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Emotions are fundamental to our experience and behavior, affecting and motivating all aspects of our lives. Scientists of various disciplines have been fascinated by emotions for centuries, yet even today vigorous debates abound about how to define emotions and how to best study their neural underpinnings. Defining emotions from an evolutionary perspective and acknowledging their important functional roles in supporting survival allows the study of emotion states in diverse species. This approach enables taking advantage of modern tools in behavioral, systems, and circuit neurosciences, allowing the precise dissection of neural mechanisms and behavior underlying emotion processes in model organisms. Here we review findings about the neural circuit mechanisms underlying emotion processing across species and try to identify points of convergence as well as important next steps in the pursuit of understanding how emotions emerge from neural activity.

The development of neural circuits involves wiring of neurons locally following their generation and migration, as well as establishing long-distance connections between brain regions. Studying these developmental processes in the human nervous system remains difficult because of limited access to tissue that can be maintained as functional over time in vitro. We have previously developed a method to convert human pluripotent stem cells into brain region-specific organoids that can be fused and integrated to form assembloids and study neuronal migration. In contrast to approaches that mix cell lineages in 2D cultures or engineer microchips, assembloids leverage self-organization to enable complex cell-cell interactions, circuit formation and maturation in long-term cultures. In this protocol, we describe approaches to model long-range neuronal connectivity in human brain assembloids. We present how to generate 3D spheroids resembling specific domains of the nervous system and then how to integrate them physically to allow axonal projections and synaptic assembly. In addition, we describe a series of assays including viral labeling and retrograde tracing, 3D live imaging of axon projection and optogenetics combined with calcium imaging and electrophysiological recordings to probe and manipulate the circuits in assembloids. The assays take 3-4 months to complete and require expertise in stem cell culture, imaging and electrophysiology. We anticipate that these approaches will be useful in deciphering human-specific aspects of neural circuit assembly and in modeling neurodevelopmental disorders with patient-derived cells.

Drug addiction represents a dramatic dysregulation of motivational circuits that is caused by a combination of exaggerated incentive salience and habit formation, reward deficits and stress surfeits, and compromised executive function in three stages. The rewarding effects of drugs of abuse, development of incentive salience, and development of drug-seeking habits in the binge/intoxication stage involve changes in dopamine and opioid peptides in the basal ganglia. The increases in negative emotional states and dysphoric and stress-like responses in the withdrawal/negative affect stage involve decreases in the function of the dopamine component of the reward system and recruitment of brain stress neurotransmitters, such as corticotropin-releasing factor and dynorphin, in the neurocircuitry of the extended amygdala. The craving and deficits in executive function in the so-called preoccupation/anticipation stage involve the dysregulation of key afferent projections from the prefrontal cortex and insula, including glutamate, to the basal ganglia and extended amygdala. Molecular genetic studies have identified transduction and transcription factors that act in neurocircuitry associated with the development and maintenance of addiction that might mediate initial vulnerability, maintenance, and relapse associated with addiction.

Pain is a multidimensional experience with sensory-discriminative, affective-motivational, and cognitive-evaluative components. Pain aversiveness is one principal cause of suffering for patients with chronic pain, motivating research and drug development efforts to investigate and modulate neural activity in the brain’s circuits encoding pain unpleasantness. Here, we review progress in understanding the organization of emotion, motivation, cognition, and descending modulation circuits for pain perception. We describe the molecularly defined neuron types that collectively shape pain multidimensionality and its aversive quality. We also review how pharmacological, stimulation, neurofeedback, surgical, and cognitive-behavioral interventions alter activity in these circuits to relieve chronic pain.

In the central nervous system (CNS), where neuronal activity promotes brain development and plasticity, including glial precursor cell proliferation, the activity of neurons robustly drives the initiation, growth, invasion, treatment resistance, and progression of brain cancers such as adult and pediatric hemispheric high-grade gliomas, diffuse midline gliomas such as diffuse intrinsic pontine glioma (DIPG), and pediatric low-grade optic gliomas. The underlying mechanisms involve both neuronal-activity-regulated paracrine signaling and direct electrochemical communication through neuron-to-glioma synapses. Neuronal inputs to tumors can then be propagated through connections between cancer cells. In turn, brain cancers such as gliomas remodel neural circuits to increase excitability, thereby augmenting the tumor-promoting effects of brain activity and contributing to tumor-associated seizures and neurological impairments, including cognitive deficits. These principles of neuron-cancer interactions are proving to be relevant to other cancers in the brain and the body, underscoring the importance of approaching cancers from a neuroscience perspective.

There are ever more compelling tools available for neuroscience research, ranging from selective genetic targeting to optogenetic circuit control to mapping whole connectomes. These approaches are coupled with a deep-seated, often tacit, belief in the reductionist program for understanding the link between the brain and behavior. The aim of this program is causal explanation through neural manipulations that allow testing of necessity and sufficiency claims. We argue, however, that another equally important approach seeks an alternative form of understanding through careful theoretical and experimental decomposition of behavior. Specifically, the detailed analysis of tasks and of the behavior they elicit is best suited for discovering component processes and their underlying algorithms. In most cases, we argue that study of the neural implementation of behavior is best investigated after such behavioral work. Thus, we advocate a more pluralistic notion of neuroscience when it comes to the brain-behavior relationship: behavioral work provides understanding, whereas neural interventions test causality.

Advances in neuroscience identified addiction as a chronic brain disease with strong genetic, neurodevelopmental, and sociocultural components. We here discuss the circuit- and cell-level mechanisms of this condition and its co-option of pathways regulating reward, self-control, and affect. Drugs of abuse exert their initial reinforcing effects by triggering supraphysiologic surges of dopamine in the nucleus accumbens that activate the direct striatal pathway via D1 receptors and inhibit the indirect striato-cortical pathway via D2 receptors. Repeated drug administration triggers neuroplastic changes in glutamatergic inputs to the striatum and midbrain dopamine neurons, enhancing the brain's reactivity to drug cues, reducing the sensitivity to non-drug rewards, weakening self-regulation, and increasing the sensitivity to stressful stimuli and dysphoria. Drug-induced impairments are long lasting; thus, interventions designed to mitigate or even reverse them would be beneficial for the treatment of addiction.

The recent worldwide surge of warfare and hostilities exposes increasingly large numbers of individuals to traumatic events, placing them at risk of developing posttraumatic stress disorder (PTSD) and challenging both clinicians and service delivery systems. This overview summarizes and updates the core knowledge of the genetic, molecular, and neural circuit features of the neurobiology of PTSD and advances in evidence-based psychotherapy, pharmacotherapy, neuromodulation, and digital treatments. While the complexity of the neurobiology and the biological and clinical heterogeneity of PTSD have challenged clinicians and researchers, there is an emerging consensus concerning the underlying mechanisms and approaches to diagnosis, treatment, and prevention of PTSD. This update addresses PTSD diagnosis, prevalence, course, risk factors, neurobiological mechanisms, current standard of care, and innovations in next-generation treatment and prevention strategies. It provides a comprehensive summary and concludes with areas of research for integrating advances in the neurobiology of the disorder with novel treatment and prevention targets.

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.

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.

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.

Wireless deep brain stimulation of well-defined neuronal populations could facilitate the study of intact brain circuits and the treatment of neurological disorders. Here, we demonstrate minimally invasive and remote neural excitation through the activation of the heat-sensitive capsaicin receptor TRPV1 by magnetic nanoparticles. When exposed to alternating magnetic fields, the nanoparticles dissipate heat generated by hysteresis, triggering widespread and reversible firing of TRPV1<SUP>+</SUP> neurons. Wireless magnetothermal stimulation in the ventral tegmental area of mice evoked excitation in subpopulations of neurons in the targeted brain region and in structures receiving excitatory projections. The nanoparticles persisted in the brain for over a month, allowing for chronic stimulation without the need for implants and connectors.

Studies in social neuroscience and brain imaging that

have investigated the neural basis of human empathy

reveal that the development of empathy is rooted in

early infancy, well before the emergence of verbal

abilities and more complex capacities in in social

understanding (Tousignant, Eugène & Jackson, 2017).

The key focus of this article is to demonstrate how

experiences and interactions in the earliest months of

life impact on neural circuits, overall brain development

and, in particular, the development of empathy and

altruist motivation in children from birth. Guidance

on supporting empathy in early childhood education

and care practice through responsive reciprocal

relationships is also offered

Large circuits of neurons are employed by the brain to encode and process information. How this encoding and processing is carried out is one of the central questions in neuroscience. Since individual neurons communicate with each other through electrical signals (action potentials), the recording of neural activity with arrays of extracellular electrodes is uniquely suited for the investigation of this question. Such recordings provide the combination of the best spatial (individual neurons) and temporal (individual action-potentials) resolutions compared to other large-scale imaging methods. Electrical stimulation of neural activity in turn has two very important applications: it enhances our understanding of neural circuits by allowing active interactions with them, and it is a basis for a large variety of neural prosthetic devices. Until recently, the state-of-the-art in neural activity recording systems consisted of several dozen electrodes with inter-electrode spacing ranging from tens to hundreds of microns. Using silicon microstrip detector expertise acquired in the field of high-energy physics, we created a unique neural activity readout and stimulation framework that consists of high-density electrode arrays, multi-channel custom-designed integrated circuits, a data acquisition system, and data-processing software. Using this framework we developed a number of neural readout and stimulation systems: (1) a 512-electrode system for recording the simultaneous activity of as many as hundreds of neurons, (2) a 61-electrode system for electrical stimulation and readout of neural activity in retinas and brain-tissue slices, and (3) a system with telemetry capabilities for recording neural activity in the intact brain of awake, naturally behaving animals. We will report on these systems, their various applications to the field of neurobiology, and novel scientific results obtained with some of them. We will also outline future directions

This paper presents work on ultra-low-power circuits for brain–machine interfaces with applications for paralysis prosthetics, stroke, Parkinson’s disease, epilepsy, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use

in multi-electrode arrays; an analog linear decoding and learning

architecture for data compression; low-power radio-frequency

(RF) impedance-modulation circuits for data telemetry that

minimize power consumption of implanted systems in the body;

a wireless link for efficient power transfer; mixed-signal system

integration for efficiency, robustness, and programmability; and

circuits for wireless stimulation of neurons with power-conserving

sleep modes and awake modes. Experimental results from chips

that have stimulated and recorded from neurons in the zebra

finch brain and results from RF power-link, RF data-link, electrode-

recording and electrode-stimulating systems are presented.

Simulations of analog learning circuits that have successfully

decoded prerecorded neural signals from a monkey brain are also

presented

How the brain generates visual percepts is a central problem in neuroscience. We propose a detailed neural model of how LGN and the interblob cortical stream through V1 and V2 generate context-sensitive perceptual groupings from visual inputs. The model suggests a functional role for cortical layers, columns, maps, and networks and proposes homologous circuits for V1 and V2 with larger scale processing in V2. An integrated treatment of interlaminar, horizontal, orientational, and endstopping cortical interactions and a role for corticogeniculate feedback in grouping are proposed. Modeled circuits simulate parametric psychophysical data about boundary grouping and illusory contour formation.Office of Naval Research (N00014-95-1-0657, N00014-95-1-0409, N00014-94-1-0597, N00014-95-1-0409, N00014-95-1-0409

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.

Abstract Synapses are the contact sites that enable neurons to form connections between each other in order to transmit and process neural information. Synaptic organization is concerned with the principles by which neurons form circuits that mediate the specific functional operations of different brain regions. One of the aims of this book is to show that the study of synaptic organization—in its full multidisciplinary, multilevel, and theoretical dimension—is a powerful means of integrating brain information to give clear insights into the neural basis of behavior. This book, which has been revised in this the fifth edition, details local circuits in the different regions of the brain. The results of the mouse and human genome projects are incorporated. Also the book contains support from neuroscience databases. Among the new advances covered are 2-photon confocal laser microscopy of dendrites and dendritic spines, biochemical analyses, and dual patch and multielectrode recordings, applied together with an increasing range of behavioral and gene-targeting methods.