The issue of whether objects and concepts are represented in the brain by single neurons or multiple ones, where the multiple ones are conceived to represent subconcepts or microfeatures, has plagued brain-related sciences for decades, spawning different scientific fields such as artificial intelligence (AI) and connectionism. It is also a source of dispute within some of these scientific fields. In connectionism, for example, there is never ending debate between the theories of localist (in a sense symbolic) and distributed representation. To resolve this conflict, we analyze a highly publicized class of models used by connectionists (distributed representation theorists) for complex cognitive processes and show that, contrary to their claim, they actually depend on localist (symbolic) representation of higher-level concepts in these models. We also find that these connectionist models use processes similar to symbolic computation. Based on this analysis and the accumulating evidence from single-unit recordings in neurophysiology that shows that single cells can indeed encode information about single objects (e.g. a Jennifer Aniston cell in our brains), we propose the theory that the brain uses both forms of representation, localist and distributed, and that both forms may be necessary, depending on the context. Our other conjecture is that the brain uses both forms of computation, symbolic and distributed (parallel). This theory should finally resolve the decades long conflict about representation and computational processes that has generated divisions within our fields and has stalled our progress towards creating brain-like learning systems.