Library — NeuroLab v0.0.9 documentation. Ecet.ecs.ru.acad.bg/cst/docs/proceedings/s2/ii-27.pdf. Java Bidirectional Associative Memory Neural Network. Emergent. Comparison of Neural Network Simulators - Emergent. Neural Ensemble :: Home. NeuroConstruct. Brian. PyNN - Trac. PyNN (pronounced 'pine') is a is a simulator-independent language for building neuronal network models. In other words, you can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON , NEST , PCSIM and Brian ). The PyNN API aims to support modelling at a high-level of abstraction (populations of neurons, layers, columns and the connections between them) while still allowing access to the details of individual neurons and synapses when required. PyNN provides a library of standard neuron, synapse and synaptic plasticity models, which have been verified to work the same on the different supported simulators.
The low-level API is good for small networks, and perhaps gives more flexibility. The other thing that is required to write a model once and run it on multiple simulators is standard cell models . Licence The code is released under the CeCILL licence . Users' Guide. PyBrain. Neural Modeling with Python (Part 3) So far we've looked at how to simulate a simple LIF model neuron and a complex Hodgkin-Huxley model neuron.
The LIF neuron is computationally simple but physiologically implausible, while Hodgkin-Huxley gives us a very good representation of actual neural dynamics but is parameter-heavy and computationally expensive. An intriguing compromise between the two exists -- one that can generate a wide variety of observed neural spiking behavior while doing so with limited computational demand. It is called the quadratic integrate-and-fire model neuron, or simply Izhikevich neuron. Izhikevich Model Neuron In 2003, Eugene Izhikevich published a paper entitled "Simple Model of Spiking Neurons" (Izhikevich, 2003) in which he describes how the quadratic integrate-and-fire neuron can be used to efficiently replicate the observed spiking dynamics of several different classes of cortical neurons. . , with a reset similar to the LIF neuron defined by . Object-Oriented Python The IzhNeuron class sims = []
Brian/StimulusArrayGroup - NeuralEnsemble Cookbook - Trac. This code is for creating a group of neurons which fire with a given 2D stimulus at a given rate. You initialise it with arguments: stimulus, which should be a 2D array with values between 0 and 1. rate, which should be a firing rate in Hz. onset, the time that the stimulus should become active. duration, the length of time for which the stimulus should be active.
The point in the stimulus array at position (y,x) will correspond to the neuron with index i=y*width+x. This neuron will fire Poisson spikes at rate*stimulus[y,x] Hz. Code snippet The StimulusArrayGroup class is defined as follows: class StimulusArrayGroup(PoissonGroup): def __init__(self, stimulus, rate, onset, duration): height, width = stimulus.shape stim = stimulus.ravel()*rate self.stimulus = stim def stimfunc(t): if onset<t<(onset+duration): return stim else: return 0. An example use, where the stimulus is a 100x100 image with a 10 pixel thick bar at 90 degrees to the vertical, firing at rate 50 Hz from 100ms to 200ms: