No abstract

Infrared neural stimulation (INS) is an optical stimulation technique which uses coherent light to stimulate nerves and neurons and which shows increased spatial selectivity compared to electrical stimulation. This could improve deep brain, high channel count, or vagus nerve stimulation. In this study, we seek to understand the wavelength dependence of INS in the near-infrared optical window. Rat sciatic nerves were excised ex vivo and stimulated with wavelengths between 700 and 900 nm. Recorded compound nerve action potentials (CNAPs) showed that stimulation was maximized in the 700 nm window despite comparable laser power levels across wavelengths. Computational models demonstrated that wavelength-based activation dependencies were not a result of passive optical properties. This data demonstrates that INS is both wavelength and power level dependent, which inform stimulation systems to actively target neural microcircuits in humans.

Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.

Background: Numerous studies have investigated emotion in virtual reality (VR) experiences using self-reported data in order to understand valence and arousal dimensions of emotion. Objective physiological data concerning valence and arousal has been less explored. Electroencephalography (EEG) can be used to examine correlates of emotional responses such as valence and arousal in virtual reality environments. Used across varying fields of research, images are able to elicit a range of affective responses from viewers. In this study, we display image sequences with annotated valence and arousal values on a screen within a virtual reality theater environment. Understanding how brain activity responses are related to affective stimuli with known valence and arousal ratings may contribute to a better understanding of affective processing in virtual reality.Methods: We investigated frontal alpha asymmetry (FAA) responses to image sequences previously annotated with valence and arousal ratings. Twenty-four participants viewed image sequences in VR with known valence and arousal values while their brain activity was recorded. Participants wore the Oculus Quest VR headset and viewed image sequences while immersed in a virtual reality theater environment.Results: Image sequences with higher valence ratings elicited greater FAA scores than image sequences with lower valence ratings (F [1, 23] = 4.631, p = 0.042), while image sequences with higher arousal scores elicited lower FAA scores than image sequences with low arousal (F [1, 23] = 7.143, p = 0.014). The effect of valence on alpha power did not reach statistical significance (F [1, 23] = 4.170, p = 0.053). We determined that only the high valence, low arousal image sequence elicited FAA which was significantly higher than FAA recorded during baseline (t [23] = −3.166, p = 0.002), suggesting that this image sequence was the most salient for participants.Conclusion: Image sequences with higher valence, and lower arousal may lead to greater FAA responses in VR experiences. While findings suggest that FAA data may be useful in understanding associations between valence and arousal self-reported data and brain activity responses elicited from affective experiences in VR environments, additional research concerning individual differences in affective processing may be informative for the development of affective VR scenarios.