Machine learning for neuroscience

What is machine learning? Machine learning is a type of statistics that places particular emphasis on the use of advanced computational algorithms. As computers become more powerful, and modern experimental methods in areas such as imaging generate vast bodies of data, machine learning is becoming ever more important for extracting reliable and meaningful relationships and for making accurate predictions. Key strands of modern machine learning grew out of attempts to understand how large numbers of interconnected, more or less neuron-like elements could learn to achieve behaviourally meaningful computations and to extract useful features from images or sound waves. By the 1990s, key approaches had converged on an elegant framework called ‘graphical models’, explained in Koller and Friedman, in which the nodes of a graph represent variables such as edges and corners in an image, or phonemes and words in speech. The probabilistic relationships between nodes are represented by conditional probability tables or simple functions whose parameters are learned from the data. There are three main problems in fitting graphical models to data: inference, parameter learning and structure learning. The inference problem is how to infer the probable values of unobserved variables when the values of a subset of the variables have been observed, and is a problem that perceptual systems need to solve if they are to infer the hidden causes of their sensory input. The parameter-learning problem is how to adjust the parameters governing the way in which one variable influences another, so that the graphical model is a better fit to some observed data. In the brain, this is presumably done by changing synapse strengths. The structure-learning problem is how to decide which unobserved variables are needed and how they must be connected to model the correlations between observed variables. In the brain, evolution and early pruning of connections presumably have a large role to play in determining the structure. Could you provide a brief description of the methods of machine learning? Machine learning can be divided into three parts: 1) in supervised learning, the aim is to predict a class label or a real value from an input (classifying objects in images or predicting the future value of a stock are examples of this type of learning); 2) in unsupervised learning, the aim is to discover good features for representing the input data; and 3) in reinforcement learning, the aim is to discover what action should be performed next in order to maximize the eventual payoff.