Countermanding in rats as a practical model for investigation of adaptive control of behaviour, lifespan changes in behavioural control and neurotransmitter function

The countermanding paradigm was designed to investigate the ability to cancel a prepotent response when a stop signal is presented and allows estimation of the stop signal response time (SSRT), an otherwise unobservable behaviour. Humans exhibit adaptive control of behaviour in the countermanding task, proactively lengthening response time (RT) in expectation of stopping and reactively lengthening RT following stop trials or errors. Human performance changes throughout the lifespan, with longer RT, SSRT and greater emphasis on post-error slowing reported for older compared to younger adults. Inhibition in the task has generally been improved by drugs that increase extracellular norepinephrine. The current thesis examined a novel choice response countermanding task in rats to explore whether rodent countermanding performance is a suitable model for the study of adaptive control of behaviour, lifespan changes in behavioural control and the role of neurotransmitters in these behaviours. Rats reactively adjusted RT in the countermanding task, shortening RT after consecutive correct go trials and lengthening RT following non-cancelled, but not cancelled stop trials, in sessions with a 10 s, but not a 1 s post-error timeout interval. Rats proactively lengthened RT in countermanding task sessions compared to go trial-only sessions. Together, these findings suggest that rats strategically lengthened RT in the countermanding task to improve accuracy and avoid longer, unrewarded timeout intervals. Next, rats exhibited longer RT and relatively conserved post-error slowing, but no significant change in SSRT when tested at 12, compared to 7 months of age, suggesting that rats exhibit changes in countermanding task performance with aging similar to those observed in humans. Finally, acute administration of yohimbine (1.25, 2.5 mg/kg) and d-amphetamine (0.25, 0.5 mg/kg), which putatively increase extracellular norepinephrine and dopamine respectively, resulted in RT shortening, baseline-dependent effects on SSRT, and attenuated adaptive RT adjustments in rats in the case of d-amphetamine. These findings suggest that dopamine and norepinephrine encouraged motivated, reward-seeking behaviour and supported inhibitory control in an inverted-U-like fashion. Taken together, these observations validate the rat countermanding task for further study of the neural correlates and neurotransmitters mediating adaptive control of behaviour and lifespan changes in behavioural control.

A NOVEL MODELING AND STATE OF CHARGE ESTIMATION SCHEME FOR LITHIUM-ION BATTERIES

Lithium Ion (Li-Ion) batteries have got attention in recent decades because of their undisputable advantages over other types of batteries. They are used in so many our devices which we need in our daily life such as cell phones, lap top computers, cameras, and so many electronic devices. They also are being used in smart grids technology, stand-alone wind and solar systems, Hybrid Electric Vehicles (HEV), and Plug in Hybrid Electric Vehicles (PHEV). Despite the rapid increase in the use of Lit-ion batteries, the existence of limited battery models also inadequate and very complex models developed by chemists is the lack of useful models a significant matter. A battery management system (BMS) aims to optimize the use of the battery, making the whole system more reliable, durable and cost effective. Perhaps the most important function of the BMS is to provide an estimate of the State of Charge (SOC). SOC is the ratio of available ampere-hour (Ah) in the battery to the total Ah of a fully charged battery. The Open Circuit Voltage (OCV) of a fully relaxed battery has an approximate one-to-one relationship with the SOC. Therefore, if this voltage is known, the SOC can be found. However, the relaxed OCV can only be measured when the battery is relaxed and the internal battery chemistry has reached equilibrium. This thesis focuses on Li-ion battery cell modelling and SOC estimation. In particular, the thesis, introduces a simple but comprehensive model for the battery and a novel on-line, accurate and fast SOC estimation algorithm for the primary purpose of use in electric and hybrid-electric vehicles, and microgrid systems. The thesis aims to (i) form a baseline characterization for dynamic modeling; (ii) provide a tool for use in state-of-charge estimation. The proposed modelling and SOC estimation schemes are validated through comprehensive simulation and experimental results.