Biomedical subjects

A David Redish

Publications and source records attributed to A David Redish.

2 recordsLinked to original sources

PubMed2003

Hippocampal map realignment and spatial learning.

The spatial selectivity of hippocampal neurons suggests that they contribute to an internal representation of current location. The activity of hippocampal pyramidal cells was recorded while adult (10-13 months old) and aged (24-28 months old) rats performed a task in which two spatial reference frames were put in conflict. Rats attempted to find an unmarked goal whose position was fixed relative to only one of the two reference frames. The ability of a rat's hippocampus to adjust to the conflicting information and use the 'correct' position estimate (hippocampal map 'realignment') was correlated with the rat's ability to find the hidden goal. In addition, aged rats were impaired relative to adult rats in both goal-finding accuracy and map realignment. Thus, changes in the effectiveness with which the hippocampal spatial representation is updated on the basis of external cues may contribute to both within-age-group spatial learning variability and age-related spatial learning deficits.

Adaptation, Physiological↗

PubMed2003

Detecting dynamical changes within a simulated neural ensemble using a measure of representational quality.

Technological advances allowing simultaneous recording of neuronal ensembles have led to many developments in our understanding of how the brain performs neural computations. One key technique for extracting information from neural populations has been population reconstruction. While reconstruction is a powerful tool, it only provides a value and gives no indication of the quality of the representation itself. In this paper, we present a mathematically and statistically justified measure for assessing the quality of a representation in a neuronal ensemble. Using a simulated neural network, we show that this measure can distinguish between system states and identify moments of dynamical change within the system. While the examples used in this paper all derive from a standard network model, the measure itself is very general. It requires only a representational space, measured tuning curves, and neural ensembles.

Animals↗