There is long history of studies documenting that some
neurons respond to images of objects, faces, and scenes in a
highly selective manner. This includes neurons in the
human hippocampus (e.g., the famous example of a neuron
responding to images of the actress Jennifer Aniston) and
neurons in high-level visual cortex in monkey (for reviews
see Bowers, 2009; Ison, Quian Quiroga, & Fried, 2015).
These findings have led to a growing interest in the claim
that some neurons code for information in a localist
(‘grandmother cell’) manner, as reflected in the many
contributions to a recent special issue on this topic in the
journal Language, Cognition, & Neuroscience (Bowers,
2017).
By contrast, it is only recently that interest in
characterizing the selectivity of single units in connectionist
networks has gathered speed. Critically, these studies also
show that networks learn highly selective representations
under a number of conditions, as detailed below. In this talk
I will summarize recent research in my lab that explores the
conditions in which artificial networks learn selective codes,
and research comparing the responses of selective neurons
and localist representations used in cognitive models. These
findings suggest when and why some neurons in cortex
respond in a highly selective manner, and highlight the
biological plausibility of localist models in psychology.