Towards reverse engineering the brain: Modeling abstractions and simulation frameworks

Nageswaran, Jayram Moorkanikara; Richert, Micah; Dutt, Nikil; Krichmar, Jeffrey L.

doi:10.1109/vlsisoc.2010.5642630

Cited by 7 publications

(5 citation statements)

References 35 publications

(66 reference statements)

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“…The reasons for such ultra-low power consumption-compiled by Ref. [14]-include sub-threshold spiking operations, sparse-energy efficient codes for signaling, and proper balance of analog computation and digital signaling.…”

Section: Power Efficiencymentioning

confidence: 99%

System-On-Chip for Biologically Inspired Vision Applications

Park

Maashri

Irick

et al. 2012

IPSJ Transactions on System LSI Design Methodology

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Neuromorphic vision algorithms are biologically-inspired computational models of the primate visual pathway. They promise robustness, high accuracy, and high energy efficiency in advanced image processing applications. Despite these potential benefits, the realization of neuromorphic algorithms typically exhibit low performance even when executed on multi-core CPU and GPU platforms. This is due to the disparity in the computational modalities prominent in these algorithms and those modalities most exploited in contemporary computer architectures. In essence, acceleration of neuromorphic algorithms requires adherence to specific computational and communicational requirements. This paper discusses these requirements and proposes a framework for mapping neuromorphic vision applications on a System-on-Chip, SoC. A neuromorphic object detection and recognition on a multi-FPGA platform is presented with performance and power efficiency comparisons to CMP and GPU implementations.

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Section: Power Efficiencymentioning

confidence: 99%

System-On-Chip for Biologically Inspired Vision Applications

Park

Maashri

Irick

et al. 2012

IPSJ Transactions on System LSI Design Methodology

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“…In this research field, neuroscientists have explored several computational models of brain processing, providing the promise of practical applications in many domains (e.g., vision, navigation, motor control, decision-making, etc.). These developments have led to different approaches [Nageswaran et al 2010] such as neuromorphic VLSI [Mead 1990;Furber and Temple 2007], neuro-biological systems [Jiping et al 2001;Giotis et al 2011;Theodorou and Valero-Cuevas 2010] and brain-inspired algorithms [FrostGorder 2008;Jhuang et al 2008;Soo-Young 2007].…”

Section: Introductionmentioning

confidence: 99%

“…Levels of abstraction used in these research works range from biophysical up to theoretical models through neural-circuit, application-specific and generic models (see Nageswaran et al [2010] for more details). Our concern is on generic models that rely on the fact that brain-circuits have a template architecture where similar circuits are replicated for processing and learning various sensory signals [Hawkins and Blakeslee 2004;Granger 2006].…”

Section: Introductionmentioning

confidence: 99%

Fully Binary Neural Network Model and Optimized Hardware Architectures for Associative Memories

Coussy

Wouafo

Conde-Canencia

2015

J. Emerg. Technol. Comput. Syst.

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Brain processes information through a complex hierarchical associative memory organization that is distributed across a complex neural network. The GBNN associative memory model has recently been proposed as a new class of recurrent clustered neural network that presents higher efficiency than the classical models. In this article, we propose computational simplifications and architectural optimizations of the original GBNN. This work leads to significant complexity and area reduction without affecting neither memorizing nor retrieving performance. The obtained results open new perspectives in the design of neuromorphic hardware to support large-scale general-purpose neural algorithms.

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“…Although much progress has been made in simulating large-scale spiking neural networks (SNNs), there are still many challenges to overcome before these neurobiologically inspired algorithms can be used in practical applications that can be deployed on neuromorphic hardware (Boahen, 2005 ; Markram, 2006 ; Nageswaran et al, 2010 ; Indiveri et al, 2011 ). Moreover, it has been difficult to construct SNNs large enough to describe the complex functionality and dynamics found in real nervous systems (Izhikevich and Edelman, 2008 ; Krichmar et al, 2011 ; Eliasmith et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

An efficient automated parameter tuning framework for spiking neural networks

Carlson

Nageswaran²,

Dutt

et al. 2014

Front. Neurosci.

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As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex neural circuits that explore various neural phenomena such as neural plasticity, vision systems, auditory systems, neural oscillations, and many other important topics of neural function. Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. Although the inclusion of realistic plasticity equations, neural dynamics, and recurrent topologies has increased the descriptive power of SNNs, it has also made the task of tuning these biologically realistic SNNs difficult. To meet this challenge, we present an automated parameter tuning framework capable of tuning SNNs quickly and efficiently using evolutionary algorithms (EA) and inexpensive, readily accessible graphics processing units (GPUs). A sample SNN with 4104 neurons was tuned to give V1 simple cell-like tuning curve responses and produce self-organizing receptive fields (SORFs) when presented with a random sequence of counterphase sinusoidal grating stimuli. A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation over the CPU implementation, or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. Additionally, the parameter value solutions found in the tuned SNN were studied and found to be stable and repeatable. The automated parameter tuning framework presented here will be of use to both the computational neuroscience and neuromorphic engineering communities, making the process of constructing and tuning large-scale SNNs much quicker and easier.

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Towards reverse engineering the brain: Modeling abstractions and simulation frameworks

Cited by 7 publications

References 35 publications

System-On-Chip for Biologically Inspired Vision Applications

System-On-Chip for Biologically Inspired Vision Applications

Fully Binary Neural Network Model and Optimized Hardware Architectures for Associative Memories

An efficient automated parameter tuning framework for spiking neural networks

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