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1. A preparation of turtle (Chrysemys picta and Pseudemys scripta) brain in which the integrity of the intracortical and geniculocortical pathways in visual cortex are maintained in vitro has been used to differentiate the excitatory amino acid (EAA) receptor subtypes involved in geniculocortical and intracortical synapses. 2. Stimulation of the geniculocortical fibers at subcortical loci produces monosynaptic excitatory postsynaptic potentials (EPSPs) in visual cortical neurons. These EPSPs are blocked by the broad-spectrum EAA receptor antagonist kynurenate (1-2 mM) and the non-N-methyl-D-aspartate (NMDA) antagonist 6, 7-dinitroquinoxaline-2,3-dione (DNQX, 10 microM), but not by the NMDA antagonist D,L-2-amino-5-phosphonovalerate (D,L-AP-5, 100 microM). These results indicate that the geniculocortical EPSP is mediated by EAAs that access principally, if not exclusively, EAA receptors of the non-NMDA subtypes. 3. Stimulation of intracortical fibers evokes compound EPSPs that could be resolved into three components differing in latency to peak. The component with the shortest latency was not affected by any of the EAA-receptor antagonists tested. The second component, of intermediate latency, was blocked by kyurenate and DNQX but not by D,L-AP-5. The component of longest latency was blocked by kynurenate and D,L-AP-5, but not by DNQX. These results indicate that the compound intracortical EPSP is comprised of three pharmacologically distinct components that are mediated by an unknown receptor, by quisqualate/kainate, and by NMDA receptors, respectively. 4. Repetitive stimulation of intracortical pathways at 0.33 Hz produces a dramatic potentiation of the late, D,L-AP-5-sensitive component of the intracortical EPSP. 5. These experiments lead to a hypothesis about the subtypes of EAA receptors that are accessed by the geniculocortical and intracortical pathways within visual cortex.

Like many complex dynamic systems, the brain exhibits scale-free dynamics that follow power-law scaling. Broadband power spectral density (PSD) of brain electrical activity exhibits state-dependent power-law scaling with a log frequency exponent that varies across frequency ranges. Widely divergent naturally occurring neural states, awake and slow wave sleep (SWS), were used to evaluate the nature of changes in scale-free indices of brain electrical activity. We demonstrate two analytic approaches to characterizing electrocorticographic (ECoG) data obtained during awake and SWS states. A data-driven approach was used, characterizing all available frequency ranges. Using an equal error state discriminator (EESD), a single frequency range did not best characterize state across data from all six subjects, though the ability to distinguish awake and SWS ECoG data in individual subjects was excellent. Multi-segment piecewise linear fits were used to characterize scale-free slopes across the entire frequency range (0.2–200 Hz). These scale-free slopes differed between awake and SWS states across subjects, particularly at frequencies below 10 Hz and showed little difference at frequencies above 70 Hz. A multivariate maximum likelihood analysis (MMLA) method using the multi-segment slope indices successfully categorized ECoG data in most subjects, though individual variation was seen. In exploring the differences between awake and SWS ECoG data, these analytic techniques show that no change in a single frequency range best characterizes differences between these two divergent biological states. With increasing computational tractability, the use of scale-free slope values to characterize ECoG and EEG data will have practical value in clinical and research studies.

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Virtual reality has made numerous advancements in recent years and is used with increasing frequency for education, diversion, and distraction. Beginning several years ago as a device that produced an image with only a few pixels, virtual reality is now able to generate detailed, three-dimensional, and interactive images. Furthermore, these images can be used to provide quantitative data when acting as a simulator or a rehabilitation device. In this article, we aim to draw attention to these areas, as well as highlight the current settings in which virtual reality (VR) is being actively studied and implemented within the field of neurosurgery and the neurosciences. Additionally, we discuss the current limitations of the applications of virtual reality within various settings. This article includes areas in which virtual reality has been used in applications both inside and outside of the operating room, such as pain control, patient education and counseling, and rehabilitation. Virtual reality's utility in neurosurgery and the neurosciences is widely growing, and its use is quickly becoming an integral part of patient care, surgical training, operative planning, navigation, and rehabilitation.