  This paper presents a massively parallel and scalable neuromorphic cortex
simulator designed for simulating large and structurally connected spiking
neural networks, such as complex models of various areas of the cortex. The
main novelty of this work is the abstraction of a neuromorphic architecture
into clusters represented by minicolumns and hypercolumns, analogously to the
fundamental structural units observed in neurobiology. Without this approach,
simulating large-scale fully connected networks needs prohibitively large
memory to store look-up tables for point-to-point connections. Instead, we use
a novel architecture, based on the structural connectivity in the neocortex,
such that all the required parameters and connections can be stored in on-chip
memory. The cortex simulator can be easily reconfigured for simulating
different neural networks without any change in hardware structure by
programming the memory. A hierarchical communication scheme allows one neuron
to have a fan-out of up to 200k neurons. As a proof-of-concept, an
implementation on one Altera Stratix V FPGA was able to simulate 20 million to
2.6 billion leaky-integrate-and-fire (LIF) neurons in real time. We verified
the system by emulating a simplified auditory cortex (with 100 million
neurons). This cortex simulator achieved a low power dissipation of 1.62 {\mu}W
per neuron. With the advent of commercially available FPGA boards, our system
offers an accessible and scalable tool for the design, real-time simulation,
and analysis of large-scale spiking neural networks.
