An efficient automated parameter tuning framework for spiking neural networks
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 …
enormous number of open parameters in these models becomes a difficult challenge. SNNs …
An efficient simulation environment for modeling large-scale cortical processing
We have developed a spiking neural network simulator, which is both easy to use and
computationally efficient, for the generation of large-scale computational neuroscience …
computationally efficient, for the generation of large-scale computational neuroscience …
Large-scale spiking neural networks using neuromorphic hardware compatible models
Neuromorphic engineering is a fast growing field with great potential in both understanding
the function of the brain, and constructing practical artifacts that build upon this …
the function of the brain, and constructing practical artifacts that build upon this …
Cortical simulator for object-oriented simulation of a neural network
Embodiments of the invention relate to a function-level simulator for modeling a
neurosynaptic chip. One embodiment comprises simulating a neural network using an …
neurosynaptic chip. One embodiment comprises simulating a neural network using an …
GPGPU accelerated simulation and parameter tuning for neuromorphic applications
Neuromorphic engineering takes inspiration from biology to design brain-like systems that
are extremely low-power, fault-tolerant, and capable of adaptation to complex environments …
are extremely low-power, fault-tolerant, and capable of adaptation to complex environments …
System-on-chip for biologically inspired vision applications
S Park, A Al Maashri, KM Irick… - IPSJ Transactions on …, 2012 - jstage.jst.go.jp
Neuromorphic vision algorithms are biologically-inspired computational models of the
primate visual pathway. They promise robustness, high accuracy, and high energy efficiency …
primate visual pathway. They promise robustness, high accuracy, and high energy efficiency …
Fully binary neural network model and optimized hardware architectures for associative memories
P Coussy, C Chavet, HN Wouafo… - ACM Journal on …, 2015 - dl.acm.org
Brain processes information through a complex hierarchical associative memory
organization that is distributed across a complex neural network. The GBNN associative …
organization that is distributed across a complex neural network. The GBNN associative …
Evaluation of a system including separable sub-systems over a multidimensional range
An artificial neural network may be configured to test the impact of certain input parameters.
To improve testing efficiency and to avoid test runs that may not alter system performance …
To improve testing efficiency and to avoid test runs that may not alter system performance …
Neuromorphic modeling abstractions and simulation of large-scale cortical networks
Biological neural systems are well known for their robust and power-efficient operation in
highly noisy environments. We outline key modeling abstractions for the brain and focus on …
highly noisy environments. We outline key modeling abstractions for the brain and focus on …
Potential Applications of Systems Modeling Language and Systems Dynamics to Simulate and Model Complex Human Brain Functions
Modeling is a universal technique to understand the real world through abstraction. A model
is a representation of a selected part of the world. The success of the model is measured in …
is a representation of a selected part of the world. The success of the model is measured in …