Physiological Mechanisms Underlying Motion-Induced Blindness

Visual disappearance illusions - such as motion-induced blindness (MIB) - are commonly used to study the neural underpinnings of visual perception. In such illusions a salient visual target becomes perceptually invisible. Previous studies are inconsistent regarding the role of primary visual cortex (V1) in these illusions. Here we provide physiological and psychophysical evidence supporting a role for V1 in generating MIB

THE NEURAL REPLAY THOUGHT EXPERIMENT

This paper is directed at scientists interested in the relationship between the physical and the mental. My goal is to provide an accessible platform to expose and analyze the readers’ (often implicit) assumptions about the relationship between physical and mental phenomena. To this end, I developed an extension of the “neural replay thought experiment

THE NEURAL REPLAY THOUGHT EXPERIMENT

This paper is directed at scientists interested in the relationship between the physical and the mental. My goal is to provide an accessible platform to expose and analyze the readers’ (often implicit) assumptions about the relationship between physical and mental phenomena. To this end, I developed an extension of the “neural replay thought experiment”</p

Prefrontal Manifold Geometry Contributes to Reaction Time Variability

&lt;p&gt;Spike counts from cells recorded from the Frontal Eye Fields (FEF) and dorso-lateral prefrontal cortex (DLPFC) of 2 non-human primates preforming a delayed memory saccade task. Each file contains spike counts for all cells aligned to either target onset, go-cue onset or movement (saccade) onset. The suffix 'raw' indicates whether the file contains raw spike counts, or spike counts normalized to the pre-fixation baseline.&nbsp;&lt;/p&gt; &lt;p&gt;The data are stored in an HD5-based format for objects created with the Julia progamming language. The following code snippet shows how to load the data&lt;/p&gt; &lt;p&gt;&nbsp;&lt;/p&gt; &lt;p&gt;```julia&lt;/p&gt; &lt;p&gt;using JLD2&lt;/p&gt; &lt;p&gt;ppsth,labels, trialidx, rtimes = JLD2.load("ppsth_fef_mov.jld2","ppsth", "labels","trialidx","rtimes")&lt;/p&gt; &lt;p&gt;```&lt;/p&gt; &lt;p&gt;Here, the variable `ppsth` contains the spike counts in `ppsth.counts`, the bins in `ppsth.bins`. The variable `labels` contains the label of the target shown for each trial and for each cell. Note that, `length(labels)==size(ppsth.counts,3)` is the number of cells and `length(labels[1])` is the number of correct trails for cell `. The variable `rtimes` contains the reaction time for each session used. The session name for each cell can be found by examining the variable `ppsth.cellnames`, where the name of each cell has the format "Animal/date/session/array/channel/cellid/", e.g. "J/20140904/session01/array01/channel001/cell01" denotes the first cell on the first channel of the first array recorded in the first session on 4th September 2014 from animal J.&lt;/p&gt; &lt;p&gt;In addition to spike counts, this dataset also contains processed data for producing the main figures in an upcoming manuscript. To reproduce the figures, first go to the paper's &lt;a href="https://github.com/grero/PrefrontalManifoldGeometry"&gt;repository&lt;/a&gt; and follow the installation instructions. Then, download the data files to a 'data' sub-folder, and run the codes as instructed in the repository's README file.&lt;/p&gt

Mixed recurrent connectivity in primate prefrontal cortex.

The functional properties of a network depend on its connectivity, which includes the strength of its inputs and the strength of the connections between its units, or recurrent connectivity. Because we lack a detailed description of the recurrent connectivity in the lateral prefrontal cortex of primates, we developed an indirect method to estimate it. This method leverages the elevated noise correlation of mutually-connected units. To estimate the connectivity of prefrontal regions, we trained recurrent neural network models with varying percentages of bump attractor connectivity and noise levels to match the noise correlation properties observed in two specific prefrontal regions: the dorsolateral prefrontal cortex and the frontal eye field. We found that models initialized with approximately 20% and 7.5% bump attractor connectivity closely matched the noise correlation properties of the frontal eye field and dorsolateral prefrontal cortex, respectively. These findings suggest that the different percentages of bump attractor connectivity may reflect distinct functional roles of these brain regions. Specifically, lower percentages of bump attractor units, associated with higher-dimensional representations, likely support more abstract neural representations in more anterior regions