Topological Schemas of Memory Spaces

Hippocampal cognitive map---a neuronal representation of the spatial environment---is broadly discussed in the computational neuroscience literature for decades. More recent studies point out that hippocampus plays a major role in producing yet another cognitive framework that incorporates not only spatial, but also nonspatial memories---the memory space. However, unlike cognitive maps, memory spaces have been barely studied from a theoretical perspective. Here we propose an approach for modeling hippocampal memory spaces as an epiphenomenon of neuronal spiking activity. First, we suggest that the memory space may be viewed as a finite topological space---a hypothesis that allows treating both spatial and nonspatial aspects of hippocampal function on equal footing. We then model the topological properties of the memory space to demonstrate that this concept naturally incorporates the notion of a cognitive map. Lastly, we suggest a formal description of the memory consolidation process and point out a connection between the proposed model of the memory spaces to the so-called Morris' schemas, which emerge as the most compact representation of the memory structure.Comment: 24 pages, 8 Figures, 1 Suppl. Figur

3D Gravity and Gauge Theories

I argue that the complete partition function of 3D quantum gravity is given by a path integral over gauge-inequivalent manifolds times the Chern-Simons partition function. In a discrete version, it gives a sum over simplicial complexes weighted with the Turaev-Viro invariant. Then, I discuss how this invariant can be included in the general framework of lattice gauge theory (qQCD$_3$). To make sense of it, one needs a quantum analog of the Peter-Weyl theorem and an invariant measure, which are introduced explicitly. The consideration here is limited to the simplest and most interesting case of $SL_q(2)$, $q=e^{i\frac{2\pi}{k+2}}$. At the end, I dwell on 3D generalizations of matrix models.Comment: 20 pp., NBI-HE-93-67 (Contribution to Proceedings of 1993 Cargese workshop

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

Statistical geometry of random weave states

I describe the first steps in the construction of semiclassical states for non-perturbative canonical quantum gravity using ideas from classical, Riemannian statistical geometry and results from quantum geometry of spin network states. In particular, I concentrate on how those techniques are applied to the construction of random spin networks, and the calculation of their contribution to areas and volumes.Comment: 10 pages, LaTeX, submitted to the Proceedings of the IX Marcel Grossmann Meeting, Rome, July 2-8, 200

Persistence Bag-of-Words for Topological Data Analysis

Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs). PDs exhibit, however, complex structure and are difficult to integrate in today's machine learning workflows. This paper introduces persistence bag-of-words: a novel and stable vectorized representation of PDs that enables the seamless integration with machine learning. Comprehensive experiments show that the new representation achieves state-of-the-art performance and beyond in much less time than alternative approaches.Comment: Accepted for the Twenty-Eight International Joint Conference on Artificial Intelligence (IJCAI-19). arXiv admin note: substantial text overlap with arXiv:1802.0485