The Operation of Memory Systems in the Brain - Computational Neuroscience: A Comprehensive Approach - page 517

Figure 16.13
Schematic diagram showing the organization of brain networks involved in learning
reinforcement associations of visual and auditory stimuli. The learning is imple-
mented by pattern association networks in the amygdala and orbitofrontal cortex.
The visual representation provided by the inferior temporal cortex is in an appropri-
ate form for this pattern association learning, in that information about objects can
be read from a population of IT neurons by dot-product neuronal operations.
trying to find a genetic way to switch off backprojections just for the projections of
mood systems back to perceptual systems (cf. [86]).
[87] (see also [75] and [77]) have developed a theory of how the effects of mood
on memory and perception could be implemented in the brain. The architecture,
shown in Figure 16.15, uses the massive backprojections from parts of the brain
where mood is represented, such as the orbitofrontal cortex and amygdala to the
cortical areas such as the inferior temporal visual cortex and hippocampus-related
areas (labelled IT in Figure 16.15) that project into these mood-representing areas
[2, 1]. The model uses an attractor in the mood module (labelled amygdala in Figure
16.15), which helps the mood to be an enduring state, and also an attractor in IT. The
system is treated as a system of coupled attractors (see [82]), but with an odd twist:
many different perceptual states are associated with any one mood state. Overall,
there is a large number of perceptual / memory states, and only a few mood states,
so that there is a many-to-one relation between perceptual / memory states and the
associated mood states. The network displays the properties that one would expect
(provided that the coupling parameters g between the attractors are weak). These