1,615 research outputs found
Limits to high-speed simulations of spiking neural networks using general-purpose computers
To understand how the central nervous system performs computations using recurrent neuronal circuitry, simulations have become an indispensable tool for theoretical neuroscience. To study neuronal circuits and their ability to self-organize, increasing attention has been directed toward synaptic plasticity. In particular spike-timing-dependent plasticity (STDP) creates specific demands for simulations of spiking neural networks. On the one hand a high temporal resolution is required to capture the millisecond timescale of typical STDP windows. On the other hand network simulations have to evolve over hours up to days, to capture the timescale of long-term plasticity. To do this efficiently, fast simulation speed is the crucial ingredient rather than large neuron numbers. Using different medium-sized network models consisting of several thousands of neurons and off-the-shelf hardware, we compare the simulation speed of the simulators: Brian, NEST and Neuron as well as our own simulator Auryn. Our results show that real-time simulations of different plastic network models are possible in parallel simulations in which numerical precision is not a primary concern. Even so, the speed-up margin of parallelism is limited and boosting simulation speeds beyond one tenth of real-time is difficult. By profiling simulation code we show that the run times of typical plastic network simulations encounter a hard boundary. This limit is partly due to latencies in the inter-process communications and thus cannot be overcome by increased parallelism. Overall, these results show that to study plasticity in medium-sized spiking neural networks, adequate simulation tools are readily available which run efficiently on small clusters. However, to run simulations substantially faster than real-time, special hardware is a prerequisite
GeNN: a code generation framework for accelerated brain simulations
Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ.
GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials,
Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/
Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections
Cortical synapse organization supports a range of dynamic states on multiple
spatial and temporal scales, from synchronous slow wave activity (SWA),
characteristic of deep sleep or anesthesia, to fluctuating, asynchronous
activity during wakefulness (AW). Such dynamic diversity poses a challenge for
producing efficient large-scale simulations that embody realistic metaphors of
short- and long-range synaptic connectivity. In fact, during SWA and AW
different spatial extents of the cortical tissue are active in a given timespan
and at different firing rates, which implies a wide variety of loads of local
computation and communication. A balanced evaluation of simulation performance
and robustness should therefore include tests of a variety of cortical dynamic
states. Here, we demonstrate performance scaling of our proprietary Distributed
and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and
AW for bidimensional grids of neural populations, which reflects the modular
organization of the cortex. We explored networks up to 192x192 modules, each
composed of 1250 integrate-and-fire neurons with spike-frequency adaptation,
and exponentially decaying inter-modular synaptic connectivity with varying
spatial decay constant. For the largest networks the total number of synapses
was over 70 billion. The execution platform included up to 64 dual-socket
nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40GHz
clock rates. Network initialization time, memory usage, and execution time
showed good scaling performances from 1 to 1024 processes, implemented using
the standard Message Passing Interface (MPI) protocol. We achieved simulation
speeds of between 2.3x10^9 and 4.1x10^9 synaptic events per second for both
cortical states in the explored range of inter-modular interconnections.Comment: 22 pages, 9 figures, 4 table
Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections
Cortical synapse organization supports a range of dynamic states on multiple
spatial and temporal scales, from synchronous slow wave activity (SWA),
characteristic of deep sleep or anesthesia, to fluctuating, asynchronous
activity during wakefulness (AW). Such dynamic diversity poses a challenge for
producing efficient large-scale simulations that embody realistic metaphors of
short- and long-range synaptic connectivity. In fact, during SWA and AW
different spatial extents of the cortical tissue are active in a given timespan
and at different firing rates, which implies a wide variety of loads of local
computation and communication. A balanced evaluation of simulation performance
and robustness should therefore include tests of a variety of cortical dynamic
states. Here, we demonstrate performance scaling of our proprietary Distributed
and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and
AW for bidimensional grids of neural populations, which reflects the modular
organization of the cortex. We explored networks up to 192x192 modules, each
composed of 1250 integrate-and-fire neurons with spike-frequency adaptation,
and exponentially decaying inter-modular synaptic connectivity with varying
spatial decay constant. For the largest networks the total number of synapses
was over 70 billion. The execution platform included up to 64 dual-socket
nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40GHz
clock rates. Network initialization time, memory usage, and execution time
showed good scaling performances from 1 to 1024 processes, implemented using
the standard Message Passing Interface (MPI) protocol. We achieved simulation
speeds of between 2.3x10^9 and 4.1x10^9 synaptic events per second for both
cortical states in the explored range of inter-modular interconnections.Comment: 22 pages, 9 figures, 4 table
Closed loop interactions between spiking neural network and robotic simulators based on MUSIC and ROS
In order to properly assess the function and computational properties of
simulated neural systems, it is necessary to account for the nature of the
stimuli that drive the system. However, providing stimuli that are rich and yet
both reproducible and amenable to experimental manipulations is technically
challenging, and even more so if a closed-loop scenario is required. In this
work, we present a novel approach to solve this problem, connecting robotics
and neural network simulators. We implement a middleware solution that bridges
the Robotic Operating System (ROS) to the Multi-Simulator Coordinator (MUSIC).
This enables any robotic and neural simulators that implement the corresponding
interfaces to be efficiently coupled, allowing real-time performance for a wide
range of configurations. This work extends the toolset available for
researchers in both neurorobotics and computational neuroscience, and creates
the opportunity to perform closed-loop experiments of arbitrary complexity to
address questions in multiple areas, including embodiment, agency, and
reinforcement learning
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
Recent studies have shown that synaptic unreliability is a robust and
sufficient mechanism for inducing the stochasticity observed in cortex. Here,
we introduce Synaptic Sampling Machines, a class of neural network models that
uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised
learning. Similar to the original formulation of Boltzmann machines, these
models can be viewed as a stochastic counterpart of Hopfield networks, but
where stochasticity is induced by a random mask over the connections. Synaptic
stochasticity plays the dual role of an efficient mechanism for sampling, and a
regularizer during learning akin to DropConnect. A local synaptic plasticity
rule implementing an event-driven form of contrastive divergence enables the
learning of generative models in an on-line fashion. Synaptic sampling machines
perform equally well using discrete-timed artificial units (as in Hopfield
networks) or continuous-timed leaky integrate & fire neurons. The learned
representations are remarkably sparse and robust to reductions in bit precision
and synapse pruning: removal of more than 75% of the weakest connections
followed by cursory re-learning causes a negligible performance loss on
benchmark classification tasks. The spiking neuron-based synaptic sampling
machines outperform existing spike-based unsupervised learners, while
potentially offering substantial advantages in terms of power and complexity,
and are thus promising models for on-line learning in brain-inspired hardware
Simulation of networks of spiking neurons: A review of tools and strategies
We review different aspects of the simulation of spiking neural networks. We
start by reviewing the different types of simulation strategies and algorithms
that are currently implemented. We next review the precision of those
simulation strategies, in particular in cases where plasticity depends on the
exact timing of the spikes. We overview different simulators and simulation
environments presently available (restricted to those freely available, open
source and documented). For each simulation tool, its advantages and pitfalls
are reviewed, with an aim to allow the reader to identify which simulator is
appropriate for a given task. Finally, we provide a series of benchmark
simulations of different types of networks of spiking neurons, including
Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based
or conductance-based synapses, using clock-driven or event-driven integration
strategies. The same set of models are implemented on the different simulators,
and the codes are made available. The ultimate goal of this review is to
provide a resource to facilitate identifying the appropriate integration
strategy and simulation tool to use for a given modeling problem related to
spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of
Computational Neuroscience, in press (2007
Principles of Neuromorphic Photonics
In an age overrun with information, the ability to process reams of data has
become crucial. The demand for data will continue to grow as smart gadgets
multiply and become increasingly integrated into our daily lives.
Next-generation industries in artificial intelligence services and
high-performance computing are so far supported by microelectronic platforms.
These data-intensive enterprises rely on continual improvements in hardware.
Their prospects are running up against a stark reality: conventional
one-size-fits-all solutions offered by digital electronics can no longer
satisfy this need, as Moore's law (exponential hardware scaling),
interconnection density, and the von Neumann architecture reach their limits.
With its superior speed and reconfigurability, analog photonics can provide
some relief to these problems; however, complex applications of analog
photonics have remained largely unexplored due to the absence of a robust
photonic integration industry. Recently, the landscape for
commercially-manufacturable photonic chips has been changing rapidly and now
promises to achieve economies of scale previously enjoyed solely by
microelectronics.
The scientific community has set out to build bridges between the domains of
photonic device physics and neural networks, giving rise to the field of
\emph{neuromorphic photonics}. This article reviews the recent progress in
integrated neuromorphic photonics. We provide an overview of neuromorphic
computing, discuss the associated technology (microelectronic and photonic)
platforms and compare their metric performance. We discuss photonic neural
network approaches and challenges for integrated neuromorphic photonic
processors while providing an in-depth description of photonic neurons and a
candidate interconnection architecture. We conclude with a future outlook of
neuro-inspired photonic processing.Comment: 28 pages, 19 figure
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