The Mejias lab uses a combination of theoretical techniques, computational modeling and data analysis to scout the neural mechanisms underlying large-scale brain communication, hierarchical brain dynamics, sensory predictions and distributed cognitive functions. You can learn more about our research and the organization of the lab below.
Research line #1: The role of neural heterogeneity in neural dynamics
Computational models have traditionally considered that neurons in the brain are fairly homogeneous –for example, many network models ignore the existence of different subtypes of neurons, or they neglect the intrinsic variability of physiological properties even within a given neuron subtype. While washing heterogeneity away is convenient to build useful simplifications of the brain, it is now clear that neural heterogeneity plays a major role in neural computations. Our previous work identified neural heterogeneity as a key factor in rate and temporal coding (Mejias and Longtin 2012; 2014) and also uncovered interactions between specific cell types which give rise to paradoxical neural dynamics (Garcia del Molino et al., 2017). We currently study the effects of neural heterogeneity on perception and cognition.
Research line #2: Neural dynamics across multiple scales
Computational models are a perfect way to link neural phenomena at different scales. In previous work, we have built models of the macaque brain spanning several scales, from microscopic circuits to full-brain networks, by incorporating in the models precise anatomical and electrophysiological data. These models correctly predict and reproduce neural dynamics across multiple spatial and temporal scales (Mejias et al., Science Advances 2016), provide biologically plausible solutions for problems in efficient brain communication (Joglekar et al., Neuron 2018) and explore the emergence of working memory and other cognitive functions in distributed brain networks (Mejias and Wang, 2021). We are expanding this approach to model the brain of other animals (such as rodents and humans) and also investigating the use of such models in computational psychiatry, for example to identify potential biomarkers in brain disorders.
Research line #3: Recurrent neural network for perception and cognition
Recent work has shown that properly trained recurrent neural networks (RNNs) may be effectively used as a model to explain the computations underlying different perceptual and cognitive functions in the brain (Yang et al. PLoS Comput. Biol. 2015; Yang et al., Nature Neuroscience 2019). Our lab is currently developing biologically plausible RNNs (for example, by explicitly considering excitatory and inhibitory neurons, as in our preliminary work by Dora et al.) to model behavioral tasks such as multisensory integration and decision making. Our goal is to use these models to link the behavioral output observed in animals with underlying neural computations which give rise to such behavior.
Research line #4: Neural mechanisms of sensory prediction
A final cornerstone of the lab is the understanding of the neural mechanisms of sensory prediction. There is abundant evidence of neural circuits in the brain which generate an “internal model of the world” and generate predictions to match the incoming sensory stimulation. A well-known example which we previously studied is the electrosensory circuit of electric fish, which cancel out redundant or unimportant electrosensory signals (Mejias et al., 2013). A more general scenario includes the theory of predictive coding, by which our brains generate predictions to cancel out familiar sensory signals. As a consequence, novel and unpredictable stimuli –which are not cancelled —are the most effective for driving learning in higher brain areas. Our lab has a strong focus in exploring the neural mechanisms of sensory predictions, and how these predictions may be useful to explain multisensory representations in the brain.
Our view is that a close collaboration between experimental and computational neuroscientists is key to successfully unravel the secrets of the brain. Our lab is uniquely well positioned in this sense: we have close interactions with the Olcese, Bosman, Suzuki, de Gee and Pennartz labs, and together we form the Cognitive and Systems Neuroscience Group. The Mejias lab specializes in theoretical and computational approaches to study the brain at different scales –from small neural circuits to full-brain models. With our work, we complement the efforts of our experimental colleagues and also develop our own independent research lines sketched above.
I am assistant professor and head of the Computational Neuroscience Lab at the Cognitive and Systems Neuroscience Group of the University of Amsterdam. With a background in physics and mathematics, I obtained a PhD in computational neuroscience from the University of Granada (Spain) in 2009. During my PhD, I was also visiting researcher at the Université Paris V René Descartes (now Université de Paris, France). I then went on to work as a postdoctoral researcher at the University of Ottawa (Canada) and New York University (USA), and as a visiting researcher at the East China Normal University/ NYU Shanghai (China), before joining the University of Amsterdam in 2017. The research of my team is focused on the theoretical and computational study of data-constrained multi-scale brain networks during perception and cognition, and the subsequent development of our advances into brain simulation technologies. Our interest spans several brain functions, including working memory, multisensory integration and predictive coding, as well as brain disorders impairing such functions. I often partake in committee or organizational duties for leading conferences like Cosyne and the OCNS annual meeting, and I currently serve as external faculty member at the Institute Carlos I for Theoretical and Computational Physics in Granada and the European Institute of Theoretical Neuroscience in Paris.
The lab is currently constituted by the following people (indices indicate joint supervision with (1) Matthan Caan, (2) Cyriel Pennartz, (3) Sander Bohte, (4) Conrado Bosman, (5) Niels Cornelisse):
Do you want to join the lab? Candidates at any level (Master, PhD, postdoc) can contact me by email. We usually have several projects available for interested candidates, and you are also welcome to bring your own ideas and interests to the table. For PhD and postdocs, I am happy to support fellowship applications if we have sufficient time for planning.
Funding and support. The lab is currently supported by the following funding sources:
Collaborators. We collaborate with other labs and researchers across the Netherlands and abroad, including: