Forming independent components via temporal locking of reconstruction architectures: a functional model of the hippocampus

Abstract

The assumption is made that the formulation of relations as independent components (IC) is a main feature of computations accomplished by the brain. Further, it is assumed that memory traces made of non-orthonormal ICs make use of feedback architectures to form internal representations. Feedback then leads to delays, and delays in cortical processing form an obstacle to this relational processing. The problem of delay compensation is formulated as a speed-field tracking task and is solved by a novel control architecture. It is shown that in addition to delay compensation the control architecture can also shape long-term memories to hold independent components if a two-phase operation mode is assumed. Features such as a trisynaptic loop and a recurrent collateral structure at the second stage of that loop emerge in a natural fashion. Based on these properties a functional model of the hippocampal loop is constructed.

Technical integration of hippocampus, basal ganglia and physical models for spatial navigation

“…The DG is thought to perform a principal-components-like dimensionality reduction of input from the EC (Lorincz, 1998 ). Writing w ij for weights on inputs y i , the j th DG unit's neural activation is given by where the sum is taken over all EC inputs.…”

Simulations of Learning, Memory, and Forgetting Processes with Model of CA1 Region of the Hippocampus

“…The contribution to CA1 is observed during activity by using delta rule [42]. Those hypothetical learning rules based on mathematics are given by L' orincz [43]; however in the previous version of this theory there is no precise input to CA1 [44].…”

Design and integration of agent - based partial brain models for robotic systems by means of hierarchical cooperative coevolution

“…Other studies following a dynamical systems modelling approach also exist in the literature [264,194,126,162,283,191]. These efforts usually work at the brain area representation level.…”