Distributed Temporal Coding of Visual Memory Categories in Human Hippocampal Neurons.

The hippocampus is crucial for forming new episodic memories. While the encoding of spatial and temporal information (where and when) in the hippocampus is well understood, the encoding of objects (what) remains less clear due to the high dimensions of object space. Rather than encoding each individual object separately, the hippocampus may instead encode categories of objects to reduce this dimensionality.

Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall of stimulus features and categories.

Objective: Here, we demonstrate the first successful use of static neural stimulation patterns for specific information content. These static patterns were derived by a model that was applied to a subject's own hippocampal spatiotemporal neural codes for memory.

Approach: We constructed a new model of processes by which the hippocampus encodes specific memory items via spatiotemporal firing of neural ensembles that underlie the successful encoding of targeted content into short-term memory.

Analysis of neural networks for routine classification of sixteen ultrasound upper abdominal cross sections.

Purpose: Abdominal ultrasound screening requires the capture of multiple standardized plane views as per clinical guidelines. Currently, the extent of adherence to such guidelines is dependent entirely on the skills of the sonographer. The use of neural network classification has the potential to better standardize captured plane views and streamline plane capture reducing the time burden on operators by combatting operator variability.