Inhibition is a phenomenon that occurs in different areas of the brain, including the visual cortex. For instance, the responses of some shape-selective neurons in the inferotemporal cortex are suppressed by the presence of certain shape contour parts in their receptive fields. This suppression phenomenon is thought to increase the selectivity of such neurons. We propose an inhibition-augmented model of shape-selective neurons, as an advancement of the trainable filter approach called combination of shifted filter responses (COSFIRE). We use a positive prototype pattern and a set of negative prototype patterns to automatically configure an inhibition-augmented model. The configuration involves the selection of responses of a bank of Gabor filters (models of V1/V2 neurons) that provide excitatory or inhibitory input(s). We compute the output of the model as the excitatory input minus a fraction of the maximum of the inhibitory inputs. The configured model responds to patterns that are similar to the positive prototype but does not respond to patterns similar to the negative prototype(s). We demonstrate the effectiveness of the proposed model in shape recognition. We use the Graphics Recognition (GREC2011) benchmark dataset and demonstrate that the proposed inhibition-augmented modeling technique increases selectivity of the COSFIRE model.