Topic: synaptic weight changes

In a standard model, associative memories are represented by assemblies of strongly interconnected neurons. It has recently been proposed that these assemblies are not static but drift freely in neural circuits. This explains experimental findings of changing memory representations. On the level of single neurons, assembly drift is reflected by characteristic dynamics: relatively long times of stable assembly membership interspersed with fast transitions. How can we mechanistically understand these dynamics? Here we answer this question by proposing simplified, reduced models. We first constru

Observations that have received little attention indicate that complexity is important for flexibility and adaptability of natural behavior and the remarkably efficient learning of the brain. The term complexity implies meaningful structural richness, which exists in systems that offer a plurality of possible solutions. The existence of multiple solutions in the neuronal network enables the brain to interpret a diverse set of sensory inputs and can allow it to operate and respond flexibly across a wide array of contexts. Hence it is important to study factors that can enable network complexity