Measuring and analyzing local field potential (LFP) signals from basolateral amygdala (BLA), hippocampus (HPC) and medial prefrontal cortex (mPFC) may help understand how they communicate with each other during fear memory formation and extinction. In our research, we have formulated a computationally simple and noise immune instantaneous amplitude cross correlation technique which can deduce lead and lag of LFPs generated in BLA, HPC, and mPFC and the directionality of brain signals exchanged between regions. LFP signals are recorded using depth electrodes in the rat brain and cross correlation analysis is applied to theta wave signals after filtering. We found that rats resilient to traumatic conditions (based on post-stress rapid eye movement sleep (REM)) showed a decrease in LFP signal correlation in REM and non-REM (NREM) sleep cycles between BLA-HPC regions after shock training and one day post shock training compared to vulnerable rats that show stress-induced reductions in REM. It is presumed this difference in neural network behavior may be related to REM sleep differences between resilient and vulnerable rats and may provide clues to help understand how traumatic conditions are processed by the brain.
Rechargeable power management system (RPMS) is significantly necessary to monitor the performance of power storage in biomedical devices. Failure of power storage device due to the battery pack, in a medical device, can entail dire consequences such as respiratory devices defibrillators and severe trembling. During charging and discharging the cell might become overstressed or underutilized, of which former may degrade the cell's lifespan. We applied an active balancing technique to distribute charges uniformly into the cell and thus increasing its efficiency and lifespan. In this technique, cells in the stack are monitored at regular intervals for their state of charge (SOC) and the average charge among them is calculated. The cell having the lowest voltage is charged by the series combination of the rest of the cells, by using switching device through isolation transformer which acts as the charge transferring device. In this research, the amount of charge transferred to the low voltage cell was controlled by controlling the frequency of the switching ranging from 100-500kHz. This RPMS includes modules for data acquisition and data logger by which the history of battery pack can be checked for any possible future breakdown and prediction of available run time for the battery pack. RPMS also features cell temperature monitoring to keep it within its safe limits. In this presentation, we will discuss the circuit simulation results using Multisim™ and hardware implementation in progress.
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