Simply put, the goal of my research is to describe the neuronal circuitry of the retina. The organization of the mammalian retina is certainly complex but it is not chaotic. Although there are many cell types, most adhere to a relatively constant morphology and they are distributed in non-random mosaics. Furthermore, each cell type ramifies at a characteristic depth in the retina and makes a stereotyped set of synaptic connections. In other words, these neurons form a series of local circuits across the retina. The next step is to identify the simplest and commonest of these repeating neural circuits. They are the building blocks of retinal function. If we think of it in this way, the retina is a fabulous model for the rest of the CNS. We are interested in identifying specific circuits and cell types that support the different functions of the retina. For example, there appear to be specific pathways for rod and cone mediated vision. Rods are used under low light conditions and rod circuitry is specialized for high sensitivity when photons are scarce (when you’re out camping, starlight). The hallmark of the rod-mediated system is monochromatic vision. In contrast, the cone circuits are specialized for high acuity and color vision under relatively bright or daylight conditions. Individual neurons may be filled with fluorescent dyes under visual control. This is achieved by impaling the cell with a glass microelectrode using a 3D micromanipulator. We are also interested in the diffusion of dye through coupled neuronal networks in the retina. The dye filled cells are also combined with antibody labeling to reveal neuronal connections and circuits. This triple-labeled material may be viewed and reconstructed in 3 dimensions by multi-channel confocal microscopy. We have our own confocal microscope facility in the department and timeslots are available to students in my lab.

The human brain is a complex network of neuronal connections. The precise arrangement of these connections, otherwise known as the topology of the network, is crucial to its functioning. Recent efforts to understand how the complex topology of the brain has emerged have used generative mathematical models, which grow synthetic networks according to specific wiring rules. Evidence suggests that a wiring rule which emulates a trade-off between connection costs and functional benefits can produce networks that capture essential topological properties of brain networks. In this webinar, Professor Alex Fornito and Dr Stuart Oldham will discuss these previous findings, as well as their own efforts in creating more physiologically constrained generative models. Professor Alex Fornito is Head of the Brain Mapping and Modelling Research Program at the Turner Institute for Brain and Mental Health. His research focuses on developing new imaging techniques for mapping human brain connectivity and applying these methods to shed light on brain function in health and disease. Dr Stuart Oldham is a Research Fellow at the Turner Institute for Brain and Mental Health and a Research Officer at the Murdoch Children’s Research Institute. He is interested in characterising the organisation of human brain networks, with particular focus on how this organisation develops, using neuroimaging and computational tools.

During learning, populations of neurons alter their connectivity and activity patterns, enabling the brain to construct a model of the external world. Conventional wisdom holds that the durability of a such a model is reflected in the stability of neural responses and the stability of synaptic connections that form memory engrams. However, recent experimental findings have challenged this idea, revealing that neural population activity in circuits involved in sensory perception, motor planning and spatial memory continually change over time during familiar behavioural tasks. This continual change suggests significant redundancy in neural representations, with many circuit configurations providing equivalent function. I will describe recent work that explores the consequences of such redundancy for learning and for task representation. Despite large changes in neural activity, we find cortical responses in sensorimotor tasks admit a relatively stable readout at the population level. Furthermore, we find that redundancy in circuit connectivity can make a task easier to learn and compensate for deficiencies in biological learning rules. Finally, if neuronal connections are subject to an unavoidable level of turnover, the level of plasticity required to optimally maintain a memory is generally lower than the total change due to turnover itself, predicting continual reconfiguration of an engram.