• SpiNNaker

  • Referenced in 29 articles [sw40456]
  • billion simple neurons, or millions of neurons with complex structure and internal dynamics. Robotics: SpiNNaker ... network of tens of thousands of spiking neurons, process sensory input and generate motor output ... computation. SpiNNaker nodes communicate using simple messages (spikes) that are inherently unreliable. This break with...
  • Spikenet

  • Referenced in 12 articles [sw08892]
  • package for modelling large networks of spiking neurons. Many biological neural network models face ... average spike discharge rate is low, millions of neurons and billions of connections...
  • NEURON

  • Referenced in 187 articles [sw03059]
  • Parallel network simulations with NEURON. The NEURON simulation environment has been extended to support parallel ... interval equal to the minimum (interprocessor) presynaptic spike generation to postsynaptic spike delivery connection delay...
  • STAR

  • Referenced in 4 articles [sw21311]
  • Analysis with R. Functions to analyze neuronal spike trains from a single neuron or from...
  • ppstat

  • Referenced in 3 articles [sw14597]
  • things as financial trade times and neuron spike times...
  • SPIKY

  • Referenced in 2 articles [sw35811]
  • synchrony. Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads...
  • ANNarchy

  • Referenced in 2 articles [sw30220]
  • simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience ... easily define and simulate rate-coded and spiking networks, as well as combinations of both ... PyNN interface, while the definition of neuron and synapse models can be specified using...
  • NEVESIM

  • Referenced in 1 article [sw39766]
  • event-driven simulation of networks of spiking neurons with a fast simulation core ... easily extended by the user with new neuron and synapse types. To enable heterogeneous networks ... simulation logic of communicating events (spikes) between the neurons at a network level from ... exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework...
  • Brian

  • Referenced in 29 articles [sw23588]
  • python. ”Brian” is a new simulator for spiking neural networks, written in Python (http://brian ... models, especially networks of single-compartment neurons. In addition to using standard types of neuron...
  • popTRT

  • Referenced in 2 articles [sw30080]
  • observed data. For spike train models of individual neurons, many goodness-of-fit measures rely ... theorem and assess model quality using rescaled spike times. Recently, there has been increasing interest ... models that describe the simultaneous spiking activity of neuron populations, either in a single brain ... have used spike sorted data to describe relationships between the identified neurons, but more recently...
  • NEST

  • Referenced in 32 articles [sw26849]
  • detailed morphological and biophysical properties of individual neurons. Examples are: Models of sensory processing ... networks or random balanced networks. Models of spike-synchronization in feed-forward networks such...
  • SEFRON

  • Referenced in 1 article [sw41928]
  • SEFRON: A New Spiking Neuron Model With Time-Varying Synaptic Efficacy Function for Pattern Classification ... Efficacy Function-based leaky-integrate-and-fire neuRON model, referred to as SEFRON ... amplitudes of weights at selected presynaptic spike times by minimizing a new error function reflecting ... acid-switch phenomenon observed in a biological neuron that switches between excitatory and inhibitory postsynaptic...
  • SPySort

  • Referenced in 1 article [sw27042]
  • SPySort: Neuronal Spike Sorting with Python. Extracellular recordings with multi-electrode arrays ... emitted action potentials, of many individual neurons. But the raw data produced by extracellular recordings ... individual contributing neurons, a pre-processing step called spike sorting is required. We present here...
  • CARLsim

  • Referenced in 1 article [sw30218]
  • allows execution of networks of Izhikevich spiking neurons with realistic synaptic dynamics on both generic...
  • SpiCoDyn

  • Referenced in 1 article [sw40614]
  • analysis of multi-site neuronal spike signals. Accepted input data formats are HDF5, level...
  • Py-oopsi

  • Referenced in 1 article [sw27105]
  • widely used to extract neuron spike activities from calcium fluorescence signals. Here, we propose detailed...
  • S4NN

  • Referenced in 1 article [sw41327]
  • spiking neural networks with one spike per neuron. We propose a new supervised learning rule ... multilayer spiking neural networks (SNNs) that use a form of temporal coding known as rank ... With this coding scheme, all neurons fire exactly one spike per stimulus, but the firing ... particular, in the readout layer, the first neuron to fire determines the class...
  • PCSIM

  • Referenced in 2 articles [sw12749]
  • simulation of large scale networks of spiking point neurons. Although its computational core is written...
  • CoCoNAD

  • Referenced in 3 articles [sw40294]
  • CoCoNAD (Continuous-time Closed Neuron Assembly Detection) is a program to find frequent imprecisely synchronous ... applications in the analysis of parallel spike trains. The idea is to provide a method ... encoded by temporally coincident spiking of groups of neurons, sometimes called cell assemblies. The Python...
  • Metastability

  • Referenced in 0 articles [sw38860]
  • networks of intrinsically bursting neurons. Active neurons can be broadly classified by their intrinsic oscillation ... spiking or bursting. Here, we show that networks of identical bursting neurons with inhibitory pulsatory ... Using the relative phases of bursts between neurons, we numerically demonstrate that the network exhibits ... reveal that networks of identical singlet-spiking neurons do not exhibit such complexity. These results...