The hierarchical timescale hypothesis: Functional and structural convergence of biological networks and artificial neural nets

Are there general, systems-level principles guiding the evolution and design of natural or artificial sensory and signaling networks? Here, we argue that the signal transduction networks in living cells display important similarities in their organization and dynamical responses to both synaptic networks of brain cells and recent architectures of artificial neural networks. We propose that the key property of all of these networks—organization into multiple layers with hierarchically distributed timescales—is not accidental but rather reflects optimal processing of complex signaling and sensory inputs. We term this the hierarchical timescale hypothesis. We propose that the convergent evolution toward multi-step processing with “decreasing bandwidth” can also explain multiple properties of signaling networks, such as how a single input can control diverse outputs on different timescales and how noise and delay accumulation can be gracefully handled by the network.