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# Connections in depth¶

The Connection object encapsulates different behaviors depending on the attributes of the connection. It can be helpful in debugging network behavior to know what is happening under the hood for different types of connections.

The biggest determiner of what happens in a connection is the pre object. When the pre object is an Ensemble with a neuron type other than Direct, Nengo will create a decoded connection. When the pre object is anything else, Nengo will create a direct connection.

The post object is only used to determine which signal will receive the data produced by the connection. If you’re not sure what your connection is doing, interrogate the pre object first.

## Decoded connections¶

Decoded connections are any connection from an ensemble to any other object. The following are all examples of decoded connections:

with nengo.Network() as net:
ens1 = nengo.Ensemble(10, dimensions=2)
node = nengo.Node(size_in=1)
ens2 = nengo.Ensemble(4, dimensions=2)

# Ensemble to ensemble
nengo.Connection(ens1, ens2)
# Ensemble slice to node
nengo.Connection(ens1[0], node)
# Ensemble to neurons slice
nengo.Connection(ens1, ens2.neurons[:2])


The important thing about decoded connections is that they do not directly compute the function defined for that connection (keeping in mind that passing in no function is equivalent to passing in the identity function). Instead, the function is approximated by solving for a set of decoding weights. The output of a decoded connection is the sum of the pre ensemble’s neural activity weighted by the decoding weights solved for in the build process.

Mathematically, you can think of a decoded connection as implementing the following equation:

$\mathbf{y}(t) = \sum_{i=0}^n \mathbf{d}^{f}_i a_i(x(t))$

where

• $$\mathbf{y}(t)$$ is the output of the connection at time $$t$$,

• $$n$$ is the number of neurons in the pre ensemble

• $$\mathbf{d}^{f}_i$$ is the decoding weight associated with neuron $$i$$ given the function $$f$$,

• $$a_i(x(t))$$ is the activity of neuron $$i$$ given $$x(t)$$, the input at time $$t$$.

Note that the length of the $$\mathbf{d}$$ and $$\mathbf{y}$$ vectors is the same, and is specified by the dimensionality of the output of the function $$f$$ when applied to input $$x$$.

While the equation above is straightforward, there are several important implications that one should keep in mind when using decoded connections.

• Decoders will be automatically solved for in the build process.

Solving for decoders makes up the majority of build time, so if building your networks takes a long time, look at your decoded connections and try lowering the number of neurons or using different Solver types.

• The function passed to the connection is used to determine decoders. It will never be run during the simulation.

When you define a function in a node, it will be execute on every simulation timestep. That may lead you to think that the function passed to a connection is executed on every timestep, but that is not the case for decoded connections. The function passed to the connection will be executed in the decoder solving process determine an error to minimize, but never during the simulation.

• The characteristics of the pre ensemble are critically important to performance.

If you determine that your decoded connection is not approximating the desired function well, examine the pre ensemble. The decoded value depends on the activity of the pre ensemble; does it represent its input reliably? If not, then a function of that input cannot be well approximated. If you think that your function may be incorrect, switch the pre ensemble to use the Direct neuron type, which does not use decoders. If that function looks correct, move on to a simpler neuron type like RectifiedLinear until you can determine why your function is not being approximated well.

Concrete examples of how the properties of pre ensemble influence the desired function can be found in 1, 2.

## Direct connections¶

Any connection that is not a decoded connection is a direct connection.

For simplicity and consistency, Nengo exposes the same interface for decoded and direct connections. In all cases, data from the pre object is sent to the post object, with an optional synapse filter. In decoded connections, weights are automatically determined through decoder solving. In direct connections, weights can be manually specified through the transform argument. 3

The most common example of a direct connection is a neuron-to-neuron connection. These connections are the types of connections you see in most neural simulators, and can be used to reproduce networks written in other simulators like Brian:

with nengo.Network() as net:
ens1 = nengo.Ensemble(10, dimensions=1)
ens2 = nengo.Ensemble(20, dimensions=2)

# Neuron to neuron
weights = np.random.normal(size=(ens2.n_neurons, ens1.n_neurons))
nengo.Connection(ens1.neurons, ens2.neurons, transform=weights)


Note that it does not matter that the dimensionality of ens1 does not match the dimensionality of ens2. It only matters that the transform is of the correct shape, which in the case of neuron-to-neuron connections is (post.n_neurons, pre.n_neurons).

In the vast majority of cases, the above description is all you need to know. Below we give some additional examples, focusing on situations that differ from the description above.

### Nodes and Direct ensembles¶

In connections from nodes and ensembles using the Direct neuron type, the function argument is valid and will result in the function being applied to the input on every timestep. This is in direct contrast to decoded connections, in which the function is executed during the build process and not during the simulation.

Examples:

with nengo.Network() as net:
node = nengo.Node(output=[1])
ens1 = nengo.Ensemble(1, dimensions=2, neuron_type=nengo.Direct())
ens2 = nengo.Ensemble(10, dimensions=1)

# Node to LIF ensemble
nengo.Connection(node, ens2, function=lambda x: x**2)
# Direct ensemble to LIF ensemble
nengo.Connection(ens1, ens2, function=lambda x: x[0] * x[1])


### Passthrough nodes¶

When creating large networks, it is often helpful to use passthrough nodes to route signals from place to place without introducing unnecessary ensembles. For example, the EnsembleArray network is often used to represent a high-dimensional vector with many lower-dimensional ensemble. The high-dimensional vector is still available as EnsembleArray.output through the use of a passthrough node that collects the output of all the lower-dimensional ensembles.

Unlike other types of nodes, we explicitly disable the function argument when connecting from passthrough nodes. The reason for this is to ensure that users know they are making a direct connection and not a decoded connection. The output of a network like EnsembleArray can usually be treated the same way as the output of an Ensemble, except for the case of applying a function to the output, since decoders are not used to approximate the function in the case of networks using passthrough nodes.

As an example, consider using an EnsembleArray to compute a product:

with nengo.Network() as net:
ea = nengo.networks.EnsembleArray(40, 2)
product = nengo.Ensemble(30, dimensions=1)

# Passthrough node to ensemble -- raises error
nengo.Connection(ea.output, product, function=lambda x: x[0] * x[1])


If this example did not raise an error, the product would be computed nearly perfectly, despite the fact that that computation is impossible to decode from the ensembles of the ensemble array. Consider that the product requires information from both dimensions of the signal (i.e., the dimensions interact nonlinearly). In order for nonlinearities to be decoded, some neurons must encode information from the nonlinearly-interacting dimensions. Since the ensemble array represents each dimension independently, no neurons will encode information from multiple dimensions, and therefore the product cannot be approximated by the ensemble array.

If you are aware that the function will not be approximated but directly computed, and you desire this behavior, you can enable it by modifying the node so that it is no longer a passthrough node, but instead computes the identity function:

with nengo.Network() as net:
ea = nengo.networks.EnsembleArray(40, 2)
product = nengo.Ensemble(30, dimensions=1)

# Make the node non-passthrough
ea.output.output = lambda t, x: x
# Node to ensemble -- no error
nengo.Connection(ea.output, product, function=lambda x: x[0] * x[1])


If you’re designing networks that may have arbitrary function applied to the output, you should implement a way to make decoded connections from the ensembles in your network. See the EnsembleArray.add_output method for an example of how that might be implemented.

### Neuron-to-ensemble connections¶

As noted above, a decoded connection is implemented by solving for a set of decoding weights and then weighting a sum of activities by those decoders. If you already know the decoding weights you want to use on a connection, then you can skip the decoder solving step by using a direct connection from the neurons of an ensemble to another object.

In the example below, we make two equivalent connections, one using a decoded connection and one using a direct connection:

with nengo.Network() as net:
ens1 = nengo.Ensemble(20, dimensions=1, seed=0)
ens2 = nengo.Ensemble(15, dimensions=1)

# Decoded ensemble to ensemble connection
conn1 = nengo.Connection(ens1, ens2, function=lambda x: x + 0.5)

with nengo.Simulator(net) as sim:
decoders = sim.data[conn1].weights

with net:
# Direct neurons to ensemble connection
conn2 = nengo.Connection(ens1.neurons, ens2, transform=decoders)


In the above example, the shape of decoders is (1, 20). If you run this example and probe the output of conn1 and conn2, you will see that their output is the same (as long as a seed is set on ens1):

with net:
probe1 = nengo.Probe(conn1, "output", synapse=0.01)
probe2 = nengo.Probe(conn2, "output", synapse=0.01)

with nengo.Simulator(net) as sim:
sim.run(0.1)

assert np.allclose(sim.data[probe1], sim.data[probe2])


Both conn1 and conn2 can have learning rules applied, so this type of direct connection can be useful when saving the weights in a learning network and loading it up in the future.

1

Gosmann, Jan. Precise multiplications with the NEF. Waterloo, Ontario, Canada: University of Waterloo; 2015. Available from: https://zenodo.org/record/35680

2

Gosmann, Jan, and Chris Eliasmith. “Optimizing Semantic Pointer Representations for Symbol-Like Processing in Spiking Neural Networks.” PLoS ONE 11, no. 2 (February 22, 2016): e0149928. doi:10.1371/journal.pone.0149928.

3

Note that decoded connections also accept the transform argument. In the case of decoded connections, the transform is a linear operation that is applied after the function is applied to the input. In most cases, slicing the input or including the transform in the function is recommended.