3 research outputs found
Gaussian and exponential lateral connectivity on distributed spiking neural network simulation
We measured the impact of long-range exponentially decaying intra-areal
lateral connectivity on the scaling and memory occupation of a distributed
spiking neural network simulator compared to that of short-range Gaussian
decays. While previous studies adopted short-range connectivity, recent
experimental neurosciences studies are pointing out the role of longer-range
intra-areal connectivity with implications on neural simulation platforms.
Two-dimensional grids of cortical columns composed by up to 11 M point-like
spiking neurons with spike frequency adaption were connected by up to 30 G
synapses using short- and long-range connectivity models. The MPI processes
composing the distributed simulator were run on up to 1024 hardware cores,
hosted on a 64 nodes server platform. The hardware platform was a cluster of
IBM NX360 M5 16-core compute nodes, each one containing two Intel Xeon Haswell
8-core E5-2630 v3 processors, with a clock of 2.40 G Hz, interconnected through
an InfiniBand network, equipped with 4x QDR switches.Comment: 9 pages, 9 figures, added reference to final peer reviewed version on
conference paper and DO
Real-time cortical simulations: energy and interconnect scaling on distributed systems
We profile the impact of computation and inter-processor communication on the
energy consumption and on the scaling of cortical simulations approaching the
real-time regime on distributed computing platforms. Also, the speed and energy
consumption of processor architectures typical of standard HPC and embedded
platforms are compared. We demonstrate the importance of the design of
low-latency interconnect for speed and energy consumption. The cost of cortical
simulations is quantified using the Joule per synaptic event metric on both
architectures. Reaching efficient real-time on large scale cortical simulations
is of increasing relevance for both future bio-inspired artificial intelligence
applications and for understanding the cognitive functions of the brain, a
scientific quest that will require to embed large scale simulations into highly
complex virtual or real worlds. This work stands at the crossroads between the
WaveScalES experiment in the Human Brain Project (HBP), which includes the
objective of large scale thalamo-cortical simulations of brain states and their
transitions, and the ExaNeSt and EuroExa projects, that investigate the design
of an ARM-based, low-power High Performance Computing (HPC) architecture with a
dedicated interconnect scalable to million of cores; simulation of deep sleep
Slow Wave Activity (SWA) and Asynchronous aWake (AW) regimes expressed by
thalamo-cortical models are among their benchmarks.Comment: 8 pages, 8 figures, 4 tables, submitted after final publication on
PDP2019 proceedings, corrected final DOI. arXiv admin note: text overlap with
arXiv:1812.04974, arXiv:1804.0344
Sleep-like slow oscillations improve visual classification through synaptic homeostasis and memory association in a thalamo-cortical model
The occurrence of sleep passed through the evolutionary sieve and is widespread in animal species. Sleep is known to be beneficial to cognitive and mnemonic tasks, while chronic sleep deprivation is detrimental. Despite the importance of the phenomenon, a complete understanding of its functions and underlying mechanisms is still lacking. In this paper, we show interesting effects of deep-sleep-like slow oscillation activity on a simplified thalamo-cortical model which is trained to encode, retrieve and classify images of handwritten digits. During slow oscillations, spike-timing-dependent-plasticity (STDP) produces a differential homeostatic process. It is characterized by both a specific unsupervised enhancement of connections among groups of neurons associated to instances of the same class (digit) and a simultaneous down-regulation of stronger synapses created by the training. This hierarchical organization of post-sleep internal representations favours higher performances in retrieval and classification tasks. The mechanism is based on the interaction between top-down cortico-thalamic predictions and bottom-up thalamo-cortical projections during deep-sleep-like slow oscillations. Indeed, when learned patterns are replayed during sleep, cortico-thalamo-cortical connections favour the activation of other neurons coding for similar thalamic inputs, promoting their association. Such mechanism hints at possible applications to artificial learning systems