def test_decode_neuron_str():
assert str(DecodeNeurons(dt=0.005)) == "DecodeNeurons(dt=0.005)"
assert str(OnOffDecodeNeurons(pairs_per_dim=2, dt=0.002, rate=None)) == (
"OnOffDecodeNeurons(pairs_per_dim=2, dt=0.002, rate=250)")
assert str(NoisyDecodeNeurons(1, rate=20)) == (
"NoisyDecodeNeurons(pairs_per_dim=1, dt=0.001, rate=20, noise_exp=-2)")
assert str(
Preset5DecodeNeurons()) == ("Preset5DecodeNeurons(dt=0.001, rate=200)")
assert str(Preset10DecodeNeurons(
dt=0.0001, rate=0.5)) == ("Preset10DecodeNeurons(dt=0.0001, rate=0.5)")
def __init__(self, dt=0.001, label=None, builder=None):
self.dt = dt
self.label = label
self.builder = Builder() if builder is None else builder
self.build_callback = None
self.decoder_cache = NoDecoderCache()
# Objects created by the model for simulation on Loihi
self.inputs = OrderedDict()
self.blocks = OrderedDict()
# Will be filled in by the network builder
self.toplevel = None
self.config = None
# Resources used by the build process
self.objs = defaultdict(dict)
self.params = {} # Holds data generated when building objects
self.probes = []
self.probe_conns = {}
self.seeds = {}
self.seeded = {}
# --- other (typically standard) parameters
# Filter on decode neurons
self.decode_tau = 0.005
# ^TODO: how to choose this filter? Even though the input is spikes,
# it may not be absolutely necessary since tau_rc provides a filter,
# and maybe we don't want double filtering if connection has a filter
self.decode_neurons = Preset10DecodeNeurons(dt=dt)
self.node_neurons = OnOffDecodeNeurons(dt=dt)
# voltage threshold for non-spiking neurons (i.e. voltage decoders)
self.vth_nonspiking = 10
# limit for clipping intercepts, to avoid neurons with high gains
self.intercept_limit = 0.95
# scaling for PES errors, before rounding and clipping to -127..127
self.pes_error_scale = 100.
# learning weight exponent for PES (controls the maximum weight
# magnitude/weight resolution)
self.pes_wgt_exp = 4
# Will be provided by Simulator
self.chip2host_params = {}
DecodeNeurons,
NoisyDecodeNeurons,
OnOffDecodeNeurons,
Preset5DecodeNeurons,
Preset10DecodeNeurons,
)
@pytest.mark.parametrize(
"decode_neurons, tolerance",
[
(OnOffDecodeNeurons(), 0.35),
(NoisyDecodeNeurons(5), 0.12),
(NoisyDecodeNeurons(10), 0.11),
(Preset5DecodeNeurons(), 0.06),
(Preset10DecodeNeurons(), 0.03),
],
)
def test_add_inputs(decode_neurons, tolerance, Simulator, seed, plt):
sim_time = 2.0
pres_time = sim_time / 4
eval_time = sim_time / 8
stim_values = [[0.5, 0.5], [0.5, -0.9], [-0.7, -0.3], [-0.3, 1.0]]
stim_times = np.arange(0, sim_time, pres_time)
stim_fn_a = nengo.processes.Piecewise(
{t: stim_values[i][0]
for i, t in enumerate(stim_times)})
stim_fn_b = nengo.processes.Piecewise(
{t: stim_values[i][1]
for i, t in enumerate(stim_times)})
def __init__(self, dt=0.001, label=None, builder=None):
self.dt = dt
self.label = label
self.builder = Builder() if builder is None else builder
self.build_callback = None
self.decoder_cache = NoDecoderCache()
# TODO: these models may not look/behave exactly the same as
# standard nengo models, because they don't have a toplevel network
# built into them or configs set
self.host_pre = NengoModel(
dt=float(dt),
label="%s:host_pre, dt=%f" % (label, dt),
decoder_cache=NoDecoderCache(),
)
self.host = NengoModel(
dt=float(dt),
label="%s:host, dt=%f" % (label, dt),
decoder_cache=NoDecoderCache(),
)
# Objects created by the model for simulation on Loihi
self.inputs = OrderedDict()
self.blocks = OrderedDict()
self.block_shapes = {}
self.probes = []
# Will be filled in by the simulator __init__
self.split = None
# Will be filled in by the network builder
self.toplevel = None
self.config = None
# Resources used by the build process
self.objs = defaultdict(dict) # maps Nengo objects to Loihi objects
self.params = {} # maps Nengo objects to data generated during build
self.nengo_probes = [] # list of Nengo probes in the model
self.nengo_probe_conns = {}
self.seeds = {}
self.seeded = {}
# --- other (typically standard) parameters
# Filter on decode neurons
self.decode_tau = 0.005
# ^TODO: how to choose this filter? Even though the input is spikes,
# it may not be absolutely necessary since tau_rc provides a filter,
# and maybe we don't want double filtering if connection has a filter
self.decode_neurons = Preset10DecodeNeurons(dt=dt)
self.node_neurons = OnOffDecodeNeurons(dt=dt, is_input=True)
# voltage threshold for non-spiking neurons (i.e. voltage decoders)
self.vth_nonspiking = 10
# limit for clipping intercepts, to avoid neurons with high gains
self.intercept_limit = 0.95
# scaling for PES errors, before rounding and clipping to -127..127
self.pes_error_scale = 100.0
# learning weight exponent for PES (controls the maximum weight
# magnitude/weight resolution)
self.pes_wgt_exp = 4
# Used to track interactions between host models
self.chip2host_params = {}
self.chip2host_receivers = OrderedDict()
self.host2chip_senders = OrderedDict()
self.host2chip_pes_senders = OrderedDict()
self.needs_sender = {}