A theory for how sensorimotor skills are learned and retained in noisy and nonstationary neural circuits

By Robert Ajemian, A. D’Ausilio, H. Moorman, Emilio Bizzi. 2013

75 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

Citations in the corpus

Towards Continual Reinforcement Learning: A Review and Perspectives (2020, background): “Finally, the work of Ajemian et al. (2013) has demonstrated the role that noisy computations in the human brain may have in avoiding catastrophic forgetting.”
Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference (2018, background): “, 2018), the link to weight sharing (French, 1991; Ajemian et al., 2013), and the idea of influencing gradient alignment for continual learning before (Lopez-Paz & Ranzato, 2017).”
Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex. (2017, unknown): “Incorporation of Representations for Newly Learned Associations into Existing Population Activity Patterns Computational models suggest that an advantage of dynamic neuronal representations could be the flexibility of incorporating new information into the population (Ajemian et al., 2013; Rokni et ”
Learning-induced autonomy of sensorimotor systems (2014, methodology): “m in the Curve Fitting Toolbox with option “Robust” and type “Lar”): MT = D1e −tκ +D2e −tλ , (1) where t is time, κ is the exponential dropoff parameter (which we call the “learning parameter”) used to describe the fast rate of improvement, λ is the exponential dropoff parameter used to describe the”
Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills (2015, background): “Having robots learn a large set of skills, however, has been an elusive challenge because they need to learn new skills without forgetting previously acquired skills [1 – 3].”