olftrans.cpu package

Submodules

olftrans.cpu.model module

CPU Models

class olftrans.cpu.model.Model(num=1, floatType=<class 'numpy.float64'>, **states_or_params)

Bases: object

Params = {}

States = {}

Time_Scale = 1.0

abstract gradient(**inputs)

Update function of the ODE dx/dt = f(x)

Computes gradient of all states, returned as dictionary

non_gradient()

Non-gradient Update

record(steps, states)

update(dt, **inputs)

class olftrans.cpu.model.NoisyConnorStevens(num=1, floatType=<class 'numpy.float64'>, **states_or_params)

Bases: olftrans.cpu.model.Model

Params = {'EK': -72.0, 'EL': -17.0, 'ENa': 55.0, 'Ea': -75.0, 'gK': 20.0, 'gL': 0.3, 'gNa': 120.0, 'ga': 47.7, 'hs': -12.0, 'ms': -5.3, 'ns': -4.3, 'refperiod': 1.0, 'sigma': 2.05}

States = {'a': (1.0, 0.0, 1.0), 'b': (1.0, 0.0, 1.0), 'h': (1.0, 0.0, 1.0), 'm': (0.0, 0.0, 1.0), 'n': (0.0, 0.0, 1.0), 'refactory': 0.0, 'spike': 0.0, 'v': (-60, -80, 80), 'v1': -60.0, 'v2': -60.0}

Time_Scale = 1000.0

gradient(stimulus=0.0)

Update function of the ODE dx/dt = f(x)

Computes gradient of all states, returned as dictionary

non_gradient()

Non-gradient Update

class olftrans.cpu.model.OTP(num=1, floatType=<class 'numpy.float64'>, **states_or_params)

Bases: olftrans.cpu.model.Model

Params = {'Imax': 62.13, 'a1': 45.0, 'a2': 146.1, 'a3': 2.539, 'b1': 0.8, 'b2': 117.2, 'b3': 0.9096, 'br': 1.0, 'c': 0.06546, 'dr': 1.0, 'gamma': 0.215, 'kappa': 8841, 'p': 1.0}

States = {'I': 0.0, 'duh': 0.0, 'uh': (0.0, 0.0, 50000.0), 'v': (0.0, 0, 1000000000.0), 'x1': (0.0, 0.0, 1.0), 'x2': (0.0, 0.0, 1.0), 'x3': (0.0, 0.0, 1000.0)}

gradient(stimulus=0.0)

Update function of the ODE dx/dt = f(x)

Computes gradient of all states, returned as dictionary

non_gradient()

Non-gradient Update

olftrans.cpu.model.compute_fi(NeuronModel, Is, repeat=1, input_var='stimulus', spike_var='spike', voltage_var='v', dur=2.0, start=0.5, dt=1e-05, verbose=True, neuron_params=None)

Return type

Tuple[Tuple[numpy.ndarray, numpy.ndarray], olftrans.cpu.model.Model]

Module contents