Input-output mappings

…capable of implementing sophisticated input-output mappings, which we characterize analytically. Learning from extensive datasets is achieved through the stabilization of fixed points via a fully distributed and local learning process, implemented at the single-neuron level. This simple mechanism yields performance comparable to that of conventional feedforward deep neural networks train

Algorithms generate input-output mappings through step-by-step operations on representations. Cognitive scientists use algorithms to explain mental processes. For example, they use tree-search algorithms to explain planning, reinforcement learning algorithms to explain exploration, and Bayesian algorithms to explain categorization. The standard approach is to search for parts in the brain corresponding to the steps of the algorithm. But we haven’t been able to find many such parts. This has led some to deny that algorithms are useful to systems neuroscience, and to interpret neural systems usi

Since the theories of Marr, Ito, and Albus, the cerebellum has provided an attractive well-characterized model system to investigate biological mechanisms of learning. In recent years, theories have been developed that provide a normative account for many features of the anatomy and function of cerebellar cortex and cerebellum-like systems, including the distribution of parallel fiber-Purkinje cell synaptic weights, the expansion in neuron number of the granule cell layer and their synaptic in-degree, and sparse coding by granule cells. Typically, these theories focus on the learning of random