A Shared Vision for Machine Learning in Neuroscience

Abstract

With ever-increasing advancements in technology, neuroscientists are able to collect data in greater volumes and with finer resolution. The bottleneck in understanding how the brain works is consequently shifting away from the amount and type of data we can collect and toward what we actually do with the data. There has been a growing interest in leveraging this vast volume of data across levels of analysis, measurement techniques, and experimental paradigms to gain more insight into brain function. Such efforts are visible at an international scale, with the emergence of big data neuroscience initiatives, such as the BRAIN initiative (Bargmann et al., 2014), the Human Brain Project, the Human Connectome Project, and the National Institute of Mental Health's Research Domain Criteria initiative. With these large-scale projects, much thought has been given to data-sharing across groups (Poldrack and Gorgolewski, 2014; Sejnowski et al., 2014); however, even with such data-sharing initiatives, funding mechanisms, and infrastructure, there still exists the challenge of how to cohesively integrate all the data. At multiple stages and levels of neuroscience investigation, machine learning holds great promise as an addition to the arsenal of analysis tools for discovering how the brain works.

Keywords: explainable artificial intelligence; machine learning; reinforcement learning.

Figure 1.

Model for data-driven science supporting hypothesis-driven science. Within the framework of hypothesis-driven science, machine learning can be used to generate hypotheses to be subsequently tested.

Figure 2.

Hold-out trial versus out-of-sample model validation. Validation commonly accomplished within animal. For example, a model might be trained on subsets of each animal's data (top, green) and tested on the remainder of data from the same animal (top, yellow). Here, we propose training on a subset of the animals (bottom, green), and testing on an independent set of animals (bottom, yellow).

Figure 3.

XAI. Here we present a vision for leveraging machine learning toward developing unified models. The criteria for models achieving XAI are that they must be based on measurable brain biology and be descriptive, predictive, generalizable, and convergent.