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.

Dynamic population coding and its relationship to working memory.

For over 45 years, neuroscientists have conducted experiments aimed at understanding the neural basis of working memory. Early results examining individual neurons highlighted that information is stored in working memory in persistent sustained activity where neurons maintained elevated firing rates over extended periods of time. However, more recent work has emphasized that information is often stored in working memory in dynamic population codes, where different neurons contain information at different periods in time. In this paper, I review findings that show that both sustained activity as well as dynamic codes are present in the prefrontal cortex and other regions during memory delay periods. I also review work showing that dynamic codes are capable of supporting working memory and that such dynamic codes could easily be "readout" by downstream regions. Finally, I discuss why dynamic codes could be useful for enabling animals to solve tasks that involve working memory. Although additional work is still needed to know definitively whether dynamic coding is critical for working memory, the findings reviewed here give insight into how different codes could contribute to working memory, which should be useful for guiding future research.

Differential Processing of Isolated Object and Multi-item Pop-Out Displays in LIP and PFC.

Objects that are highly distinct from their surroundings appear to visually "pop-out." This effect is present for displays in which: (1) a single cue object is shown on a blank background, and (2) a single cue object is highly distinct from surrounding objects; it is generally assumed that these 2 display types are processed in the same way. To directly examine this, we applied a decoding analysis to neural activity recorded from the lateral intraparietal (LIP) area and the dorsolateral prefrontal cortex (dlPFC). Our analyses showed that for the single-object displays, cue location information appeared earlier in LIP than in dlPFC. However, for the display with distractors, location information was substantially delayed in both brain regions, and information first appeared in dlPFC. Additionally, we see that pattern of neural activity is similar for both types of displays and across different color transformations of the stimuli, indicating that location information is being coded in the same way regardless of display type. These results lead us to hypothesize that 2 different pathways are involved processing these 2 types of pop-out displays.

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.

The dynamics of invariant object recognition in the human visual system.

The human visual system can rapidly recognize objects despite transformations that alter their appearance. The precise timing of when the brain computes neural representations that are invariant to particular transformations, however, has not been mapped in humans. Here we employ magnetoencephalography decoding analysis to measure the dynamics of size- and position-invariant visual information development in the ventral visual stream. With this method we can read out the identity of objects beginning as early as 60 ms. Size- and position-invariant visual information appear around 125 ms and 150 ms, respectively, and both develop in stages, with invariance to smaller transformations arising before invariance to larger transformations. Additionally, the magnetoencephalography sensor activity localizes to neural sources that are in the most posterior occipital regions at the early decoding times and then move temporally as invariant information develops. These results provide previously unknown latencies for key stages of human-invariant object recognition, as well as new and compelling evidence for a feed-forward hierarchical model of invariant object recognition where invariance increases at each successive visual area along the ventral stream.

The neural decoding toolbox.

Population decoding is a powerful way to analyze neural data, however, currently only a small percentage of systems neuroscience researchers use this method. In order to increase the use of population decoding, we have created the Neural Decoding Toolbox (NDT) which is a Matlab package that makes it easy to apply population decoding analyses to neural activity. The design of the toolbox revolves around four abstract object classes which enables users to interchange particular modules in order to try different analyses while keeping the rest of the processing stream intact. The toolbox is capable of analyzing data from many different types of recording modalities, and we give examples of how it can be used to decode basic visual information from neural spiking activity and how it can be used to examine how invariant the activity of a neural population is to stimulus transformations. Overall this toolbox will make it much easier for neuroscientists to apply population decoding analyses to their data, which should help increase the pace of discovery in neuroscience.

Incorporation of new information into prefrontal cortical activity after learning working memory tasks.

The ability to learn new tasks requires that new information is integrated into neural systems that already support other behaviors. To study how new information is incorporated into neural representations, we analyzed single-unit recordings from the prefrontal cortex (PFC), a brain region important for task acquisition and working memory, before and after monkeys learned to perform two behavioral tasks. A population-decoding analysis revealed a large increase in task-relevant information, and smaller changes in stimulus-related information, after training. This new information was contained in dynamic patterns of neural activity, with many individual neurons containing the new task-relevant information for only relatively short periods of time in the midst of other large firing rate modulations. Additionally, we found that stimulus information could be decoded with high accuracy only from dorsal PFC, whereas task-relevant information was distributed throughout both dorsal and ventral PFC. These findings help resolve a controversy about whether PFC is innately specialized to process particular types of information or whether its responses are completely determined by task demands by showing there is both regional specialization within PFC that was present before training, as well as more widespread task-relevant information that is a direct result of learning. The results also show that information is incorporated into PFC through the emergence of a small population of highly selective neurons that overlay new signals on top of patterns of activity that contain information about previously encoded variables, which gives insight into how information is coded in neural activity.

Object decoding with attention in inferior temporal cortex.

Recognizing objects in cluttered scenes requires attentional mechanisms to filter out distracting information. Previous studies have found several physiological correlates of attention in visual cortex, including larger responses for attended objects. However, it has been unclear whether these attention-related changes have a large impact on information about objects at the neural population level. To address this question, we trained monkeys to covertly deploy their visual attention from a central fixation point to one of three objects displayed in the periphery, and we decoded information about the identity and position of the objects from populations of ∼ 200 neurons from the inferior temporal cortex using a pattern classifier. The results show that before attention was deployed, information about the identity and position of each object was greatly reduced relative to when these objects were shown in isolation. However, when a monkey attended to an object, the pattern of neural activity, represented as a vector with dimensionality equal to the size of the neural population, was restored toward the vector representing the isolated object. Despite this nearly exclusive representation of the attended object, an increase in the salience of nonattended objects caused "bottom-up" mechanisms to override these "top-down" attentional enhancements. The method described here can be used to assess which attention-related physiological changes are directly related to object recognition, and should be helpful in assessing the role of additional physiological changes in the future.

Dynamic population coding of category information in inferior temporal and prefrontal cortex.

Most electrophysiology studies analyze the activity of each neuron separately. While such studies have given much insight into properties of the visual system, they have also potentially overlooked important aspects of information coded in changing patterns of activity that are distributed over larger populations of neurons. In this work, we apply a population decoding method to better estimate what information is available in neuronal ensembles and how this information is coded in dynamic patterns of neural activity in data recorded from inferior temporal cortex (ITC) and prefrontal cortex (PFC) as macaque monkeys engaged in a delayed match-to-category task. Analyses of activity patterns in ITC and PFC revealed that both areas contain "abstract" category information (i.e., category information that is not directly correlated with properties of the stimuli); however, in general, PFC has more task-relevant information, and ITC has more detailed visual information. Analyses examining how information coded in these areas show that almost all category information is available in a small fraction of the neurons in the population. Most remarkably, our results also show that category information is coded by a nonstationary pattern of activity that changes over the course of a trial with individual neurons containing information on much shorter time scales than the population as a whole.