The Structure of Hippocampal Activity During REM Sleep

The hippocampus is a brain structure critical for the formation of long-term episodic memories. The current predominant theory is that memories are gradually established across neocortical networks under the influence of hippocampal activity. This process of memory consolidation is conjectured to occur during sleep, which is characterized by two different modes of activation: slow-wave sleep (SWS) and rapid eye movement (REM) sleep. The functional roles of these two different sleep states remain unknown. Paradoxically, REM sleep exhibits the main features of awake activity, and is the stage of sleep when most dreams occur. Despite decades of study, the organization and function of REM sleep activity remains poorly understood. The goal of this thesis is to achieve a deeper quantitative understanding of the patterns of firing in area CA1 of the hippocampus during REM sleep using chronic multi-tetrode recordings from freely behaving and naturally sleeping rats. Our analysis shows that CA1 neurons significantly elevate their firing rate for periods that are short in relation to the duration of the REM sleep episode. Furthermore, for the majority of neurons, there is exactly one such burst per REM episode. This leads to lower overall firing rates and sparser population activity in CA1 compared to SWS. The time of onset of these bursts defines a natural order of firing across the population of recorded neurons within each REM episode. We demonstrate that this order does not repeat across REM episodes. Our results suggest that CA1 neurons are activated in random sequences across REM episodes, resulting in sparse patterns with only a small fraction of neurons active at any given time

DataJoint: managing big scientific data using MATLAB or Python

The rise of big data in modern research poses serious challenges for data management: Large and intricate datasets from diverse instrumentation must be precisely aligned, annotated, and processed in a variety of ways to extract new insights. While high levels of data integrity are expected, research teams have diverse backgrounds, are geographically dispersed, and rarely possess a primary interest in data science. Here we describe DataJoint, an open-source toolbox designed for manipulating and processing scientific data under the relational data model. Designed for scientists who need a flexible and expressive database language with few basic concepts and operations, DataJoint facilitates multi-user access, efficient queries, and distributed computing. With implementations in both MATLAB and Python, DataJoint is not limited to particular file formats, acquisition systems, or data modalities and can be quickly adapted to new experimental designs. DataJoint and related resources are available at http://datajoint.github.com

DataJoint: managing big scientific data using MATLAB or Python

The rise of big data in modern research poses serious challenges for data management: Large and intricate datasets from diverse instrumentation must be precisely aligned, annotated, and processed in a variety of ways to extract new insights. While high levels of data integrity are expected, research teams have diverse backgrounds, are geographically dispersed, and rarely possess a primary interest in data science. Here we describe DataJoint, an open-source toolbox designed for manipulating and processing scientific data under the relational data model. Designed for scientists who need a flexible and expressive database language with few basic concepts and operations, DataJoint facilitates multi-user access, efficient queries, and distributed computing. With implementations in both MATLAB and Python, DataJoint is not limited to particular file formats, acquisition systems, or data modalities and can be quickly adapted to new experimental designs. DataJoint and related resources are available at http://datajoint.github.com

Release 2.7.6

<p>DataJoint release 2.7.6</p> <ul> <li>bug fixes</li> <li>enabled recursive populates</li> <li>added the pairing operator dj.GeneralRelvar/pair</li> <li>added dj.GeneralRelvar/show to show the header information</li> <li>changed the table definition syntax to make attribute comments optional</li> </ul