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

Separate and overlapping brain areas encode subjective value during delay and effort discounting

AbstractMaking decisions about rewards that involve delay or effort requires the integration of value and cost information. The brain areas recruited in this integration have been well characterized for delay discounting. However only a few studies have investigated how effort costs are integrated into value signals to eventually determine choice. In contrast to previous studies that have evaluated fMRI signals related to physical effort, we used a task that focused on cognitive effort. Participants discounted the value of delayed and effortful rewards. The value of cognitively effortful rewards was represented in the anterior portion of the inferior frontal gyrus and dorsolateral prefrontal cortex. Additionally, the value of the chosen option was encoded in the anterior cingulate cortex, caudate, and cerebellum. While most brain regions showed no significant dissociation between effort discounting and delay discounting, the ACC was significantly more activated in effort compared to delay discounting tasks. Finally, overlapping regions within the right orbitofrontal cortex and lateral temporal and parietal cortices encoded the value of the chosen option during both delay and effort discounting tasks. These results indicate that encoding of rewards discounted by cognitive effort and delay involves partially dissociable brain areas, but a common representation of chosen value is present in the orbitofrontal, temporal and parietal cortices

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

Prefrontal Manifold Geometry Contributes to Reaction Time Variability

<p>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. </p> <p>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</p> <p> </p> <p>```julia</p> <p>using JLD2</p> <p>ppsth,labels, trialidx, rtimes = JLD2.load("ppsth_fef_mov.jld2","ppsth", "labels","trialidx","rtimes")</p> <p>```</p> <p>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.</p> <p>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 <a href="https://github.com/grero/PrefrontalManifoldGeometry">repository</a> 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.</p&gt

Discovering Low-Dimensional Causal Pathways between Multiple Interacting Neuronal Populations

Understanding the nature of neural activity and computations in the brain will help us build better decision-making models to facilitate human-AI collaboration. Recording the neural activity of multiple and large neural populations in the brain is becoming widely available with modern recording techniques. It still remains a challenge, however, to understand how distinct and anatomically different neural populations interact with each other to control behaviour. We propose a new method to discover causal interactions between neural populations based on recurrent switching dynamical systems. We introduce an extended dynamics model that incorporates the current time-step when calculating the latent state variables. We also introduce an acyclicity constraint in learning the parameters of the model. These mechanisms enable rich causal interactions between neural populations to be identified from the learned model. Our model outperforms previous work on discovering interactions between neural populations in simulated datasets, without sacrificing the prediction performance of firing rates. We also apply our method on real neural recordings from two Macaque monkey brains performing a behavioral task, and show that the proposed method is able to detect causal interactions between brain regions related to the different time windows of the task