A model for structured information representation in neural networks of
Humans can reason at an abstract level and structure information into abstract
categories, but the underlying neural processes have remained unknown. Recent
experimental data provide the hint that this is likely to involve specific
subareas of the brain from which structural information can be decoded. Based
on this data, we introduce the concept of assembly projections, a general
principle for attaching structural information to content in generic networks
of spiking neurons. According to the assembly projections principle,
structure-encoding assemblies emerge and are dynamically attached to content
representations through Hebbian plasticity mechanism. This model provides
the basis for explaining a number of experimental data and provides a basis
for modeling abstract computational operations of the brain.
Reference: M. G. Müller, C. H. Papadimitriou, W. Maass, and
A model for structured information representation in neural networks of the
eNeuro, 7(3), 2020.