• Spikenet

  • Referenced in 12 articles [sw08892]
  • large networks of spiking neurons. Many biological neural network models face the problem of scalability ... neural network simulation package. The type of model one can build is not only biologically...
  • BIOSIM

  • Referenced in 2 articles [sw01083]
  • BIOSIM -- A biological neural network simulator for research and teaching, featuring interactive graphical user interface ... learning capabilities BIOSIM is a biologically oriented neural network simulator, which is available as public ... Additional synaptic types can be created interactively. Biologically oriented learning and forgetting processes are modeled ... hebbian learning and competition learning. A neural network can be created by using the interactive...
  • XDANNG

  • Referenced in 2 articles [sw20348]
  • Neural Network with Globus Toolkit. Artificial Neural Network is one of the most common ... goal of ANN is to imitate biological neural networks for solving scientific problems ... problem of ANN systems in comparison with biological systems. To solve this problem, we have...
  • Leabra7

  • Referenced in 1 article [sw26111]
  • Python package for modeling recurrent, biologically-realistic neural networks. Emergent is a software package that ... train arbitrary recurrent neural network architectures in a biologically-realistic manner. We present Leabra7...
  • STICK

  • Referenced in 2 articles [sw40455]
  • primarily developed to mimic biology. They use neural networks, which can be trained to perform ... These machines can do more than simulate biology; they allow us to rethink our current...
  • LSTM

  • Referenced in 30 articles [sw03373]
  • human brain is a recurrent neural network (RNN): a network of neurons with feedback connections ... teacher. They are computationally more powerful and biologically more plausible than other adaptive approaches such...
  • CodeNet

  • Referenced in 1 article [sw30730]
  • first strategy for error-resilient neural network training by encoding each layer separately; (ii) Keeping ... significant step towards biologically plausible neural network training, that could hold the key to orders...
  • AnimatLab

  • Referenced in 1 article [sw23735]
  • that combines biomechanical simulation and biologically realistic neural networks. You can build the body ... rate and leaky integrate and fire spiking neural models. In addition, there a number ... used to connect the various neural models to produce your nervous system. On the biomechanics ... animats, that are based on real biological systems. Best of all standard AnimatLab is completely...
  • DDLab

  • Referenced in 6 articles [sw07888]
  • used in research and education. These collective networks are at the core of complexity ... biology, cognition, society, economics and computation, and more specifically in neural and genetic networks, artificial...
  • TopologyNet

  • Referenced in 7 articles [sw41013]
  • TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions. Although deep learning approaches ... hindered by the entangled geometric complexity and biological complexity. We introduce topology, i.e., element specific ... homology (ESPH), to untangle geometric complexity and biological complexity. ESPH represents 3D complex geometry ... further integrate ESPH and convolutional neural networks to construct a multichannel topological neural network (TopologyNet...
  • hebbRNN

  • Referenced in 1 article [sw29194]
  • motor skills using neural networks. However, training these networks to produce meaningful behavior has proven ... most common methods are generally not biologically-plausible and rely on information not local ... implementation of a biologically-plausible training rule for recurrent neural networks using a delayed...
  • GANNPhos

  • Referenced in 1 article [sw30119]
  • genetic algorithm integrated neural network. With the advance of modern molecular biology it has become ... studies. Using a genetic algorithm integrated neural network (GANN), a new bioinformatics method named GANNPhos...
  • NETASA

  • Referenced in 3 articles [sw35774]
  • NETASA: neural network based prediction of solvent accessibility. Motivation: Prediction of the tertiary structure ... most important problems in molecular biology. The successful prediction of solvent accessibility will be very ... amino acids using our newly optimized neural network algorithm. Several new features in the neural...
  • CARLsim

  • Referenced in 1 article [sw30218]
  • scale spiking neural network (SNN) models with a high degree of biological detail. CARLsim allows...
  • Chainer Chemistry

  • Referenced in 1 article [sw39646]
  • framework (based on Chainer) with applications in Biology and Chemistry. It supports various state ... models (especially GCNN - Graph Convolutional Neural Network) for chemical property prediction...
  • TUNE

  • Referenced in 1 article [sw35757]
  • Threading Using Neural nEtwork (TUNE): the measure of protein sequence - structure compatibility. Motivation: Fold recognition ... predict the 3D structure and often biological function of the probe. Here we present ... protein sequence–structure compatibility. An artificial neural network model is trained to predict compatibility...
  • BindsNET

  • Referenced in 1 article [sw30217]
  • neural network simulation software is a critical component enabling the modeling of neural systems ... biologically inspired algorithms. Existing software frameworks support a wide range of neural functionality, software abstraction ... package for the simulation of spiking neural networks, specifically geared towards machine learning and reinforcement...
  • CrossNets

  • Referenced in 3 articles [sw01762]
  • integrated circuits, incorporating advanced CMOS devices for neural cell bodies, nanowires as axons and dendrites ... neuromorphic networks, operating up to 10 6 times faster than their biological prototypes...
  • DGL-LifeSci

  • Referenced in 1 article [sw39641]
  • Graphs in Life Science. Graph neural networks (GNNs) constitute a class of deep learning methods ... They have wide applications in chemistry and biology, such as molecular property prediction, reaction prediction...
  • SpineML

  • Referenced in 1 article [sw40078]
  • model description language for large scale neural network models. It is partially based upon ... scale networks of point neurons but also has the flexibility to describe biologically constrained models...