13,879 research outputs found
Integration of continuous-time dynamics in a spiking neural network simulator
Contemporary modeling approaches to the dynamics of neural networks consider
two main classes of models: biologically grounded spiking neurons and
functionally inspired rate-based units. The unified simulation framework
presented here supports the combination of the two for multi-scale modeling
approaches, the quantitative validation of mean-field approaches by spiking
network simulations, and an increase in reliability by usage of the same
simulation code and the same network model specifications for both model
classes. While most efficient spiking simulations rely on the communication of
discrete events, rate models require time-continuous interactions between
neurons. Exploiting the conceptual similarity to the inclusion of gap junctions
in spiking network simulations, we arrive at a reference implementation of
instantaneous and delayed interactions between rate-based models in a spiking
network simulator. The separation of rate dynamics from the general connection
and communication infrastructure ensures flexibility of the framework. We
further demonstrate the broad applicability of the framework by considering
various examples from the literature ranging from random networks to neural
field models. The study provides the prerequisite for interactions between
rate-based and spiking models in a joint simulation
VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing
The hardware implementation of deep neural networks (DNNs) has recently
received tremendous attention: many applications in fact require high-speed
operations that suit a hardware implementation. However, numerous elements and
complex interconnections are usually required, leading to a large area
occupation and copious power consumption. Stochastic computing has shown
promising results for low-power area-efficient hardware implementations, even
though existing stochastic algorithms require long streams that cause long
latencies. In this paper, we propose an integer form of stochastic computation
and introduce some elementary circuits. We then propose an efficient
implementation of a DNN based on integral stochastic computing. The proposed
architecture has been implemented on a Virtex7 FPGA, resulting in 45% and 62%
average reductions in area and latency compared to the best reported
architecture in literature. We also synthesize the circuits in a 65 nm CMOS
technology and we show that the proposed integral stochastic architecture
results in up to 21% reduction in energy consumption compared to the binary
radix implementation at the same misclassification rate. Due to fault-tolerant
nature of stochastic architectures, we also consider a quasi-synchronous
implementation which yields 33% reduction in energy consumption w.r.t. the
binary radix implementation without any compromise on performance.Comment: 11 pages, 12 figure
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition
A neuromorphic chip that combines CMOS analog spiking neurons and memristive
synapses offers a promising solution to brain-inspired computing, as it can
provide massive neural network parallelism and density. Previous hybrid analog
CMOS-memristor approaches required extensive CMOS circuitry for training, and
thus eliminated most of the density advantages gained by the adoption of
memristor synapses. Further, they used different waveforms for pre and
post-synaptic spikes that added undesirable circuit overhead. Here we describe
a hardware architecture that can feature a large number of memristor synapses
to learn real-world patterns. We present a versatile CMOS neuron that combines
integrate-and-fire behavior, drives passive memristors and implements
competitive learning in a compact circuit module, and enables in-situ
plasticity in the memristor synapses. We demonstrate handwritten-digits
recognition using the proposed architecture using transistor-level circuit
simulations. As the described neuromorphic architecture is homogeneous, it
realizes a fundamental building block for large-scale energy-efficient
brain-inspired silicon chips that could lead to next-generation cognitive
computing.Comment: This is a preprint of an article accepted for publication in IEEE
Journal on Emerging and Selected Topics in Circuits and Systems, vol 5, no.
2, June 201
Analytic Performance Modeling and Analysis of Detailed Neuron Simulations
Big science initiatives are trying to reconstruct and model the brain by
attempting to simulate brain tissue at larger scales and with increasingly more
biological detail than previously thought possible. The exponential growth of
parallel computer performance has been supporting these developments, and at
the same time maintainers of neuroscientific simulation code have strived to
optimally and efficiently exploit new hardware features. Current state of the
art software for the simulation of biological networks has so far been
developed using performance engineering practices, but a thorough analysis and
modeling of the computational and performance characteristics, especially in
the case of morphologically detailed neuron simulations, is lacking. Other
computational sciences have successfully used analytic performance engineering
and modeling methods to gain insight on the computational properties of
simulation kernels, aid developers in performance optimizations and eventually
drive co-design efforts, but to our knowledge a model-based performance
analysis of neuron simulations has not yet been conducted.
We present a detailed study of the shared-memory performance of
morphologically detailed neuron simulations based on the Execution-Cache-Memory
(ECM) performance model. We demonstrate that this model can deliver accurate
predictions of the runtime of almost all the kernels that constitute the neuron
models under investigation. The gained insight is used to identify the main
governing mechanisms underlying performance bottlenecks in the simulation. The
implications of this analysis on the optimization of neural simulation software
and eventually co-design of future hardware architectures are discussed. In
this sense, our work represents a valuable conceptual and quantitative
contribution to understanding the performance properties of biological networks
simulations.Comment: 18 pages, 6 figures, 15 table
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/
Fast recursive filters for simulating nonlinear dynamic systems
A fast and accurate computational scheme for simulating nonlinear dynamic
systems is presented. The scheme assumes that the system can be represented by
a combination of components of only two different types: first-order low-pass
filters and static nonlinearities. The parameters of these filters and
nonlinearities may depend on system variables, and the topology of the system
may be complex, including feedback. Several examples taken from neuroscience
are given: phototransduction, photopigment bleaching, and spike generation
according to the Hodgkin-Huxley equations. The scheme uses two slightly
different forms of autoregressive filters, with an implicit delay of zero for
feedforward control and an implicit delay of half a sample distance for
feedback control. On a fairly complex model of the macaque retinal horizontal
cell it computes, for a given level of accuracy, 1-2 orders of magnitude faster
than 4th-order Runge-Kutta. The computational scheme has minimal memory
requirements, and is also suited for computation on a stream processor, such as
a GPU (Graphical Processing Unit).Comment: 20 pages, 8 figures, 1 table. A comparison with 4th-order Runge-Kutta
integration shows that the new algorithm is 1-2 orders of magnitude faster.
The paper is in press now at Neural Computatio
Cortical Dynamics of Contextually-Cued Attentive Visual Learning and Search: Spatial and Object Evidence Accumulation
How do humans use predictive contextual information to facilitate visual search? How are consistently paired scenic objects and positions learned and used to more efficiently guide search in familiar scenes? For example, a certain combination of objects can define a context for a kitchen and trigger a more efficient search for a typical object, such as a sink, in that context. A neural model, ARTSCENE Search, is developed to illustrate the neural mechanisms of such memory-based contextual learning and guidance, and to explain challenging behavioral data on positive/negative, spatial/object, and local/distant global cueing effects during visual search. The model proposes how global scene layout at a first glance rapidly forms a hypothesis about the target location. This hypothesis is then incrementally refined by enhancing target-like objects in space as a scene is scanned with saccadic eye movements. The model clarifies the functional roles of neuroanatomical, neurophysiological, and neuroimaging data in visual search for a desired goal object. In particular, the model simulates the interactive dynamics of spatial and object contextual cueing in the cortical What and Where streams starting from early visual areas through medial temporal lobe to prefrontal cortex. After learning, model dorsolateral prefrontal cortical cells (area 46) prime possible target locations in posterior parietal cortex based on goalmodulated percepts of spatial scene gist represented in parahippocampal cortex, whereas model ventral prefrontal cortical cells (area 47/12) prime possible target object representations in inferior temporal cortex based on the history of viewed objects represented in perirhinal cortex. The model hereby predicts how the cortical What and Where streams cooperate during scene perception, learning, and memory to accumulate evidence over time to drive efficient visual search of familiar scenes.CELEST, an NSF Science of Learning Center (SBE-0354378); SyNAPSE program of Defense Advanced Research Projects Agency (HR0011-09-3-0001, HR0011-09-C-0011
- …