Theoretical and Mathematical Work: Neuroplasticity
Neuroplasticity and the related issue of synaptic plasticity are among the most important problems in theoretical neuroscience today. Even though the brain consists of other types of cells, notably microglia, astrocytes or oligodendrocytes, and its vasculature, its glymphatic system, and the brain-blood barrier are of critical importance, it is a reasonable assumption that models of neuroplasticity will provide critical insight into memory and learning. We have made critical progress in the areas of neuromodulation (1,2), and in cell-internal signaling (3). We find that models of synaptic plasticity – which focus on the variability in strength of the connective links between neurons – are insufficient on empirical as well as theoretical grounds. First, it has been established that pre-synapse and post-synapse are both controlled by their respective cell bodies. Synaptic plasticity becomes an immensely complicated task involving many pathways in two cells, which must exclusively act to adjust the strength of the connection, and have no other adaptive or modulating role. Empirical evidence shows that this is not the case (4,5). Secondly, synaptic models are also insufficient on theoretical grounds because they assume associative learning as the basis of all adaptive plasticity (6). Again, this has been shown not to be a reasonable assumption (7,8,9).
The research area of neuroplasticity has seen the most support at the Carl Correns Foundation so far (Michael, Sergey and Florian, also Fred). In our attempt to develop a comprehensive theory (10), we are focusing on neuron models. We developed a significant collection of models and algorithms (11, 12 ) To continue with this work, we require more support for interns and scholars. We plan to spin off a start-up on neuro AI (interview) in the near future. Stay tuned!
