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Does computational neuroscience need new synaptic learning paradigms?
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DOI：10.1016/j.cobeha.2016.05.012.png)
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Abstract
En 中文
Computational neuroscience is dominated by a few paradigmatic models, but it remains an open question whether the existing modelling frameworks are sufficient to explain observed behavioural phenomena in terms of neural implementation. We take learning and synaptic plasticity as an example and point to open questions, such as one-shot learning and acquiring internal representations of the world for flexible planning.
Keywords:
LONG-TERM POTENTIATION
MEMORY STORAGE CAPACITY
NEURAL-NETWORKS
DEPENDENT PLASTICITY
UNIFIED MODEL
MAP FORMATION
SPATIAL MAP
DOPAMINE
NEURONS
ORIENTATION
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