts.

Background: The advent of digital technology has significantly altered ways of writing. While typing has become the dominant mode of written communication, handwriting remains a fundamental human skill, and its profound impact on cognitive processes continues to be a topic of intense scientific scrutiny. Methods: This paper investigates the neural mechanisms underlying handwriting and typing, exploring the distinct cognitive and neurological benefits associated with each. By synthesizing findings from neuroimaging studies, we explore how handwriting and typing differentially activate brain regions associated with motor control, sensory perception, and higher-order cognitive functions. Results: Handwriting activates a broader network of brain regions involved in motor, sensory, and cognitive processing. Typing engages fewer neural circuits, resulting in more passive cognitive engagement. Despite the advantages of typing in terms of speed and convenience, handwriting remains an important tool for learning and memory retention, particularly in educational contexts. Conclusions: This review contributes to the ongoing debate about the role of technology in education and cognitive development. By understanding the neural differences between handwriting and typing, we can gain insights into optimal learning strategies and potential cognitive advantages, in order to optimize educational, cognitive, and psychological methodologies.

Optogenetics has revolutionized the experimental interrogation of neural circuits and holds promise for the treatment of neurological disorders. It is limited, however, because visible light cannot penetrate deep inside brain tissue. Upconversion nanoparticles (UCNPs) absorb tissue-penetrating near-infrared (NIR) light and emit wavelength-specific visible light. Here, we demonstrate that molecularly tailored UCNPs can serve as optogenetic actuators of transcranial NIR light to stimulate deep brain neurons. Transcranial NIR UCNP-mediated optogenetics evoked dopamine release from genetically tagged neurons in the ventral tegmental area, induced brain oscillations through activation of inhibitory neurons in the medial septum, silenced seizure by inhibition of hippocampal excitatory cells, and triggered memory recall. UCNP technology will enable less-invasive optical neuronal activity manipulation with the potential for remote therapy.

This paper provides a perspective on applying the concepts of information thermodynamics, developed recently in non-equilibrium statistical physics, to problems in theoretical neuroscience. Historically, information and energy in neuroscience have been treated separately, in contrast to physics approaches, where the relationship of entropy production with heat is a central idea. It is argued here that also in neural systems information and energy can be considered within the same theoretical framework. Starting from basic ideas of thermodynamics and information theory on a classic Brownian particle, it is shown how noisy neural networks can infer its probabilistic motion. The decoding of the particle motion by neurons is performed with some accuracy and it has some energy cost, and both can be determined using information thermodynamics. In a similar fashion, we also discuss how neural networks in the brain can learn the particle velocity, and maintain that information in the weights of plastic synapses from a physical point of view. Generally, it is shown how the framework of stochastic and information thermodynamics can be used practically to study neural inference, learning, and information storing.

The characterisation of CMB polarisation is one of the next challenge in observationnal cosmology. This is especially true for the so-called B-modes that are at least 3 order of magnitude lower than CMB temperature fluctuations. A precise measurement of the angular power spectrum of these B-modes will give important constraints on inflation parameters. In this talk, I will describe two complementary experiments, BRAIN and CLOVER, dedicated to CMB polarisation measurement. These experiments are proposed to be installed in Dome-C, Antarctica, to take advantage of the extreme dryness of the atmosphere and to allow long integration time.

Immersive virtual reality (VR) emerges as a promising research and clinical tool. However, several studies suggest that VR induced adverse symptoms and effects (VRISE) may undermine the health and safety standards, and the reliability of the scientific results. In the current literature review, the technical reasons for the adverse symptomatology are investigated to provide suggestions and technological knowledge for the implementation of VR head-mounted display (HMD) systems in cognitive neuroscience. The technological systematic literature indicated features pertinent to display, sound, motion tracking, navigation, ergonomic interactions, user experience, and computer hardware that should be considered by the researchers. Subsequently, a meta-analysis of 44 neuroscientific or neuropsychological studies involving VR HMD systems was performed. The meta-analysis of the VR studies demonstrated that new generation HMDs induced significantly less VRISE and marginally fewer dropouts.Importantly, the commercial versions of the new generation HMDs with ergonomic interactions had zero incidents of adverse symptomatology and dropouts. HMDs equivalent to or greater than the commercial versions of contemporary HMDs accompanied with ergonomic interactions are suitable for implementation in cognitive neuroscience. In conclusion, researchers technological competency, along with meticulous methods and reports pertinent to software, hardware, and VRISE, are paramount to ensure the health and safety standards and the reliability of neuroscientific results.

The cognitive frame in which most neuropsychological research on the neural basis of behavior is conducted contains the assumption that brain mechanisms per se fully suffice to explain all psychologically described phenomena. This assumption stems from the idea that the brain is made up entirely of material particles and fields, and that all causal mechanisms must therefore be formulated solely in terms of properties of these elements. One consequence of this stance is that psychological terms having intrinsic mentalistic and/or experiential content (terms such as "feeling," "knowing," and "effort") have not been included as primary causal factors in neuropsychological research: insofar as properties are not described in material terms they are deemed irrelevant to the causal mechanisms underlying brain function. However, the origin of this demand that experiential realities be excluded from the causal base is a theory of nature that has been known for more that three quarters of a century to be fundamentally incorrect. It is explained here why it is consequently scientifically unwarranted to assume that material factors alone can in principle explain all causal mechanisms relevant to neuroscience. More importantly, it is explained how a key quantum effect can be introduced into brain dynamics in a simple and practical way that provides a rationally coherent, causally formulated, physics-based way of understanding and using the psychological and physical data derived from the growing set of studies of the capacity of directed attention and mental effort to systematically alter brain function.

Anxiety, depression, and schizophrenia are complex psychiatric disorders characterized by disruptions in neural circuits, neurotransmitter systems, and brain connectivity, resulting in impairments in emotional regulation and cognitive functioning. This review examines the genetic, environmental, and neurobiological factors influencing these conditions, empha sizing the critical roles of neurotransmitters such as serotonin, dopamine, and norepinephrine in mood regulation, stress response, and neuroplasticity. These findings underscore the need for personalized treatment approaches. The review also explores integrative strategies that combine pharmacological interventions with non-pharmacological modalities, including acupuncture, herbal medicine, and mindfulness, which show promise for individualized care. Advances in neuro imaging and neurostimulation techniques, such as eigenvector centrality mapping and machine learning-driven analyses, provide deeper insights into brain connectivity and enable more targeted interventions. This is particularly significant for schizophrenia, where dopamine-mediated disruptions in striatal-prefrontal connectivity contribute to cognitive deficits and clinical symptoms. However, current limitations, such as an inadequate understanding of the neural circuits underlying these disorders and the limited effectiveness of conventional treatments for certain subgroups, highlight critical gaps in existing research and therapeutic approaches. Furthermore, the article discusses how integrating computational models with traditional medicine enhances our understanding of neurotransmitter interactions and neural pathways. This integration fosters innovative therapies that address both immediate symptoms and long-term resilience. This interdisciplinary approach bridges basic neuroscience and clinical practice, paving the way for effective, personalized treatments and offering renewed hope for individuals with psychiatric disorders.

There have been two recent revolutionary advances in neuroscience: First, genetically encoded activity sensors have brought the goal of optical detection of single action potentials in vivo within reach. Second, optogenetic actuators now allow the activity of neurons to be controlled with millisecond precision. These revolutions have now been combined, together with advanced microscopies, to allow "all-optical" readout and manipulation of activity in neural circuits with single-spike and single-neuron precision. This is a transformational advance that will open new frontiers in neuroscience research. Harnessing the power of light in the all-optical approach requires coexpression of genetically encoded activity sensors and optogenetic probes in the same neurons, as well as the ability to simultaneously target and record the light from the selected neurons. It has recently become possible to combine sensors and optical strategies that are sufficiently sensitive and cross talk free to enable single-action-potential sensitivity and precision for both readout and manipulation in the intact brain. The combination of simultaneous readout and manipulation from the same genetically defined cells will enable a wide range of new experiments as well as inspire new technologies for interacting with the brain. The advances described in this review herald a future where the traditional tools used for generations by physiologists to study and interact with the brain-stimulation and recording electrodes-can largely be replaced by light. We outline potential future developments in this field and discuss how the all-optical strategy can be applied to solve fundamental problems in neuroscience. SIGNIFICANCE STATEMENT: This review describes the nexus of dramatic recent developments in optogenetic probes, genetically encoded activity sensors, and novel microscopies, which together allow the activity of neural circuits to be recorded and manipulated entirely using light. The optical and protein engineering strategies that form the basis of this "all-optical" approach are now sufficiently advanced to enable single-neuron and single-action potential precision for simultaneous readout and manipulation from the same functionally defined neurons in the intact brain. These advances promise to illuminate many fundamental challenges in neuroscience, including transforming our search for the neural code and the links between neural circuit activity and behavior.