We have built mathematical models that capture how single neurons and neural circuits homeostatically control their signalling components and structure. We discovered that mechanisms that control neural properties face a dilemma: either they enforce rapid and precise changes and risk becoming unstable, or they tolerate imprecision in neural signalling properties.
We have developed mathematical theories and computer models of brain circuits that allow continual reconfiguration to occur without destroying stored memories. So far this has resulted in a surprising finding: excess, 'redundant' connections in the brain can enable faster and more accurate learning, even will imperfect learning rules. This theory explains experimental measurements of neural circuits, which show that many parts of the brain have many redundant paths between the same neurons. However, the theory also predicts that if neural connections are unreliable (which they are in a living system) then there is an upper limit to the benefit of having redundant pathway, above which learning becomes impaired. These results shed new light on biological brain function as well as suggesting ways that artificial neural networks can be improved, making new connections between neuroscience and artificial intelligence.
We have analysed existing neural data that shows a gradual but almost complete reconfiguration in neural activity during a familiar task in a neural circuit involved in planning and representing motor actions. We were able to identify a way to make a relatively stable mapping between neural activation and behaviour despite reconfiguration. The existence of this approximately stable mapping suggests that the observations are not in contradiction with the brain keeping a faithful representation of the world, but they do force us to revise existing theories about how this occurs. One additional outcome of this work is that it offers a potential technological path to building reliable brain-machine interfaces.
We have also found, surprisingly, that for a neural circuit to optimally store a memory, the total amount of systematic change in the synapses that store the memory trace should not exceed the total change due to random biological noise. This is surprising because it tells us that memories last longer when the signals that reinforce them do not completely dominate ongoing, noisy fluctuations that have nothing to do with the memory. This result predicts that continual reconfiguration is inevitable in a biological circuit.
Selected references:
Micou, C, & O'Leary, T (2023). Current Opinion in Neurobiology, Rule ME & O’Leary T* (2022) PNAS, Józsa M, et al (2022) PNAS, Raman DV & O'Leary T (2021). eLife, Rule ME et al (2020) eLife.
