Mental health issues are found across the world and in every population. While according to the World Health Organization, around a third of the adult population worldwide suffers from mental disorders such as depression, anxiety, and schizophrenia. Research shows that psychiatric disorders can be better understood as varying states of brain development that have a particular way of expressing difficulties in particular environmental contexts, based on genomic background, development, and experience. Psychiatry is informed by a broad range of basic biological and social sciences and has at its disposal many tools, like brain imaging, genetics, neuropsychopharmacology, neurophysiology, epidemiological models, and neuropsychology, for developing new assessment and treatment approaches, grounded in an understanding of etiology and pathophysiology.
Understanding the brain represents one of the most profound and pressing scientific challenges of the 21st century. First of all, we need to know which is the main priority of the brain. Is it an accurate prediction? Is the ability to discriminate the self from the others? Or even to understand the intentions of others? Is the brain an information processing machine? Or is the brain a metaphorical machine, producing the myriad products of the mind? Also, do brains operate on the principle of energetic processing? Brain modeling is being informed by brain imaging, connectograms, psychopharmacology, neurophysiology, neuropsychology, phenomenology, and genetics, to develop new assessment and treatment approaches.
Brain modeling derived from the brain priorities is expected to make scientific communication more precise, leading to applications across science, health care, and technology. Biotic information theories suggest organizational models for the exchange of energy-information between organism and environment. Phenomenology suggests affective intentionality as an embodied and enactive process that connects us to a shared world and guides our dealings with it. Biology provides evidence for self-organized collective or ‘swarm’ intelligence, a kind of artificial intelligence, which is based on the collective behavior of elements in decentralized and self-organized systems. Moreover, the idea of liquid neural networks or ‘liquid brains’ provides a paradigm of cognitive living networks, like ant colonies, which share collective dynamics, memory, and learning properties.
Brain models can provide psychiatry with unique and useful insights. Creating brain modeling, computational neuroscience employs mathematical models and abstractions of the brain to understand the principles that govern the development and the priorities of the brain. It is usually concerned with biologically unrealistic models used in connectionism, cybernetics, artificial intelligence, and computational learning theory. Grounded in clinical neuroscience and guided by brain modeling, psychiatry can improve both assessment and treatment strategies. In this way, psychiatry can develop more accurate interventions, via a deeper understanding of genetics, pathophysiology, functional neuroanatomy, and neuropsychopharmacology.
The goal of this Research Topic is to strengthen the dialogue, in order to fill the gap between neuroscience knowledge and the mental health unmet needs. We believe that brain model approaches from any scientific field will accelerate the progress across basic and clinical research, helping to provide more effective interventions in psychiatry.
We are happy to receive a range of manuscript types, such as original research, reviews, mini-reviews, opinions, and hypotheses on various topics related to Brain Modeling according to the Brain Priorities. Themes (but not limited to) that we endeavor to spotlight include:
· Predictive processing, predictive coding: The Predictive Brain
· Consciousness and energy in the brain, energy, and information in the brain: the Energetic Brain
· Metaphor, metonymy, conceptual metaphor theory, embodied cognition: The Metaphorical Brain
· Emotional intentionality, interoceptive predicting coding, The Emotional Brain
· Information theory, biotic information, liquid brains, swarm intelligence: The Computational Brain
Mental health issues are found across the world and in every population. While according to the World Health Organization, around a third of the adult population worldwide suffers from mental disorders such as depression, anxiety, and schizophrenia. Research shows that psychiatric disorders can be better understood as varying states of brain development that have a particular way of expressing difficulties in particular environmental contexts, based on genomic background, development, and experience. Psychiatry is informed by a broad range of basic biological and social sciences and has at its disposal many tools, like brain imaging, genetics, neuropsychopharmacology, neurophysiology, epidemiological models, and neuropsychology, for developing new assessment and treatment approaches, grounded in an understanding of etiology and pathophysiology.
Understanding the brain represents one of the most profound and pressing scientific challenges of the 21st century. First of all, we need to know which is the main priority of the brain. Is it an accurate prediction? Is the ability to discriminate the self from the others? Or even to understand the intentions of others? Is the brain an information processing machine? Or is the brain a metaphorical machine, producing the myriad products of the mind? Also, do brains operate on the principle of energetic processing? Brain modeling is being informed by brain imaging, connectograms, psychopharmacology, neurophysiology, neuropsychology, phenomenology, and genetics, to develop new assessment and treatment approaches.
Brain modeling derived from the brain priorities is expected to make scientific communication more precise, leading to applications across science, health care, and technology. Biotic information theories suggest organizational models for the exchange of energy-information between organism and environment. Phenomenology suggests affective intentionality as an embodied and enactive process that connects us to a shared world and guides our dealings with it. Biology provides evidence for self-organized collective or ‘swarm’ intelligence, a kind of artificial intelligence, which is based on the collective behavior of elements in decentralized and self-organized systems. Moreover, the idea of liquid neural networks or ‘liquid brains’ provides a paradigm of cognitive living networks, like ant colonies, which share collective dynamics, memory, and learning properties.
Brain models can provide psychiatry with unique and useful insights. Creating brain modeling, computational neuroscience employs mathematical models and abstractions of the brain to understand the principles that govern the development and the priorities of the brain. It is usually concerned with biologically unrealistic models used in connectionism, cybernetics, artificial intelligence, and computational learning theory. Grounded in clinical neuroscience and guided by brain modeling, psychiatry can improve both assessment and treatment strategies. In this way, psychiatry can develop more accurate interventions, via a deeper understanding of genetics, pathophysiology, functional neuroanatomy, and neuropsychopharmacology.
The goal of this Research Topic is to strengthen the dialogue, in order to fill the gap between neuroscience knowledge and the mental health unmet needs. We believe that brain model approaches from any scientific field will accelerate the progress across basic and clinical research, helping to provide more effective interventions in psychiatry.
We are happy to receive a range of manuscript types, such as original research, reviews, mini-reviews, opinions, and hypotheses on various topics related to Brain Modeling according to the Brain Priorities. Themes (but not limited to) that we endeavor to spotlight include:
· Predictive processing, predictive coding: The Predictive Brain
· Consciousness and energy in the brain, energy, and information in the brain: the Energetic Brain
· Metaphor, metonymy, conceptual metaphor theory, embodied cognition: The Metaphorical Brain
· Emotional intentionality, interoceptive predicting coding, The Emotional Brain
· Information theory, biotic information, liquid brains, swarm intelligence: The Computational Brain