Computational neuroscience, neural network models of perceptual and cognitive processes including cortical and hippocampal memory systems, spatial memory, semantic memory organization, frontal executive control of memory.
Builds mathematical and computational models of neural processing, with a particular emphasis on representation and learning. The main focus is on reinforcement learning and unsupervised learning, covering the ways that animals come to choose appropriate actions in the face of rewards and punishments, and the ways and goals of the process by which they come to form neural representations of the world. The models are informed and constrained by neurobiological, psychological and ethological data.
The laboratory addresses a variety of basic issues in the analysis and representation of visual imagery. 1) construction of mathematical theories for the representation of visual information, 2) development of functional models for biological visual processing, and 3) creation of novel algorithms for image processing and computer vision applications.
Features a wealth of background knowledge about learning and memory (with an emphasis on associative learning) together with published and unpublished original research on the fruitfly Drosophila and the sea-slug Aplysia.
Goal is to understand the structure, function and plasticity of the mammalian visual system. Uses a combination of electrophysiological, psychophysical, and computational techniques to analyze how visual information is coded in the spiking activity of neurons in the visual cortex.
Our goal is to devise learning rules that can develop a feature-detector hierarchy similar to that proposed by Fukushima et al. (1983) in order to recognize objects independent of location, scale, or orientation.
Relationship between the autonomic nervous system and the vascular system, mechanisms underlying disease in human arteries, cerebral and coronary arteries. Relevant to clinical medicine. Saphenous vein for CABG, and neurodegenerative diseases. University College London.
What are the mechanisms of learning and memory? How are actions and experiences encoded in the activity patterns of neurons in the brain? In the Wilson Lab we are addressing these questions through multineuron recording from the hippocampus and other brain areas of rats and mice during active behavior.
Goal is to develop methods for evolving embedded intelligent systems, such as Autonomous Robots, capable of adaptation to physical environments. Interested in artificial sensory-motor systems that display life-like properties and are based upon bio-inspired mechanisms (genetics, cellular biology, neural networks, bio-morphic engineering).
Digital signal processing (DSP) is applied to the analysis of electro-physiological signals (such as EEG), with emphasis on human brain electrical activity. From the State Committee for Scientific Research; Warsaw, Poland.
Studies processing in visual neurons of Drosophila as well as effects of molecular components on neural computations and sensory adaptation. Influence of rearing and environment on signalling is studied and signalling during natural stimulation is analyzed and modeled.
Neuroscientist doing both experiments and theory at the Institute of Neurology, London. Specializes in Bayesian Statistics and Statistics of Natural scenes. Applications to Visual, Somatosensory, Auditory and Motor problems.
The Neural Imaging Lab uses cellular imaging techniques in combination with electrophysiology and genetic approaches to study local biochemical signalling at excitatory synapses of different type of central neurons.