NeuroDecodeR: a package for neural decoding in R

Neural decoding is a powerful method to analyze neural activity. However, the code needed to run a decoding analysis can be complex, which can present a barrier to using the method. In this paper we introduce a package that makes it easy to perform decoding analyses in the R programing language. We describe how the package is designed in a modular fashion which allows researchers to easily implement a range of different analyses. We also discuss how to format data to be able to use the package, and we give two examples of how to use the package to analyze real data. We believe that this package, combined with the rich data analysis ecosystem in R, will make it significantly easier for researchers to create reproducible decoding analyses, which should help increase the pace of neuroscience discoveries

Turing++ Questions: A Test for the Science of (Human) Intelligence

There is a widespread interest among scientists in understanding a specific and well defined form of intelligence, that is human intelligence. For this reason we propose a stronger version of the original Turing test. In particular, we describe here an open-ended set of Turing++ questions that we are developing at the Center for Brains, Minds, and Machines at MIT -- that is questions about an image. For the Center for Brains, Minds, and Machines the main research goal is the science of intelligence rather than the engineering of intelligence -- the hardware and software of the brain rather than just absolute performance in face identification. Our Turing++ questions reflect fully these research priorities

Inducing Feelings of Ignorance Makes People More Receptive to Expert (economist) Opinion

While they usually should, people do not revise their beliefs more to expert (economist) opinion than to lay opinion. The present research sought to better understand the factors that make it more likely for an individual to change their mind when faced with the opinions of expert economists versus the general public. Here, across five studies (N = 2,650), I examined the role that overestimation of one’s knowledge plays in this behavior. I replicated the finding that people fail to privilege the opinion of experts over the public on two different (Study 1) and five different (Study 5) economic issues. I then found that undermining an illusion of both topic relevant (Studies 2 - 4) and irrelevant knowledge (Studies 3 & 4) can lead to greater belief revision in response to expert rather than lay opinion. I suggest one reason that people fail to revise their beliefs more to experts is because people tend to think they know more than they really do

Using neural population decoding to understand high level visual processing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2011.Cataloged from PDF version of thesis.Includes bibliographical references.The field of neuroscience has the potential to address profound questions including explaining how neural activity enables complex behaviors and conscious experience. However, currently the field is a long way from understanding these issues, and progress has been slow. One of the main problems holding back the pace of discovery is that it is still unclear how to interpret neural activity once it has been recorded. This lack of understanding has led to many different data analysis methods, which makes it difficult to evaluate the validity and importance of many reported results. If a clearer understanding of how to interpret neural data existed, it should be much easier to answer other questions about how the brain functions. In this thesis I describe how to use a data analysis method called 'neural population decoding' to analyze data in a way that is potentially more relevant for understanding neural information processing. By applying this method in novel ways to data from several vision experiments, I am able to make several new discoveries, including the fact that abstract category information is coded in the inferior temporal cortex (ITC) and prefrontal cortex (PFC) by dynamic patterns of neural activity, and that when a monkey attends to an object in a cluttered display, the pattern of ITC activity returns to a state that is similar to when the attended object is presented alone. These findings are not only interesting for insights that they give into the content and coding of information in high level visual areas, but they also demonstrate the benefits of using neural population decoding to analyze data. Thus, the methods developed in this thesis should enable more rapid progress toward an algorithmic level understanding of vision and information processing in other neural systems.by Ethan M. Meyers.Ph.D

Intelligent Information Loss: The Coding of Facial Identity, Head Pose, and Non-Face Information in the Macaque Face Patch System

Faces are a behaviorally important class of visual stimuli for primates. Recent work in macaque monkeys has identified six discrete face areas where most neurons have higher firing rates to images of faces compared with other objects (Tsao et al., 2006). While neurons in these areas appear to have different tuning (Freiwald and Tsao, 2010; Issa and DiCarlo, 2012), exactly what types of information and, consequently, which visual behaviors neural populations within each face area can support, is unknown. Here we use population decoding to better characterize three of these face patches (ML/MF, AL, and AM). We show that neural activity in all patches contains information that discriminates between the broad categories of face and nonface objects, individual faces, and nonface stimuli. Information is present in both high and lower firing rate regimes. However, there were significant differences between the patches, with the most anterior patch showing relatively weaker representation of nonface stimuli. Additionally, we find that pose-invariant face identity information increases as one moves to more anterior patches, while information about the orientation of the head decreases. Finally, we show that all the information we can extract from the population is present in patterns of activity across neurons, and there is relatively little information in the total activity of the population. These findings give new insight into the representations constructed by the face patch system and how they are successively transformed

Examining high level neural representations of cluttered scenes

Humans and other primates can rapidly categorize objects even when they are embedded in complex visual scenes (Thorpe et al., 1996; Fabre-Thorpe et al., 1998). Studies by Serre et al., 2007 have shown that the ability of humans to detect animals in brief presentations of natural images decreases as the size of the target animal decreases and the amount of clutter increases, and additionally, that a feedforward computational model of the ventral visual system, originally developed to account for physiological properties of neurons, shows a similar pattern of performance. Motivated by these studies, we recorded single- and multi-unit neural spiking activity from macaque superior temporal sulcus (STS) and anterior inferior temporal cortex (AIT), as a monkey passively viewed images of natural scenes. The stimuli consisted of 600 images of animals in natural scenes, and 600 images of natural scenes without animals in them, captured at four different viewing distances, and were the same images used by Serre et al. to allow for a direct comparison between human psychophysics, computational models, and neural data. To analyze the data, we applied population "readout" techniques (Hung et al., 2005; Meyers et al., 2008) to decode from the neural activity whether an image contained an animal or not. The decoding results showed a similar pattern of degraded decoding performance with increasing clutter as was seen in the human psychophysics and computational model results. However, overall the decoding accuracies from the neural data lower were than that seen in the computational model, and the latencies of information in IT were long (~125ms) relative to behavioral measures obtained from primates in other studies. Additional tests also showed that the responses of the model units were not capturing several properties of the neural responses, and that detecting animals in cluttered scenes using simple model units based on V1 cells worked almost as well as using more complex model units that were designed to model the responses of IT neurons. While these results suggest AIT might not be the primary brain region involved in this form of rapid categorization, additional studies are needed before drawing strong conclusions

Developing an IF panel to examine Cyclin and CDK interactions in a Pancreatic Adenocarcinoma Patient Derived Xenograph.

https://openworks.mdanderson.org/sumexp22/1084/thumbnail.jp

Preliminary MEG decoding results

Decoding analysis has been applied to electrophysiology and fMRI data to study the visual system, however, this method has only been applied to MEG visual data in a few instances. Here we use the Neural Decoding Toolbox for Matlab to show that it is possible to decode visual stimuli based on MEG data

Chemokine Receptor 5 Δ32 Allele in Patients with Severe Pandemic (H1N1) 2009

Because chemokine receptor 5 (CCR5) may have a role in pulmonary immune response, we explored whether patients with severe pandemic (H1N1) 2009 were more likely to carry the CCR5Δ32 allele than were members of the general population. We found a large proportion of heterozygosity for the CCR5Δ32 allele among white patients with severe disease

Concert recording 2013-03-31b

[Track 01]. Sweet Georgie fame / Blossom Dearie -- [Track 02]. Joy spring / Clifford Brown -- [Track 03]. Summer samba / Marcos Valle -- [Track 04]. Rhythm\u27ning / Thelonious Monk -- [Track 05]. One note samba / Antonio Carlos Jobim -- [Track 06]. In a sentimental mood / Duke Ellington -- [Track 07]. Recordame / Joe Henderson -- [Track 08]. Full house / Wes Montgomery -- [Track 09]. Cats and kittens / Peter Erskine -- [Track 10]. Primal prayer / Dan Haerle -- [Track 11]. Cookin\u27 Boox / Detroit Jackson