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1.
Nat Neurosci ; 21(6): 860-868, 2018 06.
Artigo em Inglês | MEDLINE | ID: mdl-29760527

RESUMO

Over the past 20 years, neuroscience research on reward-based learning has converged on a canonical model, under which the neurotransmitter dopamine 'stamps in' associations between situations, actions and rewards by modulating the strength of synaptic connections between neurons. However, a growing number of recent findings have placed this standard model under strain. We now draw on recent advances in artificial intelligence to introduce a new theory of reward-based learning. Here, the dopamine system trains another part of the brain, the prefrontal cortex, to operate as its own free-standing learning system. This new perspective accommodates the findings that motivated the standard model, but also deals gracefully with a wider range of observations, providing a fresh foundation for future research.


Assuntos
Aprendizagem/fisiologia , Córtex Pré-Frontal/fisiologia , Reforço Psicológico , Algoritmos , Animais , Inteligência Artificial , Simulação por Computador , Dopamina/fisiologia , Humanos , Modelos Neurológicos , Optogenética , Recompensa
2.
Nature ; 557(7705): 429-433, 2018 05.
Artigo em Inglês | MEDLINE | ID: mdl-29743670

RESUMO

Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go1,2. Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinforcement learning3-5 failing to rival the proficiency of mammalian spatial behaviour, which is underpinned by grid cells in the entorhinal cortex 6 . Grid cells are thought to provide a multi-scale periodic representation that functions as a metric for coding space7,8 and is critical for integrating self-motion (path integration)6,7,9 and planning direct trajectories to goals (vector-based navigation)7,10,11. Here we set out to leverage the computational functions of grid cells to develop a deep reinforcement learning agent with mammal-like navigational abilities. We first trained a recurrent network to perform path integration, leading to the emergence of representations resembling grid cells, as well as other entorhinal cell types 12 . We then showed that this representation provided an effective basis for an agent to locate goals in challenging, unfamiliar, and changeable environments-optimizing the primary objective of navigation through deep reinforcement learning. The performance of agents endowed with grid-like representations surpassed that of an expert human and comparison agents, with the metric quantities necessary for vector-based navigation derived from grid-like units within the network. Furthermore, grid-like representations enabled agents to conduct shortcut behaviours reminiscent of those performed by mammals. Our findings show that emergent grid-like representations furnish agents with a Euclidean spatial metric and associated vector operations, providing a foundation for proficient navigation. As such, our results support neuroscientific theories that see grid cells as critical for vector-based navigation7,10,11, demonstrating that the latter can be combined with path-based strategies to support navigation in challenging environments.


Assuntos
Biomimética/métodos , Aprendizado de Máquina , Redes Neurais de Computação , Navegação Espacial , Animais , Córtex Entorrinal/citologia , Córtex Entorrinal/fisiologia , Meio Ambiente , Células de Grade/fisiologia , Humanos
3.
Stud Health Technol Inform ; 169: 897-901, 2011.
Artigo em Inglês | MEDLINE | ID: mdl-21893876

RESUMO

For the assessment of physical activity, motion sensors have become increasingly important. To assure a high accuracy of the generated sensor data, the measurement error of these devices needs to be determined. Sensor variability has been assessed with various types of mechanical shakers. We conducted a small feasibility study to explore if a programmable robotic arm can be a suitable tool for the assessment of variability between different accelerometers (inter-device variability). We compared the output of the accelerometers GT1M and GT3X (both ActiGraph) and RT3 (Stayhealthy) for two different movement sequences.


Assuntos
Informática Médica/métodos , Monitorização Ambulatorial/métodos , Movimento (Física) , Robótica , Aceleração , Calibragem , Metabolismo Energético , Desenho de Equipamento , Humanos , Atividade Motora , Movimento , Reprodutibilidade dos Testes , Razão Sinal-Ruído
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