A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex, bioRxiv, 2018-10-13

AbstractHow the neocortex works is a mystery. In this paper we propose a novel framework for understanding its function. Grid cells are neurons in the entorhinal cortex that represent the location of an animal in its environment. Recent evidence suggests that grid cell-like neurons may also be present in the neocortex. We propose that grid cells exist throughout the neocortex, in every region and in every cortical column. They define a location-based framework for how the neocortex functions. Whereas grid cells in the entorhinal cortex represent the location of one thing, the body relative to its environment, we propose that cortical grid cells simultaneously represent the location of many things. Cortical columns in somatosensory cortex track the location of tactile features relative to the object being touched and cortical columns in visual cortex track the location of visual features relative to the object being viewed. We propose that mechanisms in the entorhinal cortex and hippocampus that evolved for learning the structure of environments are now used by the neocortex to learn the structure of objects. Having a representation of location in each cortical column suggests mechanisms for how the neocortex represents object compositionality and object behaviors. It leads to the hypothesis that every part of the neocortex learns complete models of objects and that there are many models of each object distributed throughout the neocortex. The similarity of circuitry observed in all cortical regions is strong evidence that even high-level cognitive tasks are learned and represented in a location-based framework.

biorxiv neuroscience 100-200-users 2018

Measuring the average power of neural oscillations, bioRxiv, 2018-10-13

AbstractBackgroundNeural oscillations are often quantified as average power relative to a cognitive, perceptual, andor behavioral task. This is commonly done using Fourier-based techniques, such as Welch’s method for estimating the power spectral density, andor by estimating narrowband oscillatory power across trials, conditions, andor groups. The core assumption underlying these approaches is that the mean is an appropriate measure of central tendency. Despite the importance of this assumption, it has not been rigorously tested.New methodWe introduce extensions of common approaches that are better suited for the physiological reality of how neural oscillations often manifest as nonstationary, high-power bursts, rather than sustained rhythms. Log-transforming, or taking the median power, significantly reduces erroneously inflated power estimates.ResultsAnalyzing 101 participants’ worth of human electrophysiology, totaling 3,560 channels and over 40 hours data, we show that, in all cases examined, spectral power is not Gaussian distributed. This is true even when oscillations are prominent and sustained, such as visual cortical alpha. Power across time, at every frequency, is characterized by a substantial long tail, which implies that estimates of average power are skewed toward large, infrequent high-power oscillatory bursts.Comparison with existing methodsIn a simulated event-related experiment we show how introducing just a few high-power oscillatory bursts, as seen in real data, can, perhaps erroneously, cause significant differences between conditions using traditional methods. These erroneous effects are substantially reduced with our new methods.ConclusionsThese results call into question the validity of common statistical practices in neural oscillation research.Highlights<jatslist list-type=bullet><jatslist-item>Analyses of oscillatory power often assume power is normally distributed.<jatslist-item><jatslist-item>Analyzing &gt;40 hours of human MEEG and ECoG, we show that in all cases it is not.<jatslist-item><jatslist-item>This effect is demonstrated in simple simulation of an event-related task.<jatslist-item><jatslist-item>Overinflated power estimates are reduced via log-transformation or median power.<jatslist-item>

biorxiv neuroscience 0-100-users 2018

The Flexiscope a Low Cost, Flexible, Convertible, and Modular Microscope with Automated Scanning and Micromanipulation, bioRxiv, 2018-10-13

AbstractWith technologies rapidly evolving, many research institutions are now opting to invest in costly, high-quality, specialised microscopes which are shared by many researchers. As a consequence, the user does not have the ability to adapt a microscope to their specific needs and limitations in experimental design are introduced. A flexible work-horse microscopy system is a valuable tool in any laboratory to meet the diverse needs of a research team and promote innovation in experimental design. We have developed the Flexiscope; a multi-functional, adaptable, efficient and high performance microscopyelectrophysiology system for everyday applications in a neurobiology laboratory. The core optical components are relatively constant in the three configurations described here; an upright configuration, an inverted configuration and an uprightelectrophysiology configuration. We have provided a comprehensive description of the Flexiscope. We show that this method is capable of oblique infrared illumination imaging, multi-channel fluorescent imaging, and automated 3D scanning of larger specimens. Image quality is conserved across the three configurations of the microscope, and conversion between configurations is possible quickly and easily, while the motion control system can be repurposed to allow sub-micron computer-controlled micromanipulation. The Flexiscope provides similar performance and usability to commercially available systems. However, as it can be easily reconfigured for multiple roles, it can remove the need to purchase multiple microscopes, giving significant cost savings. The modular re-configurable nature allows the user to customise the system to their specific needs and adaptupgrade the system as challenges arise.

biorxiv neuroscience 0-100-users 2018

 

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