Anatomy of a cortical simulator

Insights into brain’s high-level computational principles will lead to novel cognitive systems, computing architectures, programming paradigms, and numerous practical applications. An important step towards this end is the study of large networks of cortical spiking neurons. We have built a cortical simulator, C2, incorporating several algorithmic enhancements to optimize the simulation scale and time, through: computationally efficient simulation of neurons in a clock-driven and synapses in an event-driven fashion; memory efficient representation of simulation state; and communication efficient message exchanges. Using phenomenological, single-compartment models of spiking neurons and synapses with spike-timing dependent plasticity, we represented a rat-scale cortical model (55 million neurons, 442 billion synapses) in 8TB memory of a 32,768processor BlueGene/L. With 1 millisecond resolution for neuronal dynamics and 1-20 milliseconds axonal delays, C2 can simulate 1 second of model time in 9 seconds per Hertz of average neuronal firing rate. In summary, by combining state-of-the-art hardware with innovative algorithms and software design, we simultaneously achieved unprecedented time-to-solution on an unprecedented problem size. 1

Gibbs Sampling with Low-Power Spiking Digital Neurons

Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in a wide variety of applications including image classification and speech recognition. Inference and learning in these algorithms uses a Markov Chain Monte Carlo procedure called Gibbs sampling. A sigmoidal function forms the kernel of this sampler which can be realized from the firing statistics of noisy integrate-and-fire neurons on a neuromorphic VLSI substrate. This paper demonstrates such an implementation on an array of digital spiking neurons with stochastic leak and threshold properties for inference tasks and presents some key performance metrics for such a hardware-based sampler in both the generative and discriminative contexts.Comment: Accepted at ISCAS 201

Implementation of Olfactory Bulb Glomerular-Layer Computations in a Digital Neurosynaptic Core

We present a biomimetic system that captures essential functional properties of the glomerular layer of the mammalian olfactory bulb, specifically including its capacity to decorrelate similar odor representations without foreknowledge of the statistical distributions of analyte features. Our system is based on a digital neuromorphic chip consisting of 256 leaky-integrate-and-fire neurons, 1024 × 256 crossbar synapses, and address-event representation communication circuits. The neural circuits configured in the chip reflect established connections among mitral cells, periglomerular cells, external tufted cells, and superficial short-axon cells within the olfactory bulb, and accept input from convergent sets of sensors configured as olfactory sensory neurons. This configuration generates functional transformations comparable to those observed in the glomerular layer of the mammalian olfactory bulb. Our circuits, consuming only 45 pJ of active power per spike with a power supply of 0.85 V, can be used as the first stage of processing in low-power artificial chemical sensing devices inspired by natural olfactory systems