Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware.
SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 × 104 neurons and 5.1 × 107 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45× more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models.
Advanced Processor Technologies Group, School of Computer Science, University of Manchester Manchester, UK.
James C
JC
Knight
Department of Computational Biology, Royal Institute of TechnologyStockholm, Sweden; Stockholm Brain Institute, Karolinska InstituteStockholm, Sweden; Institute for Adaptive and Neural Computation, School of Informatics, University of EdinburghEdinburgh, UK.
Philip J
PJ
Tully
Department of Visualization and Data Analysis, Zuse Institute Berlin Berlin, Germany.
Bernhard A
BA
Kaplan
Department of Computational Biology, Royal Institute of TechnologyStockholm, Sweden; Stockholm Brain Institute, Karolinska InstituteStockholm, Sweden; Department of Numerical analysis and Computer Science, Stockholm UniversityStockholm, Sweden.
Anders
A
Lansner
Advanced Processor Technologies Group, School of Computer Science, University of Manchester Manchester, UK.
Steve B
SB
Furber
Bayesian confidence propagation neural network (BCPNN); SpiNNaker; digital neuromorphic hardware; event-driven simulation; fixed-point accuracy; learning; plasticity