2015 – 2018 WildCog: Evolution and local adaptation of cognitive abilities and brain structure in the wild
Cognition plays a critical role in how organisms interact with their social and ecological environment, and while the mechanisms underlying cognitive processes are becoming clearer, we still know little about the evolution of cognitive traits in natural populations. Cognitive abilities of organisms implicitly lie at the core of many fields since they determine in part how organisms compete with each other and acquire mates, how they find food and avoid being eaten, how they flexibly adjust to new contexts, and how they navigate landscapes. Many different cognitive capacities exist and show within and across species variation; the extent to which this variation results from ecological imperatives faced by each species or population remains to be determined. Furthermore, new methods in neurosciences suggest that we may now be able to link variation in specific cognition functions to variation in neurological structures. An understanding of the selective processes that shape variation in brains and cognitive abilities could provide major advances in our understanding of variations in cognitive abilities, both within and among species. To do this, we have gathered an interdisciplinary research team to conduct a novel research project that will combine measurements of fitness, individual variation in cognitive performance, and neuroanatomy of brain structure and function. Combining data on fitness, cognition, and brain physiology on the same individuals will give us an unprecedented understanding of how selection operates on and shapes variation in cognition.
Supported by Human Frontier Science Program (RGP0006/2015).
2014 – 2017 Scaling up computational models of visual processing in cortex
The goal of this research is to identify functionally-relevant biophysically-plausible computational mechanisms beyond those currently implemented in existing models of the visual cortex and other deep learning architectures. Rather than dealing with more realistic but elaborate spiking neuron models, we propose to develop novel mathematical idealizations that naturally extends current system-level models of vision and related deep networks while still being amenable to many learning algorithms and interpretable in the context of biophysical mechanisms. Our approach, which consists in building phenomenological models of biophysically-realistic operations to scale up computational models of the visual cortex (and other deep learning nets) will thus help bridge a major gap between detailed biophysical models of spiking neurons and state-of-the-art machine learning and computer vision.
Supported by DARPA young investigator award (N66001-14-1-4037).
2013 – 2018 Computational mechanisms of rapid visual categorization
The goal of this research is to identify the perceptual principles and model the neural mechanisms underlying rapid visual categorization. By forcing processing to be fast, rapid visual categorization paradigms help isolate the very first pass of visual information before more complex visual routines take place. Hence, understanding ‘vision at a glance’ is arguably a necessary first step before studying natural everyday vision where eye movements and attentional shifts are known to play a key role. Specifically, this proposal will lead to the development of a computational neuroscience model of rapid visual recognition in the primate visual system, which is both consistent with physiological properties of cells in the visual cortex and able to predict behavioral responses (both correct and incorrect responses as well as reaction times) from human participants across a range of conditions.
Supported by NSF early career award (IIS-1252951).
2011 – 2014 Towards a biologically-inspired vision system for the control of navigation in complex environments
The goal of this project is to identify the perceptual principles and model the neural mechanisms responsible for the visual control of primate navigation as a novel approach to navigation for ground vehicles with minimal sensing requirements. Specifically we will develop a neurally plausible, quantitative model of visual perception, which by incorporating bottom-up and top-down cortical connectivity as well as mechanisms of 3D shape and motion processing, should ultimately describe human performance in navigation tasks as well as the underlying circuits and neural mechanisms.
Supported by ONR (N000141110743).
2011 – 2013 Automated monitoring and analysis of human and animal behavior
Neurobehavioural analysis of mouse phenotypes requires the monitoring of mouse behaviour over long periods of time. We are currently developing trainable computer vision systems enabling the automated analysis of complex behaviors in mouse, monkey and humans. Link to the Rodent Neuro-Developmental Behavioral testing facility website here.
Supported by the Robert J. and Nancy D. Carney Fund for Scientific Innovation.
2010 – 2011 Towards a human-level neuromorphic artificial visual system
The goal of this project is to create intelligent general-purpose cognitive vision algorithms inspired from the primate brain to alleviate the limitations of human-based analysis of complex visual scenes. The human brain is arguably the most advanced information processing device in existence, with a large fraction of its computing power devoted to sensory processing. Despite the importance of image processing for the control of animal behavior, after decades of intensive research in the computer vision communities, artificial vision systems remain poor cousins to their biological counterparts. The assumption underlying this research is that a concerted effort to understand and emulate the information-processing strategies used in biological sensory systems will lead to substantial near-term technological benefits. Importantly, based on our own experience with studying mechanisms of image formation, attention, and object recognition in the primate brain, we believe that a quantum leap advance is only possible through research and development of a new breed of neuroscience-inspired algorithms.
Supported by DARPA (N10AP20013).