Spatial reorientation with a geometric array of auditory cues

Vestibular contribution to spatial encoding

Determining the spatial relation between objects and our location in the surroundings is essential for survival. Vestibular inputs provide key information about the position and movement of our head in the three-dimensional space, contributing to spatial navigation. Yet, their role in encoding spatial localisation of environmental targets remains to be fully understood. We probed the accuracy and precision of healthy participants' representations of environmental space by measuring their ability to encode the spatial location of visual targets (Experiment 1). Participants were asked to detect a visual light and then walk towards it. Vestibular signalling was artificially disrupted using stochastic galvanic vestibular stimulation (sGVS) applied selectively during encoding targets' location. sGVS impaired the accuracy and precision of locating the environmental visual targets. Importantly, this effect was specific to the visual modality. The location of acoustic targets was not influenced by vestibular alterations (Experiment 2). Our findings indicate that the vestibular system plays a role in localising visual targets in the surrounding environment, suggesting a crucial functional interaction between vestibular and visual signals for the encoding of the spatial relationship between our body position and the surrounding objects.

Effects of neuromodulation-inspired mechanisms on the performance of deep neural networks in a spatial learning task

In recent years, the biological underpinnings of adaptive learning have been modeled, leading to faster model convergence and various behavioral benefits in tasks including spatial navigation and cue-reward association. Furthermore, studies have investigated how the neuromodulatory system, a major driver of synaptic plasticity and state-dependent changes in the brain neuronal activities, plays a role in training deep neural networks (DNNs). In this study, we extended previous studies on neuromodulation-inspired DNNs and explored the effects of neuromodulatory components on learning and single unit activities in a spatial learning task. Under the multiscale neuromodulatory framework, plastic components, dropout probability modulation, and learning rate decay were added to the single unit, layer, and whole network levels of DNN models, respectively. We observed behavioral benefits including faster learning and smaller error of ambulation. We then concluded that neuromodulatory components can affect learning trajectories, outcomes, and single unit activities, in a component- and hyperparameter-dependent manner.