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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Amirpasha Zamani mozafarabadi
Robustness of Persistent Firing in a Minimal Recurrent Network of Working Memory
9
2017
This thesis which is a theoretical work in the field of computational neuroscience is an attempt to simulate the underlying mechanisms of working memory in cellular level. It is shown that bistability is the main required dynamical feature for obtaining a successful model of mnemonic activity comparable with the so-called ‘persistent activity’ observed in delay selective cortical cells. The proposed model is composed of two Quadratic Integrate and Fire (QIF) neurons that are coupled to form a neural oscillator. The oscillatory activity of the system corresponds to the formation of a limit cycle in the phase space and is responsible for information storage. Under a particular range of parameters, another quiescent state coexists with the oscillatory state which makes it possible to switch between two different modes.

The transition from activity to silent mode is studied under different conditions. Firstly, effects of transient excitatory and inhibitory pulses are discussed as a possible mechanism for clearing the memory. Then the influence of random perturbations is investigated to find out how different levels of noise may terminate the persistent firing. It is suggested that biological neural circuits may have taken advantage of the ubiquitous randomness in the brain as a mechanism for clearing the irrelevant information from working memory.

In particular, the Inverse Stochastic Resonance (ISR) phenomenon is studied in our recurrent bi-stable model. Optima noise values to erase the memory, probability of erasure with noise, the patterns of firing activity in presence of different noise levels, and the concavity width of ISR curves are investigated in detail to gain the best parameters for an optimal model of working memory.

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