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Editorial Material
Biology
Paul Cisek et al.
Summary: The nervous system is shaped by its evolutionary history, offering valuable insights into understanding the brain. This article highlights the importance of evolution in neuroscience and provides specific examples of how evolutionary theory enhances our understanding of neural mechanisms and brain organization.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
(2022)
Correction
Biochemical Research Methods
Talmo D. Pereira et al.
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Computer Science, Artificial Intelligence
Kenji Doya et al.
Summary: Advances in artificial intelligence and brain science will have a significant impact on society, providing immense benefits but also posing risks such as abuse and inequity. It is important to incorporate ethical principles in practical ways and learn from democratic mechanisms and open discussions.
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Multidisciplinary Sciences
Kwabena Boahen
Summary: The study proposes a new learning method for artificial intelligence called dendrocentric learning, which arranges the input signals along dendrites to overcome the thermal constraints of three-dimensional chips. This approach allows for efficient AI computation with low power consumption.
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Neurosciences
Jesse D. Marshall et al.
Summary: CAPTURE is a new behavioral monitoring system that combines motion capture and deep learning techniques to continuously track the movements of a rat's head, trunk, and limbs in freely behaving animals for up to a week. This system shows significant improvement in precision and robustness compared to existing convolutional network approaches, allowing researchers to comprehensively profile natural rodent behavior and investigate its variations across individuals. By using CAPTURE, researchers can explore a wider range of behaviors and contexts and gain new insights into natural behavior and its neural basis.
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Computer Science, Artificial Intelligence
Tom Macpherson et al.
Summary: Neuroscience and artificial intelligence have a long history of collaboration, with advances in computer processing power leading to the development of in silico neural networks that mimic biological systems. AI is now capable of advanced perceptual and cognitive abilities, and is increasingly being used as a tool for neuroscience research, transforming our understanding of brain functions. Excitingly, neuroscience-inspired AI also holds promise for understanding changes in brain networks that result in psychopathologies, and could even be utilized in treatment regimes.
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Multidisciplinary Sciences
Agrim Gupta et al.
Summary: The new framework of deep evolutionary reinforcement learning evolves agents with diverse morphologies to learn difficult tasks in complex environments, revealing insights into the relationships between environmental physics, embodied intelligence, and rapid learning evolution. Research indicates that environmental complexity fosters the evolution of morphological intelligence, and that more physically stable and energy efficient morphologies facilitate learning and control. Additionally, a morphological Baldwin effect is demonstrated, where evolution rapidly selects morphologies that learn faster, impacting the behaviors learned and expressed throughout generations.
NATURE COMMUNICATIONS
(2021)
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Engineering, Electrical & Electronic
Mike Davies et al.
Summary: Neuromorphic computing aims to develop chips inspired by biological neural circuits to process new knowledge, adapt, behave, and learn in real time at low power levels, with recent advancements showing promising results with Intel's Loihi processor. Compelling neuromorphic networks using spike-based hardware demonstrate significantly lower latency and energy consumption compared to state-of-the-art conventional approaches, solving diverse problems representative of brain-like computation.
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(2021)
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IEEE SIGNAL PROCESSING MAGAZINE
(2019)
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NATURE COMMUNICATIONS
(2019)
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CURRENT OPINION IN BEHAVIORAL SCIENCES
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NATURE COMMUNICATIONS
(2019)
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Kenneth O. Stanley et al.
NATURE MACHINE INTELLIGENCE
(2019)
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Alexander Mathis et al.
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(2018)
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Charles L. Ortiz et al.
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Brenden M. Lake et al.
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(2017)
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Demis Hassabis et al.
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Multidisciplinary Sciences
David Silver et al.
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Multidisciplinary Sciences
Volodymyr Mnih et al.
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Zsuzsa Akos et al.
BIOINSPIRATION & BIOMIMETICS
(2010)
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Biochemistry & Molecular Biology
P Lennie
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Computer Science, Artificial Intelligence
M Campbell et al.
ARTIFICIAL INTELLIGENCE
(2002)
Review
Endocrinology & Metabolism
D Attwell et al.
JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM
(2001)
