def test_sigmoid_invalid(Simulator, max_rate, intercept): """Check that invalid sigmoid ensembles raise an error.""" with nengo.Network() as m: nengo.Ensemble( 1, 1, neuron_type=Sigmoid(), max_rates=[max_rate], intercepts=[intercept] ) with pytest.raises(BuildError): with Simulator(m): pass
def test_sigmoid_invalid(Simulator): """Check that invalid sigmoid ensembles raise an error.""" sigmoid = Sigmoid(tau_ref=0.5) with pytest.raises(ValidationError, match="Max rates must be below"): sigmoid.gain_bias(max_rates=np.array([100]), intercepts=np.array([0])) for max_rate, intercept in [(300.0, 1.1), (300.0, 1.0), (100.0, 0.9), (100, 1.0)]: with nengo.Network() as net: nengo.Ensemble( 1, 1, neuron_type=Sigmoid(), max_rates=[max_rate], intercepts=[intercept], ) with pytest.raises(BuildError, match="lead to neurons with negative"): with Simulator(net): pass
def test_sigmoid_response_curves(Simulator, max_rate, intercept, allclose): """Check the sigmoid response curve monotonically increases. The sigmoid response curve should work fine: - if max rate > rate at inflection point and intercept < 1 - if max rate < rate at inflection point and intercept > 1 """ with nengo.Network() as m: e = nengo.Ensemble( 1, 1, neuron_type=Sigmoid(), max_rates=[max_rate], intercepts=[intercept] ) with Simulator(m) as sim: pass _, y = nengo.utils.ensemble.response_curves(e, sim) assert allclose(np.max(y), max_rate) assert np.all(y > 0.0) assert np.all(np.diff(y) > 0.0) # monotonically increasing
def test_probeable(): def check_neuron_type(neuron_type, expected): assert neuron_type.probeable == expected ens = nengo.Ensemble(10, 1, neuron_type=neuron_type) assert ens.neurons.probeable == expected + ("input", ) with nengo.Network(): check_neuron_type(Direct(), ("output", )) check_neuron_type(RectifiedLinear(), ("output", )) check_neuron_type(SpikingRectifiedLinear(), ("output", "voltage")) check_neuron_type(Sigmoid(), ("output", )) check_neuron_type(Tanh(), ("output", )) check_neuron_type(LIFRate(), ("output", )) check_neuron_type(LIF(), ("output", "voltage", "refractory_time")) check_neuron_type(AdaptiveLIFRate(), ("output", "adaptation")) check_neuron_type( AdaptiveLIF(), ("output", "voltage", "refractory_time", "adaptation")) check_neuron_type(Izhikevich(), ("output", "voltage", "recovery")) check_neuron_type(RegularSpiking(LIFRate()), ("output", "rate_out", "voltage")) check_neuron_type(StochasticSpiking(AdaptiveLIFRate()), ("output", "rate_out", "adaptation")) check_neuron_type(PoissonSpiking(LIFRate()), ("output", "rate_out"))
def test_argreprs(): """Test repr() for each neuron type.""" def check_init_args(cls, args): assert getfullargspec(cls.__init__).args[1:] == args def check_repr(obj): assert eval(repr(obj)) == obj check_init_args(Direct, []) check_repr(Direct()) check_init_args(RectifiedLinear, ["amplitude"]) check_repr(RectifiedLinear()) check_repr(RectifiedLinear(amplitude=2)) check_init_args(SpikingRectifiedLinear, ["amplitude"]) check_repr(SpikingRectifiedLinear()) check_repr(SpikingRectifiedLinear(amplitude=2)) check_init_args(Sigmoid, ["tau_ref"]) check_repr(Sigmoid()) check_repr(Sigmoid(tau_ref=0.1)) check_init_args(LIFRate, ["tau_rc", "tau_ref", "amplitude"]) check_repr(LIFRate()) check_repr(LIFRate(tau_rc=0.1)) check_repr(LIFRate(tau_ref=0.1)) check_repr(LIFRate(amplitude=2)) check_repr(LIFRate(tau_rc=0.05, tau_ref=0.02)) check_repr(LIFRate(tau_rc=0.05, amplitude=2)) check_repr(LIFRate(tau_ref=0.02, amplitude=2)) check_repr(LIFRate(tau_rc=0.05, tau_ref=0.02, amplitude=2)) check_init_args(LIF, ["tau_rc", "tau_ref", "min_voltage", "amplitude"]) check_repr(LIF()) check_repr(LIF(tau_rc=0.1)) check_repr(LIF(tau_ref=0.1)) check_repr(LIF(amplitude=2)) check_repr(LIF(min_voltage=-0.5)) check_repr(LIF(tau_rc=0.05, tau_ref=0.02)) check_repr(LIF(tau_rc=0.05, amplitude=2)) check_repr(LIF(tau_ref=0.02, amplitude=2)) check_repr(LIF(tau_rc=0.05, tau_ref=0.02, amplitude=2)) check_repr(LIF(tau_rc=0.05, tau_ref=0.02, min_voltage=-0.5, amplitude=2)) check_init_args(AdaptiveLIFRate, ["tau_n", "inc_n", "tau_rc", "tau_ref", "amplitude"]) check_repr(AdaptiveLIFRate()) check_repr(AdaptiveLIFRate(tau_n=0.1)) check_repr(AdaptiveLIFRate(inc_n=0.5)) check_repr(AdaptiveLIFRate(tau_rc=0.1)) check_repr(AdaptiveLIFRate(tau_ref=0.1)) check_repr(AdaptiveLIFRate(amplitude=2)) check_repr( AdaptiveLIFRate(tau_n=0.1, inc_n=0.5, tau_rc=0.05, tau_ref=0.02, amplitude=2)) check_init_args( AdaptiveLIF, ["tau_n", "inc_n", "tau_rc", "tau_ref", "min_voltage", "amplitude"]) check_repr(AdaptiveLIF()) check_repr(AdaptiveLIF(tau_n=0.1)) check_repr(AdaptiveLIF(inc_n=0.5)) check_repr(AdaptiveLIF(tau_rc=0.1)) check_repr(AdaptiveLIF(tau_ref=0.1)) check_repr(AdaptiveLIF(min_voltage=-0.5)) check_repr( AdaptiveLIF( tau_n=0.1, inc_n=0.5, tau_rc=0.05, tau_ref=0.02, min_voltage=-0.5, amplitude=2, )) check_init_args( Izhikevich, ["tau_recovery", "coupling", "reset_voltage", "reset_recovery"]) check_repr(Izhikevich()) check_repr(Izhikevich(tau_recovery=0.1)) check_repr(Izhikevich(coupling=0.3)) check_repr(Izhikevich(reset_voltage=-1)) check_repr(Izhikevich(reset_recovery=5)) check_repr( Izhikevich(tau_recovery=0.1, coupling=0.3, reset_voltage=-1, reset_recovery=5))