NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.

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An FPGA-based systolic architecture for the high speed simulation of spiking neural networks is presented. The design is an implementation of Izhikevich's neuron model and employs optimizations for the typical case where neuron activity is low. Since execution time required is related to the activity level, performance of the design can be improved by an order of magnitude.

In Chinese orthography, a dominant structure exists in which a semantic radical appears on the left and a phonetic radical on the right (SP characters); the minority, opposite arrangement also exists (PS characters). Recent studies showed that SP character processing is more left hemisphere (LH) lateralized than PS character processing; nevertheless, it remains unclear whether this is due to phonetic radical position or character type frequency. Through computational modeling with artificial lexicons, in which we implement a theory of hemispheric asymmetry in perception that posits differential frequency bias in the two hemispheres (i.e., the DFF theory; Ivry & Robertson, 1998), but do not assume phonological processing being LH lateralized, we show that although phonetic radical position, visual complexity of the radicals, and character information structure may all modulate lateralization effects, the difference in character type frequency alone is sufficient to exhibit the effect that the dominant type has a stronger LH lateralization than the minority type. Further analysis suggests that this effect is due to higher visual similarity among characters in the dominant type as compared with those in the minority type. This result demonstrates that word type frequency alone can modulate hemispheric lateralization effects in visual word recognition.