Neuron's eye view: Inferring features of complex stimuli from neural responses

The Effect of EEG Biofeedback Training Frequency and Environmental Conditions on Simple and Complex Reaction Times

The objective of this study is to evaluate the impact of EEG biofeedback training under normoxic and normobaric hypoxic conditions on both simple and complex reaction times in judo athletes, and to identify the optimal training frequency and environmental conditions that substantially enhance reaction times in the examined athlete groups. The study comprised 20 male judo athlete members of the Polish national judo team in the middleweight and heavyweight categories. We randomly assigned participants to an experimental group and a control group. We conducted the research over four cycles, varying the frequency of EEG biofeedback sessions and environmental circumstances for both the experimental and control groups. Every research cycle had 15 training sessions. The results showed that the experimental group, following the theta/beta regimen, got significantly faster at complex reactions after a training cycle that included sessions every other day at normal oxygen levels. Following daily training sessions in normoxic circumstances, we noted enhancements in simple reaction speeds. Under normobaric hypoxia conditions, the judo athletes showed deterioration in both simple and complex reaction times. The control group showed no similar changes. Daily EEG training in normoxic settings markedly improved simple reaction time, but EEG-BF training conducted every other day greatly raised complicated reaction time. In contrast, training under normobaric hypoxia settings did not result in enhancements in basic or complicated reaction times following EEG training.

Quantifying behavior to solve sensorimotor transformations: advances from worms and flies

The development of new computational tools has recently opened up the study of natural behaviors at a precision that was previously unachievable. These tools permit a highly quantitative analysis of behavioral dynamics at timescales that are well matched to the timescales of neural activity. Here we examine how combining these methods with established techniques for estimating an animal's sensory experience presents exciting new opportunities for dissecting the sensorimotor transformations performed by the nervous system. We focus this review primarily on examples from Caenorhabditis elegans and Drosophila melanogaster-for these model systems, computational approaches to characterize behavior, in combination with unparalleled genetic tools for neural activation, silencing, and recording, have already proven instrumental for illuminating underlying neural mechanisms.