Sugandha Sharma, masters student graduate of the University of Waterloo's CTN, discusses her research and time in the laboratory of CTN Founding Director Chris Eliasmith as well as her current PhD research at MIT on the Generally Intelligent Podcast. Give it a listen.
- Computational Neuroscience Research Group (CNRG)
- Spafford Neurobiology Research Laboratory
- Computational Epistemology Lab (CEL)
- Anderson Group
- BRAIN Lab
- Campbell Group
- Danckert Attention and Action Group (DAAG)
- Marriott Lab
- Laboratory for Research in Reasoning & Decision Making
- Ingalls Group
- Waterloo Configurables Architectures Group (WatCAG)
- Neurocognitive Computing Lab
Lab Head: Dr. Chris Eliasmith
Research Description: The Computational Neuroscience Research Group (CNRG) is dedicated to developing and using a unified mathematical framework for modeling large-scale neurobiological systems. We are currently applying this framework to specific projects in sensory processing, motor control, and cognitive function. Our on-going work encompasses purely theoretical issues, specific biologically realistic models (e.g., of Parkinson's Disease, hemineglect, human linguistic inference, rodent navigation, among others), and practical applications (e.g., automatic text classification, clustering, and data mining). These modeling efforts are carried out in collaboration with various experimental groups who use techniques that span the range from single cell physiology to functional magnetic resonance imaging (fMRI).
Lab Head: Dr. David Spafford
Research Description: In the Spafford laboratory, we principally study voltage-gated calcium channels. Calcium channels participate in brain functions, such as synaptic transmission, neuronal plasticity, patterned nerve activity underlying rhythmic behaviours, outgrowth of neurons and synapse formation. Lab trainees are exposed to multidisciplinary research that spans molecular physiology and biophysics, to cell and integrative physiology underlying animal behaviour. Students have access to techniques in electrophysiology as well as in molecular and cell biology, protein biochemistry, fluorescence microscopy and tissue culture.
Lab Head: Dr. Paul Thagard
Webpage: Computational Epistemology Lab
Research Description: Paul Thagard works with students from several different departments to develop computational models of human reasoning, including scientific, legal, and moral thinking. Recent work models how brains perform decision making, scientific discovery, and other emotional inferences. He is also concerned with the philosophical implications of advances in neuroscience and is currently working on topics such as emotional consciousness, the implications of mirror neurons for the problem of other minds, conscience and moral intuition, the mind-brain identity theory, and the nature of mental illness.
Lab head: Dr. Britt Anderson
Webpage: Anderson Group
Research Description: Precise computational models make cognitive theories concrete and yield specific experimental predictions. By combining psychophysical studies of normal and brain damaged human subjects with the computational tools of theoretical neuroscience, we study the functional architecture of, and basis for, higher level cognitive capacities such as spatial attention and general cognitive ability.
Lab head: Dr. Bryan Tripp
Webpage: Bryan Tripp Lab
Research Description: The Tripp lab researches visual and motor systems. Vision and motor control are highly accessible to both detailed experimentation and detailed modelling. From a practical perspective, biological vision and motor systems vastly outperform artificial systems in many tasks, so that an improved understanding of these systems should lead to substantial technological advances. The central goal of the lab is to develop increasingly realistic computer/robotic models of the dorsal visual pathways and the networks that control eye and limb motion.
Group Head: Dr. Sue Ann Campbell
Webpage: Campbell Group
Research Description: The research of this group focuses on understanding how rhythms are generated in the brain. We approach this problem by formulating mathematical models for networks of neurons and studying their properties using dynamical systems theory, numerical simulation and other computational tools. Current work centres on investigating how certain model properties (coupling, oscillation characteristics, presence of conduction and synaptic time delays, connectivity of the network) affect the ability of the network to producing synchronized rhythms.
Lab Head: Dr. James Danckert
Webpage: Danckert Attention and Action Group
Research Description: The DAAG examines the role of parietal cortex in the control of visually guided actions, attention and consciousness. One pivotal aspect of the group's research involves exploring the consequences of injury to the right parietal cortex which typically leads to a disorder known as unilateral neglect. Neglect patients behave as if one half of their world has simply ceased to exist. These patients display unique impairments in visuomotor control, deployment of attention and conscious representation of the external world. Recently, we have made use of computational models to disentangle the contributions of each of these impairments to the disorder. In addition, we are interested in using computational models to understand how the brain perceives the passage of time - a skill that is involved in all aspects of condition and control.
Lab Head: Dr. Paul Marriott
Webpage: Marriott Lab
Research Description: I am interested in statistical modelling of spike train data from neuron firing experiments. In these problems we are typically working with very large amounts of point process data where the spike train represents the firing times of neurons. Issues which we are examining are possible state space models and the important question of modelling dependence between large numbers of neurons.
Lab Head: Dr. Jonathan Fugelsang
Research Description: The Laboratory for Research in Reasoning & Decision Making addresses several topics in cognitive psychology and
cognitive neuroscience, though a primary focus in higher level cognition. Recently, the lab has predominantly focused on how we integrate
multiple sources of information when making complex decisions. These decisions may involve analogical, causal, deductive, or inductive reasoning
processes. To understand the mechanisms underlying these processes, the lab employs both behavioural and functional brain imaging methodologies.
Group Head: Dr. Brian Ingalls
Webpage: Ingalls Group
Research Description: Our group uses tools and methods from control theory to reverse-engineer cellular and intracellular behaviour. This work involves mathematical analysis of dynamic models of biochemical networks. Our research is devoted both to the construction of such models and the development of novel methods for their investigation.
Lab Head: Dr. Nachiket Kapre
Research Description: Dr. Kapre, is broadly interested in understanding and exploiting the potential of spatial parallelism for implementing computation using reconfigurable architectures such as FPGAs. Reconfigurable computing has now come of age with the multi-billion dollar acquisition of Altera by Intel, and rapid adoption of FPGAs in the cloud at Microsoft, Amazon, Huawei, Baidu, Alibaba among other cloud providers. With the rising computing demands of machine learning workloads coupled with the pending demise of Moore's Law, there has never been a more exciting time to work in this field than today. Prof Kapre hopes to leverage his membership of CTN to formulate research problems at the intersection of neuromorphic computing and hardware design/automation.
Lab Head: Dr. Jeff Orchard
Webpage: Neurocognitive Computing Lab
Research Description: We study neural networks and artificial intelligence, with an eye on what we can learn about the brain. Deep neural networks have begun to rival human abilities in perceptual tasks. How do these networks relate to our own perceptual systems, like vision, or audition?
We try to unravel how the brain learns. This pursuit often leads to neural-network learning algorithms that involve feed-back connections and dynamical systems methodologies. The challenge is to train these networks in a deep architecture and investigate how they react to ambiguous stimulus or optical illusions.