Topic: pattern recognition

A central problem in biological learning is how information about the outcome of a decision or behavior can be used to reliably guide learning across distributed neural circuits while obeying biological constraints. This “credit assignment” problem is commonly solved in artificial neural networks through supervised gradient descent and the backpropagation algorithm. In contrast, biological learning is typically modelled using unsupervised Hebbian learning rules. While these rules only use local information to update synaptic weights, and are sometimes combined with weight constraints to reflec

Neural networks can learn and recognize subtle correlations in high dimensional inputs. However, neural networks are simply many-body systems with strong non-linearities and disordered interactions. Hence, many-body physical systems with similar interactions should be able to show neural network-like behavior. Here we show neural network-like behavior in the nucleation dynamics of promiscuously interacting molecules with multiple stable crystalline phases. Using a combination of theory and experiments, we show how the physics of the system dictates relationships between the difficulty of the

Deep learning is an emerging computational approach inspired by the human brain’s neural connectivity that has transformed machine-based image analysis. By using histopathology as a model of an expert-level pattern recognition exercise, we explore the ability for humans to teach machines to learn and mimic image-recognition and decision making. Moreover, these models also allow exploration into the ability for computers to independently learn salient histological patterns and complex ontological relationships that parallel biological and expert knowledge without the need for explicit direction