Robust spatial memory maps encoded in networks with transient connections

The spiking activity of principal cells in mammalian hippocampus encodes an internalized neuronal representation of the ambient space---a cognitive map. Once learned, such a map enables the animal to navigate a given environment for a long period. However, the neuronal substrate that produces this map remains transient: the synaptic connections in the hippocampus and in the downstream neuronal networks never cease to form and to deteriorate at a rapid rate. How can the brain maintain a robust, reliable representation of space using a network that constantly changes its architecture? Here, we demonstrate, using novel Algebraic Topology techniques, that cognitive map's stability is a generic, emergent phenomenon. The model allows evaluating the effect produced by specific physiological parameters, e.g., the distribution of connections' decay times, on the properties of the cognitive map as a whole. It also points out that spatial memory deterioration caused by weakening or excessive loss of the synaptic connections may be compensated by simulating the neuronal activity. Lastly, the model explicates functional importance of the complementary learning systems for processing spatial information at different levels of spatiotemporal granularity, by establishing three complementary timescales at which spatial information unfolds. Thus, the model provides a principal insight into how can the brain develop a reliable representation of the world, learn and retain memories despite complex plasticity of the underlying networks and allows studying how instabilities and memory deterioration mechanisms may affect learning process.Comment: 24 pages, 10 figures, 4 supplementary figure

The Topology of Probability Distributions on Manifolds

Let $P$ be a set of $n$ random points in $R^d$, generated from a probability measure on a $m$-dimensional manifold $M \subset R^d$. In this paper we study the homology of $U(P,r)$ -- the union of $d$-dimensional balls of radius $r$ around $P$, as $n \to \infty$, and $r \to 0$. In addition we study the critical points of $d_P$ -- the distance function from the set $P$. These two objects are known to be related via Morse theory. We present limit theorems for the Betti numbers of $U(P,r)$, as well as for number of critical points of index $k$ for $d_P$. Depending on how fast $r$ decays to zero as $n$ grows, these two objects exhibit different types of limiting behavior. In one particular case ($n r^m > C \log n$), we show that the Betti numbers of $U(P,r)$ perfectly recover the Betti numbers of the original manifold $M$, a result which is of significant interest in topological manifold learning