Parents (Total: 4)

The searched file hash is included in 4 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
CRC32	D2203F0B
FileName	11853
FileSize	399380
MD5	38B4AF662FA8B47BD08F1780B41C07B3
OpSystemCode	362
PackageDescription	simulator 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
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python-brian
PackageSection	python
PackageVersion	1.4.3-1
ProductCode	184813
RDS:package_id	182052
SHA-1	1026CB23170136E7B3BFC13A9B58158B699DF220
SHA-256	38F33DE95F14AC719A3D0679A443967539763F698D73C8179A737EEE3B14BAE2
SpecialCode
db	nsrl_legacy
insert-timestamp	1679408381.6858978
source	RDS.db

Key	Value
FileSize	400836
MD5	3C1C4DCD4EDE4D8FFDF6B14B08DE432F
PackageDescription	simulator 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
PackageMaintainer	NeuroDebian Team <team@neuro.debian.net>
PackageName	python-brian
PackageSection	python
PackageVersion	1.4.1-2
SHA-1	580486173533B476C645D3E90E1F84CFD1A80425
SHA-256	56053F72B3B1FF04C3C33E430CA9DEFA103733112861809634E15307D6667145

Key	Value
FileSize	399634
MD5	5E9FB7A13F3C7B732BC90A41BF5FFA3D
PackageDescription	simulator 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
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python-brian
PackageSection	python
PackageVersion	1.4.1-2
SHA-1	23EDC17DD0E6B25AEB2C6E2917FFF1D6486EBFE9
SHA-256	2E44ADD3477999F05B0E3422B922CA57E7A0F12657A7EE9E432083046FC97812

Key	Value
FileSize	401766
MD5	AAB0825B5D80D29E54A7B43B3A4FD6D1
PackageDescription	simulator 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
PackageMaintainer	NeuroDebian Team <team@neuro.debian.net>
PackageName	python-brian
PackageSection	python
PackageVersion	1.4.3-1
SHA-1	88606468F7B3028C456C3F801EC4C31BCD852169
SHA-256	FF3EA610C10A2C1CBAC1DB69FBA7669B08343927BD9E3E394528B5478FADF8A1