  Actin is a globular protein which forms long polar filaments in eukaryotic.
The actin filaments play roles of cytoskeleton, motility units , information
processing and learning. We model actin filament as a double chain of finite
state machines, nodes, which take states `0' and `1'. The states are
abstractions of absence and presence of a sub-threshold charge on an actin
units corresponding to the nodes. All nodes update their state in parallel in
discrete time. A node updates its current state depending on states of two
closest neighbours in the node chain and two closest neighbours in the
complementary chain. Previous models of actin automata considered momentary
state transitions of nodes. We enrich the actin automata model by assuming that
states of nodes depends not only on the current states of neighbouring node but
also on their past states. Thus, we assess the effect of memory of past states
on the dynamics of acting automata. We demonstrate in computational experiments
that memory slows down propagation of perturbations, decrease entropy of
space-time patterns generated, transforms travelling localisations to
stationary oscillators, and stationary oscillations to still patterns.
