Deep(er) Learning

Animals successfully thrive in noisy environments with finite resources. The necessity to function with resource constraints has led evolution to design animal brains (and bodies) to be optimal in their use of computational power while being adaptable to their environmental niche. A key process unde...

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Veröffentlicht in:	The Journal of neuroscience 2018-08, Vol.38 (34), p.7365-7374
Hauptverfasser:	Srinivasan, Shyam, Greenspan, Ralph J, Stevens, Charles F, Grover, Dhruv
Format:	Artikel
Sprache:	eng
Schlagworte:	Afferent Pathways - physiology Animals Artificial neural networks Association Learning - physiology Avoidance Learning - physiology Background noise Brain Case depth Circuit design Computation Computational neuroscience Conditioning, Classical - physiology Conditioning, Operant - physiology Deep Learning Design Design optimization Drosophila melanogaster - physiology Environment Fruit flies Machine learning Models, Neurological Mushroom Bodies - physiology Nerve Net - physiology Neural networks Neuropil - physiology Odorants Olfactory discrimination learning Olfactory Perception - physiology Olfactory Receptor Neurons - physiology Reinforcement, Psychology Reward Signal Detection, Psychological - physiology Signal-To-Noise Ratio Synapses - physiology Viewpoints
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