Dataset Information

Intelligence-Augmented Rat Cyborgs in Maze Solving.

ABSTRACT: Cyborg intelligence is an emerging kind of intelligence paradigm. It aims to deeply integrate machine intelligence with biological intelligence by connecting machines and living beings via neural interfaces, enhancing strength by combining the biological cognition capability with the machine computational capability. Cyborg intelligence is considered to be a new way to augment living beings with machine intelligence. In this paper, we build rat cyborgs to demonstrate how they can expedite the maze escape task with integration of machine intelligence. We compare the performance of maze solving by computer, by individual rats, and by computer-aided rats (i.e. rat cyborgs). They were asked to find their way from a constant entrance to a constant exit in fourteen diverse mazes. Performance of maze solving was measured by steps, coverage rates, and time spent. The experimental results with six rats and their intelligence-augmented rat cyborgs show that rat cyborgs have the best performance in escaping from mazes. These results provide a proof-of-principle demonstration for cyborg intelligence. In addition, our novel cyborg intelligent system (rat cyborg) has great potential in various applications, such as search and rescue in complex terrains.

SUBMITTER: Yu Y

PROVIDER: S-EPMC4747605 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Intelligence-Augmented Rat Cyborgs in Maze Solving.

Yu Yipeng Y Pan Gang G Gong Yongyue Y Xu Kedi K Zheng Nenggan N Hua Weidong W Zheng Xiaoxiang X Wu Zhaohui Z

PloS one 20160209 2

Cyborg intelligence is an emerging kind of intelligence paradigm. It aims to deeply integrate machine intelligence with biological intelligence by connecting machines and living beings via neural interfaces, enhancing strength by combining the biological cognition capability with the machine computational capability. Cyborg intelligence is considered to be a new way to augment living beings with machine intelligence. In this paper, we build rat cyborgs to demonstrate how they can expedite the ma ...[more]

PMID: 26859299

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Project description:The neurobiology of postnatal hippocampal development in rodents is receiving increased attention as a means to address neurodevelopmental questions and to better understand the neural code(s) for spatial navigation in adulthood. We previously showed that spontaneous alternation (SA) in a Y-maze, which emerges at the end of the third postnatal week, was related to changes in fast glutamatergic synaptic transmission. In adults, oscillations in the hippocampal local field potential (LFP) (i.e., theta, 4-12 Hz; slow gamma, 25-55 Hz; and fast gamma, 65-100 Hz) have been shown to coordinate the activity of spatially tuned cell types in the hippocampus during route planning and execution. Other investigators have shown that theta activity matures during the first month of life. However, relationships between developmental alterations in gamma oscillations and cognitive function have not been investigated in juveniles, and the impact of developmental changes in excitatory synaptic transmission on network activity remains unclear. We implanted rats at postnatal day 14 to record LFPs from the synaptic layer of area CA1 during Y-maze exploration at postnatal Days 18, 19, 23, and 24. The positive allosteric modulator of AMPA receptors, CX614, or vehicle was administered prior to each test. We found that slow gamma peak frequency, but not peak power, remained constant across this age range. Fast gamma event rate increased with increasing age, while peak frequency decreased. AMPA receptor modulation impacted slow and fast gamma differently at different ages. Furthermore, gamma events showed relationships with novelty and movement speed that differed based on gamma sub-band (slow vs. fast). These results support the presence of environmentally-driven gamma oscillations in hippocampal network activity prior to the end of the third postnatal week of development. Differential refinement of slow and fast gamma occurring across the subsequent week supports involvement in the emergence of spatial navigation ability.

Project description:BackgroundDespite the emerging application of clinical decision support systems (CDSS) in pregnancy care and the proliferation of artificial intelligence (AI) over the last decade, it remains understudied regarding the role of AI in CDSS specialized for pregnancy care.ObjectiveTo identify and synthesize AI-augmented CDSS in pregnancy care, CDSS functionality, AI methodologies, and clinical implementation, we reported a systematic review based on empirical studies that examined AI-augmented CDSS in pregnancy care.MethodsWe retrieved studies that examined AI-augmented CDSS in pregnancy care using database queries involved with titles, abstracts, keywords, and MeSH (Medical Subject Headings) terms. Bibliographic records from their inception to 2022 were retrieved from PubMed/MEDLINE (n=206), Embase (n=101), and ACM Digital Library (n=377), followed by eligibility screening and literature review. The eligibility criteria include empirical studies that (1) developed or tested AI methods, (2) developed or tested CDSS or CDSS components, and (3) focused on pregnancy care. Data of studies used for review and appraisal include title, abstract, keywords, MeSH terms, full text, and supplements. Publications with ancillary information or overlapping outcomes were synthesized as one single study. Reviewers independently reviewed and assessed the quality of selected studies.ResultsWe identified 30 distinct studies of 684 studies from their inception to 2022. Topics of clinical applications covered AI-augmented CDSS from prenatal, early pregnancy, obstetric care, and postpartum care. Topics of CDSS functions include diagnostic support, clinical prediction, therapeutics recommendation, and knowledge base.ConclusionsOur review acknowledged recent advances in CDSS studies including early diagnosis of prenatal abnormalities, cost-effective surveillance, prenatal ultrasound support, and ontology development. To recommend future directions, we also noted key gaps from existing studies, including (1) decision support in current childbirth deliveries without using observational data from consequential fetal or maternal outcomes in future pregnancies; (2) scarcity of studies in identifying several high-profile biases from CDSS, including social determinants of health highlighted by the American College of Obstetricians and Gynecologists; and (3) chasm between internally validated CDSS models, external validity, and clinical implementation.

Dataset Information

Intelligence-Augmented Rat Cyborgs in Maze Solving.

Publications

Intelligence-Augmented Rat Cyborgs in Maze Solving.

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