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Found 7 papers, 1 papers with code
Action-modulated midbrain dopamine activity arises from distributed control policies
The model provides a computational account for numerous experimental findings about dopamine activity that cannot be explained by classic models of reinforcement learning in the basal ganglia.
Sequence Learning and Consolidation on Loihi using On-chip Plasticity
In this work we develop a model of predictive learning on neuromorphic hardware.
Learning to Learn with Feedback and Local Plasticity
Interest in biologically inspired alternatives to backpropagation is driven by the desire to both advance connections between deep learning and neuroscience and address backpropagation's shortcomings on tasks such as online, continual learning.
The effects of neural resource constraints on early visual representations
Neural representations vary drastically across the first stages of visual processing.
A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs
The visual system is hierarchically organized to process visual information in successive stages.
The emergence of multiple retinal cell types through efficient coding of natural movies
Also, we train a nonlinear encoding model with a rectifying nonlinearity to efficiently encode naturalistic movies, and again find emergent receptive fields resembling those of midget and parasol cells that are now further subdivided into ON and OFF types.
Pre-training Attention Mechanisms
Recurrent neural networks with differentiable attention mechanisms have had success in generative and classification tasks.
