Biological computation through recurrence
arxiv(2024)
摘要
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 or work,
mainly coming from the machine learning and computational neuroscience fields,
has shown that such complex information processing can be performed by
recurrent networks. In those networks, temporal computations emerge from the
interaction between incoming stimuli and the internal dynamic state of the
network. 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.
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