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An Exploration of the Role of Cellular Neuroplasticity in Large Scale Models of Biological Neural Networks


Cellular level learning is vital to almost all brain function, and extensive homeostatic plasticity is required to maintain brain functionality. While much has been learned about cellular level plasticity in vivo, how these mechanisms affect higher level functionality is not readily apparent. The cellular level circuitry of most networks that process information is unknown. A variety of models were developed to better understand plasticity in both learning and homeostasis.

Spike time dependent plasticity (STDP) and reward-modulated plasticity may be the primary methods through which neurons record information. We implemented rewarded STDP to model foraging behavior in a virtual environment. When appropriate homeostatic mechanisms were in place, the network of spiking neurons developed the capability of producing highly successful decision-making.

The networks used in the foraging model used a very simple initial configuration to avoid assumptions about network organization. More realistic network configurations can help to show how plasticity interacts with genetically determined network. We developed three network models of synaptic mechanisms of FM sweep processing based on published experimental data. One of these, the `inhibitory sideband' model, used frequency selective inputs to a network of excitatory and inhibitory cells. The strength and asymmetry of these connections resulted in neurons responsive to sweeps in a single direction and of sufficient rate. STDP was shown to be capable of causing to become selective for sweeps in the same direction as a repeatedly presented training sweep.

The experience dependent plasticity, occurs primarily during the waking state, however, sleep is essential for learning. Slow wave sleep activity may be essential for memory consolidation and homeostasis. We developed a model of slow wave sleep that included methods to calculate the electrical field in the space around the network. We show here that a network model of up and down states displays this CSD profile only if a frequency-filtering extracellular medium is assumed. While initiation of the active cortical states during sleep slow oscillation has been intensively studied, the it's termination remains poorly understood. We explored the impact of intrinsic and synaptic inhibition on the state transition. We found that synaptic inhibition controls the duration and the synchrony of active state termination.

Together these models set the stage for a model network that can learn through input driven processes in a waking state then explore the consolidation of memory in a sleeping state. This will allow us to explore in greater detail how plasticity on the level of a single cell contributes to learning and stability on the level of the whole brain.

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