  Sequential neuronal activity underlies a wide range of processes in the
brain. Neuroscientific evidence for neuronal sequences has been reported in
domains as diverse as perception, motor control, speech, spatial navigation and
memory. Consequently, different dynamical principles have been proposed as
possible sequence-generating mechanisms. Combining experimental findings with
computational concepts like the Bayesian brain hypothesis and predictive coding
leads to the interesting possibility that predictive and inferential processes
in the brain are grounded on generative processes which maintain a sequential
structure. While probabilistic inference about ongoing sequences is a useful
computational model for both the analysis of neuroscientific data and a wide
range of problems in artificial recognition and motor control, research on the
subject is relatively scarce and distributed over different fields in the
neurosciences. Here we review key findings about neuronal sequences and relate
these to the concept of online inference on sequences as a model of
sensory-motor processing and recognition. We propose that describing sequential
neuronal activity as an expression of probabilistic inference over sequences
may lead to novel perspectives on brain function. Importantly, it is promising
to translate the key idea of probabilistic inference on sequences to machine
learning, in order to address challenges in the real-time recognition of speech
and human motion.
