  This paper presents a highly speculative model encompassing the cortex,
thalamus, and hippocampus of the mammalian brain. While the majority of
computational neuroscience models are founded upon empirical evidence, this
model is predicated upon a hardware proposal for a machine learning
accelerator. Such a device was designed to perform a specific task, such as
speech recognition. The design process employed the principles and techniques
typically used by computer architects in the design of devices such as
processors. However, it also sought to maintain plausibility with biological
systems in accordance with the current understanding of the mammalian brain. In
the course of our research, we have identified a functional framework that may
help to fill the gaps in current neuroscience, thereby facilitating the
explanations for many elusive cognitive-level effects. This paper does not
describe the device itself or the rationale behind the design decision, but
instead, it presents a concise description of the derived model. In brief, the
model provides a functional definition of the cortical column and its
structural definition by the minicolumns. It also offers a descriptive model
for the corticothalamic and corticostriatal loops, a functional proposal for
the hippocampal complex, and a simplified view of the brainstem circuitry
involved in auditory processing. The proposed model appears to provide an
explanation for a number of cognitive phenomena, including some ERP effects,
bottom-up and top-down attention, and the relationship between phenomena such
as the cocktail party effect, anterograde and retrograde amnesia following
hippocampal complex damage, and so forth.
