title: A Scalable Weight-Free Learning Algorithm for Regulatory Control of Cell Activity in Spiking Neuronal Networks.
abstract: Recent developments in neural stimulation and recording technologies are providing scientists with the ability of recording and controlling the activity of individual neurons in vitro or in vivo, with very high spatial and temporal resolution. Tools such as optogenetics, for example, are having a significant impact in the neuroscience field by delivering optical firing control with the precision and spatiotemporal resolution required for investigating information processing and plasticity in biological brains. While a number of training algorithms have been developed to date for spiking neural network (SNN) models of biological neuronal circuits, exiting methods rely on learning rules that adjust the synaptic strengths (or weights) directly, in order to obtain the desired network-level (or functional-level) performance. As such, they are not applicable to modifying plasticity in biological neuronal circuits, in which synaptic strengths only change as a result of pre- and post-synaptic neuron firings or biological mechanisms beyond our control. This paper presents a weight-free training algorithm that relies solely on adjusting the spatiotemporal delivery of neuron firings in order to optimize the network performance. The proposed weight-free algorithm does not require any knowledge of the SNN model or its plasticity mechanisms. As a result, this training approach is potentially realizable in vitro or in vivo via neural stimulation and recording technologies, such as optogenetics and multielectrode arrays, and could be utilized to control plasticity at multiple scales of biological neuronal circuits. The approach is demonstrated by training SNNs with hundreds of units to control a virtual insect navigating in an unknown environment.
authors.affiliation: 1 Mechanical Engineering and Materials Science, Duke University, Box 90300 Hudson Hall, Durham, NC, US.
authors.forename: Xu
authors.initials: X
authors.lastname: Zhang
authors.affiliation: 1 Mechanical Engineering and Materials Science, Duke University, Box 90300 Hudson Hall, Durham, NC, US.
authors.forename: Greg
authors.initials: G
authors.lastname: Foderaro
authors.affiliation: 2 Biomedical Engineering, Duke University, Box 90281 Hudson Hall, Durham, 27708, US.
authors.forename: Craig
authors.initials: C
authors.lastname: Henriquez
authors.affiliation: 3 Sibley School of Mechanical and Aerospace Engineering, Cornell University, 105 Upson Hall, Ithaca, New York, 14853, US.
authors.forename: Silvia
authors.initials: S
authors.lastname: Ferrari
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D007313:Insecta; D007858:Learning / Q000502:physiology*; D008124:Locomotion / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D014584:User-Computer Interface
keywords: Spiking neural networks; neural control; neuromorphic; optogenetics; reinforcement learning; spike timing-dependent plasticity