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License: GNU General Public License v3.0
This project forked from inm-6/neural_network_meanfield
License: GNU General Public License v3.0
Package calculating stationary and dynamical properties of networks composed of leaky integrate-and-fire neurons connected with fast and slow exponentially decaying synapses. ========================================================================= Dependencies ------------ libmathlib2-gfortran libmathlib2-dev -> available via sudo apt-get install h5py_wrapper -> available on https://github.com/INM-6/h5py_wrapper Documentation ------------- The main class is Circuit() in circuit.py. Depending on the chosen analysis_type (None, 'stationary', 'dynamical'), an instantiation of Circuit() offers function to calculate the stationary and dynamical properties of the circuit. For example: - firing rates (Brunel & Hakim 1999, Fourcoud & Brunel 2002, Moreno-Bote, R. & Parga, N. (2010)) - transfer functions (Schuecker 2015) - power spectra and their anatomical origin (Bos 2015, Schuecker 2015) By default the analysis_type is set to 'dynamical' and the transfer function is calculated for the all frequencies which might be time consuming. The paramters of the circuit are specified in params_circuit.py. All parameters including the analysis_type can be altered after the circuit has been initialised using the function alter_default_params(). References ---------- - Brunel N, Hakim V (1999) Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural Comput. 11:1621-1671. - Fourcaud N, Brunel N (2002) Dynamics of the firing probability of noisy integrate-and-fire neurons. Neural Comput. 14:2057โ2110. - Moreno-Bote, R. & Parga, N. (2010). Response of integrate-and-fire neurons to noisy inputs filtered by synapses with arbitrary timescales: Firing rate and correlations. Neural Comput., 22(6), 1528-1572. - Schuecker J, Diesmann M, Helias M. Modulated escape from a metastable state driven by colored noise. Phys Rev E. 2015 Nov;92:052119. Available from: http://link.aps.org/doi/10.1103/PhysRevE.92.052119. - Bos H, Diesmann M, Helias M (2015) Identifying anatomical origins of coexisting oscillations in the cortical microcircuit arXiv:1510.00642 [q-bio.NC] Examples -------- # Calculation of firing rates import circuit circ = circuit.Circuit('microcircuit', analysis_type='stationary') print 'firing rates', circ.th_rates # Calculation of population rate spectra import matplotlib.pyplot as plt import numpy as np import circuit dic = {'dsd': 1.0, 'delay_dist': 'truncated_gaussian'} circ = circuit.Circuit('microcircuit', dic) freqs, power = circ.create_power_spectra() plt.figure() for i in range(8): plt.plot(freqs, np.sqrt(power[i]), label=circ.populations[i]) plt.yscale('log') plt.legend() plt.show()
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