Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps

Cognitive maps are mental representations of spatial and conceptual relationships in an environment, and are critical for flexible behavior. To form these abstract maps, the hippocampus has to learn to separate or merge aliased observations appropriately in different contexts in a manner that enables generalization and efficient planning. Here we propose a specific higher-order graph structure, clone-structured cognitive graph (CSCG), which forms clones of an observation for different contexts as a representation that addresses these problems. CSCGs can be learned efficiently using a probabilistic sequence model that is inherently robust to uncertainty. We show that CSCGs can explain a variety of cognitive map phenomena such as discovering spatial relations from aliased sensations, transitive inference between disjoint episodes, and formation of transferable schemas. Learning different clones for different contexts explains the emergence of splitter cells observed in maze navigation and event-specific responses in lap-running experiments. Moreover, learning and inference dynamics of CSCGs offer a coherent explanation for disparate place cell remapping phenomena. By lifting aliased observations into a hidden space, CSCGs reveal latent modularity useful for hierarchical abstraction and planning. Altogether, CSCG provides a simple unifying framework for understanding hippocampal function, and could be a pathway for forming relational abstractions in artificial intelligence.

CONTROL OF LOAD OF THE COMMUNICATION NETWORK WITH VIRTUAL FRAGMENTATION AND TAKING INTO ACCOUNT THE CURRENT «COMPOSITION» OF OVERLOADED FRAGMENTS

The problems of network load management for a generalized version of a large packet switching network are investigated. The network is divided into elementary fragments according to the selected rule. Data routing is "flat" (not hierarchical). Abstraction is used - a set of network fragments can be represented as a set of networked elements. For each fragment, a significant indicator of its load (for example, the average load of its nodes or another) is defined. The limit of this indicator is set, which provides the definition of an elementary fragment as a fragment with an increased load. In the entered imaginary "network of fragments" there are connected groups of fragments with the increased loading. For groups of elementary fragments with high load, modifications of the load control method are used due to the choice of the lowest cost paths, in which the routing takes into account the node's belonging to the fragments with high load.

Learning cognitive maps as structured graphs for vicarious evaluation

Cognitive maps are mental representations of spatial and conceptual relationships in an environment. These maps are critical for flexible behavior as they permit us to navigate vicariously, but their underlying representation learning mechanisms are still unknown. To form these abstract maps, hippocampus has to learn to separate or merge aliased observations appropriately in different contexts in a manner that enables generalization, efficient planning, and handling of uncertainty. Here we introduce a specific higher-order graph structure – clone-structured cognitive graph (CSCG) – which forms different clones of an observation for different contexts as a representation that addresses these problems. CSCGs can be learned efficiently using a novel probabilistic sequence model that is inherently robust to uncertainty. We show that CSCGs can explain a variety cognitive map phenomena such as discovering spatial relations from an aliased sensory stream, transitive inference between disjoint episodes of experiences, formation of transferable structural knowledge, and shortcut-finding in novel environments. By learning different clones for different contexts, CSCGs explain the emergence of splitter cells and route-specific encoding of place cells observed in maze navigation, and event-specific graded representations observed in lap-running experiments. Moreover, learning and inference dynamics of CSCGs offer a coherent explanation for a variety of place cell remapping phenomena. By lifting the aliased observations into a hidden space, CSCGs reveal latent modularity that is then used for hierarchical abstraction and planning. Altogether, learning and inference using a CSCG provides a simple unifying framework for understanding hippocampal function, and could be a pathway for forming relational abstractions in artificial intelligence.

A Review of ‘Brain Computation as Hierarchical Abstraction’

This article is a review of the book ‘Brain Computation As Hierarchical Abstraction’ by Dana H. Ballard published by MIT press in 2015. The book series computational neuroscience familiarizes the reader with the computational aspects of brain functions based on neuroscientific evidence. It provides an excellent introduction of the functioning, i.e. the structure, the network and the routines of the brain in our daily life. The final chapters even discuss behavioral elements such as decision-making, emotions and consciousness. These topics are of high relevance in other sciences such as economics and philosophy. Overall, Ballard’s book stimulates a scientifically well-founded debate and, more importantly, reveals the need of an interdisciplinary dialogue towards social sciences.