Sequence processing is a critical area of research that underpins complex behavior, enabling organisms to encode, recognize, and express temporally patterned sequences. This ability spans a wide range of cognitive processes, from sensory transduction to abstract reasoning and decision-making, and is observed across various species. The term ""sequence processing"" encompasses diverse research disciplines, including cognitive science, psycholinguistics, neuroscience, and computer science, each with its unique perspective on dealing with temporally structured information. Despite the progress made in understanding sequence processing, significant gaps remain, particularly in linking these processes to neurobiological mechanisms. Recent studies have highlighted the role of the mammalian neocortex in implicitly acquiring sequential structures and regularities, emphasizing the need for a deeper exploration of the neural circuits involved. While recurrent neural networks are naturally suited for discovering temporal structures, current biophysical models often fall short in capturing the full complexity of these sequences. Promising candidate mechanisms, such as dendritic computation and reward-modulated plasticity, have been identified, but further research is needed to develop biologically-inspired computational models that bridge the gap between abstract cognitive theories and neural mechanisms.

This research topic aims to explore and advance our understanding of the neural mechanisms that enable learning and processing of sequential structures. The primary objective is to investigate how neural circuits, through their biophysical properties, support general capabilities for sequence processing. Key questions include identifying novel mechanisms and architectures that facilitate pattern perception, rule learning, segmentation, and compositionality. The research will also test hypotheses related to neuronal morphology, learning rules, and circuit motifs, with the goal of establishing causal links between brain function, cognition, and behavior.

To gather further insights in the intersection of biophysical models and neural mechanisms of sequence processing, we welcome articles addressing, but not limited to, the following themes:
- Novel mechanisms and architectures supporting sequence processing
- Biophysical processes with biological verisimilitude in neural circuits
- Artificial neural networks with biological properties
- Modeling studies in cognitive sciences, psychology, or psycholinguistics
- Neurophysiological experiments on human and non-human subjects
- Integration of cross-domain knowledge for mechanistic understanding
- Perspectives and strategies for bridging neuroscience with cognitive and linguistic domains

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Frontiers in Computational Neuroscience

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