A framework will be developed to help scientists and engineers create brain-inspired, brain-sized networks that can carry out practical applications. Large-scale spiking neural networks, which follow the brain's architecture and activity, have been used to successfully model phenomena such as learning and memory, vision, auditory processing, neural oscillations, and many other important aspects of neural function. Additionally, spiking neural networks are particularly well suited to run on neuromorphic hardware, state of the art computers that emulate the brain?s structure and dynamics. These neuromorphic systems depend on the binary nature of spikes to lower communication bandwidth and energy consumption. Although significant progress has been made towards the specification and simulation of large-scale spiking neural networks on a variety of hardware platforms, many challenges remain before these neurobiologically inspired algorithms can be used in practical applications. While biology does provide increasingly abundant empirical data that can constrain these systems, many parameter values must be chosen manually by the designer to achieve appropriate neuronal dynamics, a task that is extremely tedious and often error-prone. To meet this challenge, an automated parametertuning framework will be developed that is capable of quickly and efficiently tuning large-scale spiking neural networks. The framework will leverage recent progress in evolutionary algorithms and optimization techniques for off-the-shelf graphics processing units (GPUs). The parameter search will be guided by the idea in neuroscience that biological networks adapt their responses to increase the amount of transmitted information, reduce redundancies, and span the stimulus space. This notion of efficient coding will guide the tuning process of the artificial spiking neural networks. Computer scientists and engineers will be able to use the resulting automated parameter-tuning framework to create brain inspired applications, such as vision and memory systems, on neuromorphic hardware. Moreover, the resulting framework will allow neuroscientists to more readily create models that better describe their empirical data and generate new quantitative hypotheses that can be tested in the laboratory.
