Topographic maps are a brain structure connecting pre-synpatic and post-synaptic brain regions. Topographic development is dependent on Hebbian-based plasticity mechanisms working in conjunction with spontaneous patterns of neural activity generated in the pre-synaptic regions. Studies performed in mouse have shown that these spontaneous patterns can exhibit complex spatial-temporal structures which existing models cannot incorporate. Neural field theories are appropriate modelling paradigms for topographic systems due to the dense nature of the connections between regions and can be augmented with a plasticity rule general enough to capture complex time-varying structures. We propose a theoretical framework for studying the development of topography in the context of complex spatial-temporal activity fed-forward from the pre-synaptic to post-synaptic regions. Analysis of the model leads to an analytic solution corroborating the conclusion that activity can drive the refinement of topographic projections. The analysis also suggests that biological noise is used in the development of topography to stabilise the dynamics. MCMC simulations are used to analyse and understand the differences in topographic refinement between wild-type and the $\beta2$ knock-out mutant in mice. The time scale of the synaptic plasticity window is estimated as $0.56$ seconds in this context with a model fit of $R^2 = 0.81$.
Mathematical modelling of the macroscopic electrical activity of the brain is highly non-trivial and requires a detailed understanding of not only the associated mathematical techniques, but also the underlying physiology and anatomy. Neural field theory is a population-level approach to modelling the non-linear dynamics of large populations of neurons, while maintaining a degree of mathematical tractability. This class of models provides a solid theoretical perspective on fundamental processes of neural tissue such as state transitions between different brain activities as observed during epilepsy or sleep. Various anatomical, physiological, and mathematical assumptions are essential for deriving a minimal set of equations that strike a balance between biophysical realism and mathematical tractability. However, these assumptions are not always made explicit throughout the literature. Even though neural field models (NFMs) first appeared in the literature in the early 1970's, the relationships between them have not been systematically addressed. This may partially be explained by the fact that the inter-dependencies between these models are often implicit and non-trivial. Herein we provide a review of key stages of the history and development of neural field theory and contemporary uses of this branch of mathematical neuroscience. First, the principles of the theory are summarised throughout a discussion of the pioneering models by Wilson and Cowan, Amari and Nunez. Upon […]
We study a model of interacting neurons. The structure of this neural system is composed of two layers of neurons such that the neurons of the first layer send their spikes to the neurons of the second one: if $N$ is the number of neurons of the first layer, at each spiking time of the first layer, every neuron of both layers receives an amount of potential of the form $U/\sqrt{N},$ where $U$ is a centered random variable. This kind of structure of neurons can model a part of the structure of the visual cortex: the first layer represents the primary visual cortex V1 and the second one the visual area V2. The model consists of two stochastic processes, one modelling the membrane potential of the neurons of the first layer, and the other the membrane potential of the neurons of the second one. We prove the convergence of these processes as the number of neurons~$N$ goes to infinity and obtain a convergence speed. The proofs rely on similar arguments as those used in [Erny, Löcherbach, Loukianova (2022)]: the convergence speed of the semigroups of the processes is obtained from the convergence speed of their infinitesimal generators using a Trotter-Kato formula, and from the regularity of the limit semigroup.
Among other models aimed at understanding self-sustained oscillations in neural networks, the NNLIF model with synaptic delay was developed almost twenty years ago to model fast global oscillations in networks of weakly firing inhibitory neurons. Periodic solutions were numerically observed in this model, but despite its intensive study by researchers in PDEs and probability, there is up-to-date no analytical result on this topic. In this article, we propose to approximate formally these solutions by a Gaussian wave whose periodic movement is described by an associate delay differential equation (DDE). We prove the existence of a periodic solution for this DDE and we give a rigorous asymptotic result on these solutions when the connectivity parameter $b$ goes to $-\infty$. Lastly, we provide heuristic and numerical evidence of the validity of our approximation.