Adding noise to Topographica

Most of the default settings in Topographica give precise, repeatable, noise-free results, in order to make the behavior predictable and understandable. However, there are many ways to increase realism by adding noise, a few of which are outlined below.

Additive and multiplicative noise in output_fns

One easy way to add noise in Topographica is by using a TransferFn. A TransferFn is simply a function that maps an array into another array of the same size and shape. At many locations in the Topographica code, a parameter named output_fns is provided to allow the user to put in any desired functions of this type. For instance, the output_fns of a Sheet (e.g. the Retina or V1) constitute its activation or transfer function. The output_fns of a Projection (e.g. Afferent or LateralExcitatory) are applied to the activity in that Projection after it has been computed, and thus are also transfer functions.

Multiple TransferFns can be placed in Topographica output_fns, and will be applied sequentially. For instance, LISSOM V1 Sheets typically have one PiecewiseLinear() TransferFn in the list of output_fns; noise can be added to this by appending to output_fns a TransferFn that adds noise. Others that have an empty list of output_fns by default can again just append the new output_function to the list. Suitable transfer functions for noise include variants of:


where the offset and scale parameters determine the mean value and the range of the variation, respectively, the operator determines whether the noise is multiplicative, divisive, or some other type of combination, and the noise itself can either be Uniform or Gaussian (i.e., normal), or even some some user-defined distribution. There are also some other noise-related TransferFns available, such as ProportionalGaussian (variance proportional to the mean).

Hints: For additive UniformRandom noise, if you are modelling non-zero background levels of activity in a Sheet or Projection, you can use an offset of zero and a scale that is the level of noise you want. In other cases where you want to avoid changing the average activity levels, you can get zero-mean UniformRandom additive noise by making the offset be a negative number that is half of the scale. To keep the average activity levels constant with multiplicative noise requires an offset of 1.0-scale/2.0; the scale then determines the noise level. You can of course combine both types of noise in succession, in which case you will typically want to do the multiplicative noise first, to avoid scaling the additive noise. Note that GaussianRandom noise is zero mean already; the difference compared to UniformRandom is for historical reasons.

As an example, to inject zero-mean additive uniform random noise into the LateralExcitatory Projection of a LISSOM-based model, just change e.g.


(if output_fns are specified) to e.g.:


Some networks may not state “output_fns=[]” explicitly, in which case just add the entire string above to the definition of that projection in the .ty file. To see the results immediately, just run the network for one step, then visualize the Projection Activity and the final Activity. The long-term effects of this noise can then be evaluated by running for longer periods.

Projection mapping jitter

Another important way to add variability is to add jitter when the initial mapping between sheets is set up, i.e. to disturb the topographic mapping of a CFProjection’s Connection Fields from the input sheet. The mapping of a CFProjection is controlled by a parameter “coord_mapper”, which by default does a perfect topographic mapping that makes analysis easier, but is too ideal to be realistic. Instead, you can specify a jittered mapping for the CFProjection, by adding:

coord_mapper = coordmapper.Jitter(gen=numbergen.UniformRandom(seed=1023),scale=0.2)

to the topo.sim.connect command, to offset the values by a random amount in the range plus or minus 0.1 Sheet coordinates around the precisely topographic mapped value. The results can be visualized by plotting the Projection and enabling “Situate”. Once set up, the jitter of the CF boundaries will always be present, but the weights inside the boundaries may reorganize to remove the effect of the jittering.

Note that the seed value allows you to control which specific pattern of jitter is used, e.g. if you want two different Projections of the same shape to have the same specific jittered values (e.g. for matching ON and OFF projections). Different seeds will allow the projections to be jittered independently of each other.

Also note that the coord_mapper varies the incoming connection field location. Because of how connection fields are currently implemented, it is much more difficult to vary the outgoing connection field location. In the case of the LGN->V1 projection, one can instead add jitter to the Retina->LGN projection, which effectively varies the outgoing connection field of the LGN.

ConnectionField shape noise

Another type of noise is differences in the connection field shapes between neurons in the same projection. Most of the example .ty files specify simple circular weights outlines, and to save memory by default all CFs in a projection share the same weight outline. If you want to try using noisy outlines where only some values within the circle have any effect, first set CFProjection.same_cf_shape_for_all_cfs=False, then set CFProjection.cf_shape to a PatternGenerator that returns different results each time it is evaluated. By default, dynamic parameters in Topographica only advance once per simulation time, so it is next necessary to call CFProjection.cf_shape.set_dynamic_time_fn(None), causing the PatternGenerator’s dynamic values to change each time it is evaluated. Note that one further command may be necessary in some cases: by default, the PatternGenerator used for cf_shape has its size set automatically; if instead you want to control the size, set CFProjection.autosize_mask to be False.

Weight adjustment noise

One could imagine the process of adjusting weights to be a stochastic or quantized process, either of which would give some variability to the process of updating weight values. For instance, this could be modeled with additive or multiplicative noise before or after any weight normalization.

Spatially correlated noise

The examples above focus on types of noise that are spatially uncorrelated, i.e. where the noise for each unit or weight or Connection Field is chosen independently of all others of that type. Many kinds of “noise” in biological systems will have spatial correlations, e.g. due to some underlying source that has a spatial extent (such as the vasculature, some diffusible chemical, etc.). To include such effects for the noise sources described above (output_fns), you could add new classes in topo.pattern.random that generate spatially correlated noise rather than noise that is independent per pixel. E.g., the noise matrix could be convolved with a small blurring kernel before it is added or multiplied with the activity, or the noise matrix could be low-pass filtered, e.g. to create the 1/f noise (pink noise) that is common in physical systems.

Measurement noise

One could also consider the effects of measurement noise, e.g. on computing preference maps, which could be done by temporarily modifying the output_fns of each sheet so that what is measured is no longer the actual activity value, but a transformation of it. A better approach would probably be to add an output_fns parameter to the commands for measuring maps, so that such functions could be supplied for any measurement.