Abstract
Computational models whose organization is inspired by the
cortex are increasing in both number and popularity. Current
instances of such models include convolutional networks,
HMAX, Hierarchical Temporal Memory, and deep belief
networks. These models present two practical
challenges. First, they are computationally intensive. Second,
while the operations performed by individual cells, or units,
are typically simple, the code needed to keep track of network
connectivity can quickly become complicated, leading to
programs that are difficult to write and to modify. Massively
parallel commodity computing hardware has recently become
available in the form of general-purpose GPUs. This helps
address the first problem but exacerbates the second. GPU
programming adds an extra layer of difficulty, further
discouraging exploration. To address these concerns, we have
created a programming framework called CNS
(’Cortical Network
Simulator’). CNS models are automatically
compiled and run on a GPU, typically 80-100x faster than on
a single CPU, without the user having to learn any GPU
programming. A novel scheme for the parametric specification
of network connectivity allows the user to focus on writing
just the code executed by a single cell. We hope that the
ability to rapidly define and run cortically-inspired models
will facilitate research in the cortical modeling
community. CNS is available under the GNU General Public
License.
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