Our focus is on fundamental basic research, with a portfolio that spans genes to behaviour.
Investing in basic science that is not driven by one single disorder or the quest for a ‘cure’ is essential to meeting the challenge of identifying, observing, manipulating and ultimately understanding the circuits that generate behaviour. The knowledge accumulated through these studies will underpin many of the breakthroughs in neurological disorders.
Our portfolio spans four key areas, all of which are essential to meeting the challenge of understanding the circuits that generate behaviour:
- Circuit Assembly and Organisation An essential goal for neuroscience is to know how neurons are connected to form circuits. Using a range of methods to locate and trace circuits and to perturb their function provides precise cellular and molecular definition of the identified circuits, neurons and synapses, and will permit the elucidation of connectivity. Approaches in our portfolio include multiple scales of optical and electron microscopic imaging as well as serial reconstruction of brain sections using fast automated systems.
- Information Processing: from Synapse to Neuron to Circuit We need to explore and understand how information is transmitted and transformed from neuron to neuron, and within neural circuits, how signals are compartmentalised, integrated, timed and faithfully transmitted throughout the brain. The use of new light activated genetically-encoded tools is enabling exquisite manipulation of activity within the circuit to assess the effect on behaviour.
- Circuits and Behavioural Systems We need to utilise the unique aspects of many neural systems. Model organisms like the worm, fly, fish and rodents are essential to understand sensory processing, motor systems, fear, reward, and emotional states, memory, attention and decision making. This involves analysis of complex, freely-moving behaviour such as sexual, social and affective interactions or spatial exploration.
- Computational and Theoretical Neuroscience There is a need to ground experimental neuroscience in a firm theoretical context. As theoreticians propose which aspects of the observed patterns of activity are crucial, this can be tested experimentally by perturbing only those aspects of the total observed pattern. These computational models will contribute not only to refining data analysis, but will also lead to new theories about network function, and generate new predictions for further experiments.