The availability of efficient and reliable simulation tools is one of the mission-critical technologies in the fast-moving field of computational neuroscience. Research indicates that higher brain functions emerge from large and complex cortical networks and their interactions. The large number of elements (neurons) combined with the high connectivity (synapses) of the biological network and the specific type of interactions impose severe constraints on the explorable system size that previously have been hard to overcome. Here we present a collection of new techniques combined to a coherent simulation tool removing the fundamental obstacle in the computational study of biological neural networks: the enormous number of synaptic contacts per neuron. Distributing an individual simulation over multiple computers enables the investigation of networks orders of magnitude larger than previously possible. The

Computational neuroscience relies heavily on the simulation of large networks of neuron models. There are essentially two simulation strategies: 1) using an approximation method (e.g. Runge-Kutta) with spike times binned to the time step; 2) calculating spike times exactly in an event-driven fashion. In large networks, the computation time of the best algorithm for either strategy scales linearly with the number of synapses, but each strategy has its own assets and constraints: approximation methods can be applied to any model but are inexact; exact simulation avoids numerical artefacts but is limited to simple models. Previous work has focused on improving the accuracy of approximation methods. In this paper we extend the range of models that can be simulated exactly to a more realistic model, namely an integrate-and-fire model with exponential synaptic conductances.

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.

In the quest for cognitive computing, we have built a massively parallel cortical simulator, C2, that incorporates a number of innovations in computation, memory, and communication. Using C2 on LLNL’s Dawn Blue Gene/P supercomputer with 147, 456 CPUs and 144 TB of main memory, we report two cortical simulations – at unprecedented scale – that effectively saturate the entire memory capacity and refresh it at least every simulated second. The first simulation consists of 1.6 billion neurons and 8.87 trillion synapses with experimentally-measured gray matter thalamocortical connectivity. The second simulation has 900 million neurons and 9 trillion synapses with probabilistic connectivity. We demonstrate nearly perfect weak scaling and attractive strong scaling. The simulations, which incorporate phenomenological spiking neurons, individual learning synapses, axonal delays, and dynamic synaptic channels, exceed the scale of the cat cortex, marking the dawn of a new era in the scale of cortical simulations. 1.

In this paper we present a conceptual view on the incorporation of cognitive processing capabilities in future generation computer systems. We argue that cognition is at the heart of autonomic behavior, and therefore a necessary ingredient for autonomic computing and communication. We introduce a bioinspired cognitive engine that interacts with and has control over major operating system components, and showcase, based on scenario descriptions, how communicating applications take advantage of this setup by adapting and autonomously reacting to changes in heterogeneous and widely varying network resources. As a conceptual work, this paper does not cover experiences with the implementation of the introduced concepts, nor does it describe experimental results. 1.

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Abstract—A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components to manage the tradeoff between flexibility and computational power; the neuron model is implemented in hardware and the network model and the learning are implemented in software. The incremental transition of the software components into hardware is supported. We focus on a spike response model (SRM) for a neuron where the synapses are modeled as input-driven conductances. The temporal dynamics of the synaptic integration process are modeled with a synaptic time constant that results in a gradual injection of charge. This type of model is computationally expensive and is not easily amenable to existing software-based event-driven approaches. As an alternative we have designed an efficient time-based computing architecture in hardware, where the different stages of the neuron model are processed in parallel. Further improvements occur by computing multiple neurons in parallel using multiple processing units. This design is tested using reconfigurable hardware and its scalability and performance evaluated. Our overall goal is to investigate biologically realistic models for the real-time control of robots operating within closed action-perception loops, and so we evaluate the performance of the system on simulating a model of the cerebellum where the emulation of the temporal dynamics of the synaptic integration process is important. Index Terms—Field-programmable gate arrays, pipeline processing, real time system, spiking neural network hardware. I.

This dissertation attempts to shed new light on the mechanisms used by human subjects to extract features from visual stimuli and for their subsequent classification. A methodology combining human psychophysics and machine learning is introduced, where feature extractors are modeled using methods from unsupervised machine learning whereas supervised machine learning is considered for classification. We consider a gender classification task using stimuli drawn from the Max Planck Institute face database. Once a feature extractor is chosen and the corresponding data representation is computed, the resulting feature vector is classified using a separating hyperplane (SH) between the classes. The behavioral responses of humans to one stimulus, in our study the gender estimate and its corresponding reaction time and confidence rating, are compared and correlated to the distance of the feature vector of this stimulus to the SH. It is successfully demonstrated that machine learning can be used as a novel method to “look into the human

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