Result for 0B614C4D890EE03CF987714F83EB9543DF3A5793

Query result

Key Value
FileName./usr/share/pyshared/brian/hears/filtering/__init__.py
FileSize288
MD59CB2C4A0F25C0C92D1A13B1343DF8506
SHA-10B614C4D890EE03CF987714F83EB9543DF3A5793
SHA-256C0FC4F26903C197696F35A36E6F7439D97F495D6A30D461DD09188581B7A83C1
SSDEEP6:1DGTIFJBvTIFDEINTIFDWmTIFDuXQTIFBb4oQT0LTFB7pFDX+PAJP8eXKBroGFRZ:1aEFJdEFnNEFimEFTEFB8oQT0LTFB7r2
TLSHT173D067339AFBB2A491FCD6C0A21746344373A1125F13941609A8233D23D7105CD25536
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize391972
MD538B29F07727B3A0A9C593D302D1E440E
PackageDescriptionsimulator for spiking neural networks Brian is a clock-driven simulator for spiking neural networks. It is designed with an emphasis on flexibility and extensibility, for rapid development and refinement of neural models. Neuron models are specified by sets of user-specified differential equations, threshold conditions and reset conditions (given as strings). The focus is primarily on networks of single compartment neuron models (e.g. leaky integrate-and-fire or Hodgkin-Huxley type neurons). Features include: - a system for specifying quantities with physical dimensions - exact numerical integration for linear differential equations - Euler, Runge-Kutta and exponential Euler integration for nonlinear differential equations - synaptic connections with delays - short-term and long-term plasticity (spike-timing dependent plasticity) - a library of standard model components, including integrate-and-fire equations, synapses and ionic currents - a toolbox for automatically fitting spiking neuron models to electrophysiological recordings
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython-brian
PackageSectionpython
PackageVersion1.3.1-1build1
SHA-1D22A96386DAAAEA729DD4FD466829609044013BA
SHA-2568DDF9628F43F67F9AFEFDEFC72832B8508A76F50009EDFF5A4F6FCE11469A8B0