Neuromorphic hardware platforms implement biological neurons and synapses to execute spiking neural networks (SNNs) in an energy-efficient manner. We present SpiNeMap, a design methodology to map SNNs to crossbar-based neuromorphic hardware, minimizing spike latency and energy consumption. SpiNeMap operates in two steps: SpiNeCluster and SpiNePlacer. SpiNeCluster is a heuristic-based clustering technique to partition SNNs into clusters of synapses, where intracluster local synapses are mapped within crossbars of the hardware and inter-cluster global synapses are mapped to the shared interconnect. SpiNeCluster minimizes the number of spikes on global synapses, which reduces spike congestion on the shared interconnect, improving application performance. SpiNePlacer then finds the best placement of local and global synapses on the hardware using a meta-heuristic-based approach to minimize energy consumption and spike latency. We evaluate SpiNeMap using synthetic and realistic SNNs on the DynapSE neuromorphic hardware. We show that SpiNeMap reduces average energy consumption by 45% and average spike latency by 21%, compared to state-of-the-art techniques.

Neuromodulatory systems, including the noradrenergic, serotonergic, dopaminergic, and cholinergic systems, track environmental signals, such as risks, rewards, novelty, effort, and social cooperation. These systems provide a foundation for cognitive function in higher organisms; attention, emotion, goal-directed behavior, and decision-making derive from the interaction between the neuromodulatory systems and brain areas, such as the amygdala, frontal cortex, hippocampus, and sensory cortices. Given their strong influence on behavior and cognition, these systems also play a key role in disease states and are the primary target of many current treatment strategies. The fact that these systems interact with each other either directly or indirectly, however, makes it difficult to understand how a failure in one or more systems can lead to a particular symptom or pathology. In this review, we explore experimental evidence, as well as focus on computational and theoretical models of neuromodulation. Better understanding of neuromodulatory systems may lead to the development of novel treatment strategies for a number of brain disorders.

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