Biological computation through recurrence
María Sol Vidal-Saez, Óscar Vilarroya, Jordi García‐Ojalvo · Biochemical and Biophysical Research Communications · 2024
One of the defining features of living systems is their adaptability to changing environmental conditions. This requires organisms to extract temporal and spatial features of their environment, and use that information to compute the appropriate response. In the last two decades, a growing body of work, mainly coming from the machine learning and computational neuroscience fields, has shown that such complex information processing can be performed by recurrent networks. Temporal computations arise in these networks through the interplay between the external stimuli and the network's internal state. In this article we review our current understanding of how recurrent networks can be used by biological systems, from cells to brains, for complex information processing. Rather than focusing on sophisticated, artificial recurrent architectures such as long short-term memory (LSTM) networks, here we concentrate on simpler network structures and learning algorithms that can be expected to have been found by evolution. We also review studies showing evidence of naturally occurring recurrent networks in living organisms. Lastly, we discuss some relevant evolutionary aspects concerning the emergence of this natural computation paradigm. • Recurrent networks enable dynamic memory acquisition in biological systems. • Brain and gene networks have recurrent structures with computational capabilities. • Recurrence-based computations impose evolutionary constraints on biological networks.