The neuron illustrated was chosen to have the highest single cell stimulus-specific information about object 4 that could be decoded from the responses of a neuron to the 9 exemplars of object 4 (as well as a high firing rate).
The same point applies to many other computer-based object recognition systems, including deep convolutional neural networks, namely that they have no way of learning transform invariances from systematically transformed sets of training exemplars of objects.
In the category-match search displays, a second exemplar of the object was a distractor.
Another way that the output of HMAX may be assessed is by the use of view-tuned units (VTUs), each of which is set to respond to one view of a class or object by setting its synaptic weights from each C unit to the value of the firing of the C unit to one view or exemplar of the object or class (Serre et al. 2007a).
Experiment 2 shows that VisNet produces single neurons with sparse representations, in which a single neuron can respond to many exemplars of one object and to no exemplars of many other objects (Figs. 8, 9).
The relatively poor performance of VisNet (which produces a biologically plausible output), and of HMAX when its performance is measured in a biologically plausible way, raises the point that training with a diverse sets of exemplars of an object class as in the Caltech dataset is not a very useful way to test object recognition networks of the type found in the brain.
The reason for this is that the exemplars of each object class in the CalTech-256 dataset are too discontinuous to provide a basis for learning transform-invariant object representations.
The objects presented at test could be the same exemplar as at study (physically identical picture), a different exemplar of the same object category, or a new, non-studied object.
Fourteen schizophrenia patients and 10 healthy control subjects participated in an fMRI experiment to determine the functional status of the FFA by viewing a series of faces and exemplars of other object categories, while completing a low-level task designed to verify their engagement with the stimuli.
In some of their experiments, the distractor was either the memorized object (e.g., picture of a cat) or another exemplar of the memorized object (e.g., picture of another cat).
