Encoding (memory)

About: Encoding (memory) is a research topic. Over the lifetime, 7547 publications have been published within this topic receiving 120214 citations. The topic is also known as: memory encoding & encoding of memories.

Behavioral functions of the CA3 subregion of the hippocampus

Abstract: From a behavioral perspective, the CA3a,b subregion of the hippocampus plays an important role in the encoding of new spatial information within short-term memory with a duration of seconds and minutes. This can easily be observed in tasks that require rapid encoding, novelty detection, one-trial short-term or working memory, and one-trial cued recall primarily for spatial information. These are tasks that have been assumed to reflect the operations of episodic memory and require interactions between CA3a,b and the dentate gyrus via mossy fiber inputs into the CA3a,b. The CA3a,b is also important for encoding of spatial information requiring multiple trials including the acquisition of arbitrary and relational associations. These tasks tend to be non-episodic and can be mediated by arbitrary and conjunctive operations. All these tasks are assumed to operate within an autoassociative network function of the CA3 region. The output from CA3a,b via the fimbria and the medial and lateral perforant path inputs play a supporting role in the neural circuit that supports the operation of these tasks. The CA3a,b also plays a role in sequential processing of information in cooperation with CA1 based on the Schaffer collateral output from CA3a,b to CA1. The CA3a,b also supports retrieval of short-term memory information based on a spatial pattern completion process. Finally, CA3c may, in cooperation with the dentate gyrus, serve an important role in processing the geometry of the environment.

Early Tagging of Cortical Networks Is Required for the Formation of Enduring Associative Memory

Abstract: Although formation and stabilization of long-lasting associative memories are thought to require time-dependent coordinated hippocampal-cortical interactions, the underlying mechanisms remain unclear Here, we present evidence that neurons in the rat cortex must undergo a "tagging process" upon encoding to ensure the progressive hippocampal-driven rewiring of cortical networks that support remote memory storage This process was AMPA- and N-methyl-D-aspartate receptor-dependent, information-specific, and capable of modulating remote memory persistence by affecting the temporal dynamics of hippocampal-cortical interactions Post-learning reinforcement of the tagging process via time-limited epigenetic modifications resulted in improved remote memory retrieval Thus, early tagging of cortical networks is a crucial neurobiological process for remote memory formation whose functional properties fit the requirements imposed by the extended time scale of systems-level memory consolidation

A comparison between cellular encoding and direct encoding for genetic neural networks

Abstract: This paper compares the efficiency of two encoding schemes for Artificial Neural Networks optimized by evolutionary algorithms. Direct Encoding encodes the weights for an a priori fixed neural network architecture. Cellular Encoding encodes both weights and the architecture of the neural network. In previous studies, Direct Encoding and Cellular Encoding have been used to create neural networks for balancing 1 and 2 poles attached to a cart on a fixed track. The poles are balanced by a controller that pushes the cart to the left or the right. In some cases velocity information about the pole and cart is provided as an input; in other cases the network must learn to balance a single pole without velocity information. A careful study of the behavior of these systems suggests that it is possible to balance a single pole with velocity information as an input and without learning to compute the velocity. A new fitness function is introduced that forces the neural network to compute the velocity. By using this new fitness function and tuning the syntactic constraints used with cellular encoding, we achieve a tenfold speedup over our previous study and solve a more difficult problem: balancing two poles when no information about the velocity is provided as input.