How many neurons can be fully connected?


How many neurons can I fully connect from one to another using Nengo Loihi?

At least for the v0.4.0 emulator, the answer seems to depend on whether I partition the ensemble into a bunch of sub-ensembles ($d$ ensembles, each containing $n$ neurons), even though the total number of neurons ($nd$) and total number of connections ($n^2d^2$), remains the same (every sub-ensemble is fully-connected to every sub-ensemble, including itself). In other words, it seems to depend on how the same number of virtual resources (neurons and connections) are being physically mapped.

     n  d   nd      ?
0  512  1  512  False
1  256  2  512  False
2  170  3  510   True
3  128  4  512   True
4  102  5  510   True
5   85  6  510   True
6   73  7  511   True

For example, in the above table, 4 ensembles of 128 neurons are okay, while 1 ensemble of 512 neurons are not. In both cases, there are 512 neurons and 512**2 connections.

Is there an equation that describes this in general? Is there a way to have nengo_loihi perform the optimal partitioning for a given ensemble or network configuration, or some helper functions for satisfying these constraints?

import warnings

from collections import defaultdict

import numpy as np
from pandas import DataFrame

import nengo
from nengo_loihi import Simulator
from nengo_loihi.builder import BuildError

def attempt(n, d):
    with nengo.Network(seed=0) as model:
        ensembles = [nengo.Ensemble(n, 1) for _ in range(d)]
        for ens1 in ensembles:
            for ens2 in ensembles:
                nengo.Connection(ens1, ens2, solver=nengo.solvers.LstsqL2(weights=True))
        with Simulator(model, progress_bar=None) as sim:
    except BuildError:
        return False
        return True
data = defaultdict(list)
nd = 512
for d in range(1, 8):
    n = nd // d
    data['?'].append(attempt(n, d))


Right now, the partitioning is very simple. We map one ensemble to one Loihi core. Each core has a fixed amount of memory, so when you split your neurons between more cores, then you have more synapse memory per neuron.

At some point this should absolutely be done better, but for now, it’s just this simple one-to-one mapping.


In case this can help anyone, I’ve coded up this VirtualEnsemble helper network that scales to thousands of recurrently connected neurons. However, due to some other issues posted on GitHub I don’t yet have an example that shows off the benefits. A feed-forward example is at the bottom. I will make edits to this post as the network / applications are improved.

import numpy as np

import nengo
from nengo.params import IntParam
from nengo.utils.builder import default_n_eval_points

import nengo_loihi
from nengo_loihi.builder import get_gain_bias, get_samples
from nengo_loihi.neurons import loihi_rates

class VirtualEnsemble(nengo.Network):
    """Virtualize a single ensemble using multiple sub-ensembles.
    The naming comes from an analogy to "virtual memory" in PCs.
    Since Loihi maps each ensemble to one core, large ensembles with
    dense connection matrices can easily consume all of the memory.
    A solution is to partition the ensemble across multiple cores,
    and then jointly optimize for decoders across all sub-ensembles.
    This network achieves this by configuring the tuning curves
    in advance and stacking the optimization problems together to
    connect up each output pre-build time. This provides an
    Ensemble-like interface, that can be connected into and decoded
    from, but is implemented using multiple sub-ensembles underneath.

     - document and test
     - add_output assumes function is a callable
     - label the ensembles, node, connections

    n_ensembles = IntParam('n_ensembles', low=1)
    def __init__(self, n_ensembles, n_neurons_per_ensemble,
                 intercept_limit=0.95, rng=np.random,
                 label=None, seed=None, add_to_container=None,

        super(VirtualEnsemble, self).__init__(
            label=label, seed=seed, add_to_container=add_to_container)
        for illegal in ('eval_points', 'n_eval_points'):
            if illegal in ens_kwargs:
                raise ValueError("Ensemble parameter '%s' is unsupported" % illegal)

        self._ensembles = []
        self.n_ensembles = n_ensembles
        self.n_neurons_per_ensemble = n_neurons_per_ensemble

        with self:
            for _ in range(n_ensembles):
                ens = nengo.Ensemble(n_neurons=n_neurons_per_ensemble, **ens_kwargs)

                gain, bias, max_rates, intercepts = get_gain_bias(
                    ens, rng=rng, intercept_limit=intercept_limit)

                ens.gain = gain
                ens.bias = bias
                ens.max_rates = max_rates
                ens.intercepts = intercepts

                ens.encoders = get_samples(
                    ens.encoders, ens.n_neurons, ens.dimensions, rng=rng)

        # last ensemble is representative of all others in terms of dimensions
        self.dimensions = ens.dimensions
    def add_input(self, pre, weights=True, **conn_kwargs):
        if weights:
            transform = np.asarray(conn_kwargs.get('transform', 1))
        with self:
            for post in self._ensembles:
                if weights:
                    conn_kwargs['transform'] =
                    post = post.neurons
                nengo.Connection(pre, post, **conn_kwargs)

    def add_neuron_output(self):
        with self:
            output = nengo.Node(size_in=self.n_neurons_per_ensemble * self.n_ensembles)
            for i, ens in enumerate(self._ensembles):
                nengo.Connection(ens.neurons, output[i*ens.n_neurons:(i+1)*ens.n_neurons],
        return output
    def add_output(self,
                   function=lambda x: x, 

        if not isinstance(eval_points, nengo.dists.Distribution):
            raise TypeError("eval_points (%r) must be a "
                            "nengo.dists.Distribution" % eval_points)
        n = self.n_ensembles * self.n_neurons_per_ensemble
        n_points = default_n_eval_points(n, self.dimensions)
        eval_points = eval_points.sample(n_points, self.dimensions, rng=rng)
        A = np.empty((n_points, n))
        Y = np.asarray([np.atleast_1d(function(ep)) for ep in eval_points])
        size_out = Y.shape[1]

        for i, ens in enumerate(self._ensembles):
            x =, ens.encoders.T / ens.radius)
            activities = loihi_rates(ens.neuron_type, x, ens.gain, ens.bias, dt)
            A[:, i*ens.n_neurons:(i+1)*ens.n_neurons] = activities

        D, info = solver(A, Y, rng=rng)  # AD ~ Y
        assert D.shape == (n, size_out)

        with self:
            output = nengo.Node(size_in=size_out)
            for i, ens in enumerate(self._ensembles):
                # NoSolver work-around for Neurons -> Ensemble
                # nengo.Connection(
                #     ens, output, synapse=None,
                #     solver=nengo.solvers.NoSolver(
                #         D[i*ens.n_neurons:(i+1)*ens.n_neurons, :],
                #         weights=False))
                # TODO: investigate weird behaviour having something to do
                #   with the function not being respected when the
                #   add_output weights are embedded in NoSolver to form
                #   a recurrent passthrough
                    ens.neurons, output, synapse=None,
                    transform=D[i*ens.n_neurons:(i+1)*ens.n_neurons, :].T)

        return output, info
import matplotlib.pyplot as plt
import seaborn as sns
from nengo.utils.matplotlib import rasterplot

with nengo.Network() as model:
    u = nengo.Node(output=lambda t: np.sin(2*np.pi*t))
    x = VirtualEnsemble(
        n_ensembles=30, n_neurons_per_ensemble=100, dimensions=1)
    x.add_input(u, synapse=None)
    x_hat, info = x.add_output()
    p_x = nengo.Probe(x_hat, synapse=0.05)
    p_a = nengo.Probe(x.add_neuron_output(), synapse=None)

with nengo_loihi.Simulator(model) as sim:

print("Decoder solver info:", info)

fig, ax = plt.subplots(2, 1, sharex=True, figsize=(12, 18),
                       gridspec_kw={'height_ratios': [1, 3]})
ax[0].set_title("Sinusoidal Communication Channel")

A =[p_a]
t_slice = (sim.trange() > 0.4) & (sim.trange() < 0.55)
I = np.argsort(np.sum(A[t_slice], axis=0))

ax[1].set_title("Spike Raster")
rasterplot(sim.trange(), A[:, I], ax=ax[1])
ax[1].set_ylabel("Neuron #")
ax[1].set_xlabel("Time (s)")

Decoder solver info: {'rmses': array([0.00066519]), 'time': 0.4871366024017334}