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STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds
Fig 6
Illustration of the emergence of inhibitory subfields.
A: Feedforward mapping from an input 
to two neural units s1 and s2. The mapping is defined by two receptive fields with only positive entries. In this case, any strong activation of unit s2 does not negatively effect unit s1. For overlapping positive subfields, a stronger activation of s2 will even result in a stronger activation of s1 as well. B: Activations of neural units s1 and s2 according to a statistical model with non-negative generative fields (GFs). Both units compete to explain a presented input 
. A high probability for s2 decreases the probability of s1 and vica versa. This effect is known as “explaining away”, and it depends on the assumed model including the model for the combination of primitives, noise model, and prior. C: Illustration of an optimal feedforward mapping to approximate neural responses according to the statistical model in B. The stronger mutual suppression caused by explaining away is approximated by the introduction of inhibitory subfields. If the input is, e.g., now made stronger or less diffuse, then unit s2 can increase while unit s1 can simultaneously decrease, which is in accordance with probabilistic inference for a statistical model. D: Example of STRFs estimated from artificial data. The top row shows non-negative GFs. If the corresponding STRFs are now estimated using Eq 10, then negative subfields emerge (bottom row). For fields which do compete little with other fields (e.g., field three) the effect is the weakest. The strongest effects are observed for fields with large overlap (e.g. fields four and six). In general, explaining away effects increase with overcompleteness, i.e., with the number of GFs compared to input size. Color scales for all subfigures as in Fig 2A.
doi: https://doi.org/10.1371/journal.pcbi.1006595.g006