Theta mediated dynamics of human hippocampal-neocortical learning systems in memory formation and retrieval.

Episodic memory arises as a function of dynamic interactions between the hippocampus and the neocortex, yet the mechanisms have remained elusive. Here, using human intracranial recordings during a mnemonic discrimination task, we report that 4-5 Hz (theta) power is differentially recruited during discrimination vs. overgeneralization, and its phase supports hippocampal-neocortical when memories are being formed and correctly retrieved.

A Scalable Open-Set ECG Identification System Based on Compressed CNNs.

Deep learning (DL) is known for its excellence in feature learning and its ability to deliver high-accuracy results. Its application to ECG biometric recognition has received increasing interest but is also accompanied by several deficiencies. In this study, we focus on applying DL, especially convolutional neural networks (CNNs), to ECG biometric identification to address these deficiencies.

Drosophila Brain Functional Data Analysis: A Unified Framework.

A unified framework for the analysis of fluorescence data taken by a two-photon imaging system is presented. As in the processing of blood-oxygen-level-dependent signals of functional magnetic resonance imaging, the acquired functional images have to be co-registered with a structural brain atlas before delineating the regions activated by a given stimulus. The voxels whose calcium traces are highly correlated with the predicted responses are demarcated without the need for subjective reasoning.

A novel framework for feature extraction in multi-sensor action potential sorting.

Background: Extracellular recordings of multi-unit neural activity have become indispensable in neuroscience research. The analysis of the recordings begins with the detection of the action potentials (APs), followed by a classification step where each AP is associated with a given neural source. A feature extraction step is required prior to classification in order to reduce the dimensionality of the data and the impact of noise, allowing source clustering algorithms to work more efficiently.

Matching pursuit and source deflation for sparse EEG/MEG dipole moment estimation.

In this paper, we propose novel matching pursuit (MP)-based algorithms for EEG/MEG dipole source localization and parameter estimation for multiple measurement vectors with constant sparsity. The algorithms combine the ideas of MP for sparse signal recovery and source deflation, as employed in estimation via alternating projections. The source-deflated matching pursuit (SDMP) approach mitigates the problem of residual interference inherent in sequential MP-based methods or recursively applied (RAP)-MUSIC.