How do biological neural networks learn to predict their environment?

The most popular theory of how the brain works states that animals learn a predictive model of their environment. Amongst other things, such predictive model allows them to efficiently navigate and to choose the appropriate actions to reach their goals. The neuroscience community strongly believes that the highly complex network of neurons found in the brain is responsible for encoding such a predictive model. However, it is not yet clear how the connectivity structure in this network emerges. At birth, this network is severely underdeveloped and cannot predict much, but as the animal explores more of it's environment, the connectivity structure of such network changes and the animal starts being able to predict and appropriately act upon the environment. The physical mechanisms underlying this adaptation remain poorly understood. In this project, we try to gain a better understanding of such adaptability mechanisms by simulating models of biological neural networks and carefully observing how they learn to predict events in simple synthetic environments.

The topic of learning predictive models of the environment is not only relevant to neuroscience but also for any scientific discipline trying to model some phenomena. Machine learning, in particular, has been pursuing such ideas for many years and has produced an extensive mathematical formalism about predictive modeling. Most useful practical models are crafted by hand but a main research goal of machine learning is to produce a single algorithm that can output an arbitrary model from some data. In fact, a said algorithm is believed to be operating in the brain and changing its connections to match the true predictive model of the environment. The most popular algorithm in machine learning right now operates in networks of neurons that are loosely modeled after the brain, also called Artificial Neural Networks (ANNs). Such models, however, do not match observations made by neurophysiology and neuroanatomy and are a very poor approximation of the biological neural networks operating in the brain. Furthermore, ANNs cannot cope very well with temporal correlations, especially for video perception and prediction, and cannot explain how the brain copes so well with them. This also poses a fundamental issue in the field of machine learning and a solution could produce advances in fields like robotics, since it is very hard nowadays to make robots that can learn the predictive and casual (predict with actions) structure of the environment.

In this research, we will focus on understanding the brain but also on building predictive systems which can be evaluated under a machine learning framework. We simulate biologically plausible networks in simple synthetic environments and try to understand how they learn the spatio-temporal factors of those simple environments, as well as its causal dynamics. Instead of taking an optimization approach, which changes the connectivity structure based on an objective function, we aim to understand how simple biologically plausible rules perturb the connectivity of the network and how such perturbations aid correct learning. Following with the principles of computational neuroscience we will analyse the models studied under the frameworks of linear algebra and probability theory to come up with understandable hypothesis about the physical behaviour of general biological neural networks.