I am currently doing a postdoc in computational and experimental neuroscience there, investigating models of decision-making in the basal ganglia, and conducting experiments corresponding to those models with rhesus monkeys.
My research spans the synthetic and biological world. I am passionate about understanding how the body and brain develop and interact to produce animals that can decide and act into the world. I am naturally drawn to studying early sensorimotor learning. I view robots as excellent scientific tools to test ideas and embody models.
It is difficult to understand how animals behave without understanding their brain. Thus, after an education in theoretical computer science and a PhD in developmental robotics, I went for a postdoc in neuroscience. I have been tremendously fortunate to find a position that allows me to do both computational and experimental work with animals. I am currently studying decision-making using a recurrent neural network of the basal ganglia, trying to understand economic decisions as combinations of Hebbian and reinforcement learning.
I am currently collaborating with Randall O’Reilly’s Computational Cognitive Neuroscience Laboratory from the University of Colorado Boulder. The goal of the collaboration is a reimplementation of the computation neuroscience framework Leabra in Python that is quantitatively equivalent to the one in the emergent software. In parallel, I work with the Jonathan Cohen’s team at the Princeton Neuroscience Institute to integrate the implementation into the upcoming PsyNeuLink framework.
I am committed to the produce open and reproducible science. All my publications are freely available and come along with their code, released under the Open Science License. I am involved in several replication efforts and I am a reviewer at the ReScience journal.
The Recode project aims at reimplementing experiments described in published scientific articles. Besides replicating published results, those implementations strive to be as short, as simple and as understandable as possible, so that all the code can be shown alongside the explanations.