Bayesian optimization of distributed neurodynamical controller models for spatial navigation

Neurodynamical Computing at the Information Boundaries of Intelligent Systems

“…Neural oscillations are measured extracellularly as the net synaptic loop currents in the local volume [139,140] and the structure of prevalent frequency bands is highly conserved across mammals including humans [141][142][143]. Neuronal spike phase is measured as the relative alignment of spikes to oscillatory cycles and effectively constitutes a distinct spatiotemporal dimension of neural interaction that naturally supports sequence learning, generation, and chunking in biological [144][145][146][147][148][149][150][151][152] and artificial [153][154][155][156] systems. Because various lines of evidence indicate that neuronal spike phase and collective oscillations may be causally bidirectional [157][158][159][160][161][162], the discrete neural states organized by oscillatory cycles are candidate computational states.…”

Special Issue—Biosensors and Neuroscience: Is Biosensors Engineering Ready to Embrace Design Principles from Neuroscience?

“…In addition to addressing two distinct grand challenges [25,26], BRAID sought to advance the application of a new class of brain-inspired engineering learning system based on novel or existing neuroscience theories [27][28][29][30][31] in the domain of autonomous systems, including neuromorphic sensors [32][33][34], brain-inspired robots [35][36][37], metacontrollers for multi-agent robots [38][39][40], neuromorphic medical technologies [41,42], and other applications with global economic and health benefits. BRAID especially focused on improving performance metrics beyond energy-and data-efficient learning algorithms [20,43] and learning hardware, including the development of neuromorphic systems [10,[44][45][46], the designs of which were based on insights from recent neuroscience advances [27,31,[47][48][49][50][51][52].…”