Characteristic Neural Firing Profiles in Different Hippocampal Subfields for Successful and Unsuccessful Memory

Memory is our ability to encode, store, retrain, and subsequently recall information and past experiences. Different areas of the brain are responsible for different aspects of memory, including the hippocampus which enables us to form, organize, and store new memories. Numerous research studies show that the hippocampal subfields are affected by memory related diseases such as Alzheimer’s Disease and Schizophrenia in different ways. Understanding what the different hippocampal subfields do is important for basic science, but also for understanding neurodegenerative disorders which are associated with structural and functional abnormalities of hippocampal neurons. In order to examine the effects of memory success and failure of the firing patterns of the hippocampal neurons in the different subfields, I used a unique dataset, published by Faraut et al (2018), of a large sample of intracranial neural spiking data from humans.) and ran a hierarchical clustering algorithm on the neural firing patterns. Results suggest that the neurons in the different hippocampus subfields (CA1, CA2, CA3, and DG) have certain firing profiles which as a result causes them to group together according to these specific subfields. These firing patters were different in some degree depending on weather on successful and unsuccessful memory – and thus suggest each subfield processes memories in a different way.Undergraduat

Attractor Modulation and Proliferation in 1+∞\infty Dimensional Neural Networks

We extend a recently introduced class of exactly solvable models for recurrent neural networks with competition between 1D nearest neighbour and infinite range information processing. We increase the potential for further frustration and competition in these models, as well as their biological relevance, by adding next-nearest neighbour couplings, and we allow for modulation of the attractors so that we can interpolate continuously between situations with different numbers of stored patterns. Our models are solved by combining mean field and random field techniques. They exhibit increasingly complex phase diagrams with novel phases, separated by multiple first- and second order transitions (dynamical and thermodynamic ones), and, upon modulating the attractor strengths, non-trivial scenarios of phase diagram deformation. Our predictions are in excellent agreement with numerical simulations.Comment: 16 pages, 15 postscript figures, Late

An associative network with spatially organized connectivity

We investigate the properties of an autoassociative network of threshold-linear units whose synaptic connectivity is spatially structured and asymmetric. Since the methods of equilibrium statistical mechanics cannot be applied to such a network due to the lack of a Hamiltonian, we approach the problem through a signal-to-noise analysis, that we adapt to spatially organized networks. The conditions are analyzed for the appearance of stable, spatially non-uniform profiles of activity with large overlaps with one of the stored patterns. It is also shown, with simulations and analytic results, that the storage capacity does not decrease much when the connectivity of the network becomes short range. In addition, the method used here enables us to calculate exactly the storage capacity of a randomly connected network with arbitrary degree of dilution.Comment: 27 pages, 6 figures; Accepted for publication in JSTA