Center for the Neural Basis of Cognition

A neglected question regarding cognitive control is how control processes might detect situations calling for their involvement. The authors propose here that the demand for control may be evaluated in part by monitoring for conflicts in information processing. This hypothesis is supported by data concerning the anterior cingulate cortex, a brain area involved in cognitive control, which also appears to respond to the occurrence of conflict. The present article reports two computational modeling studies, serving to articulate the conflict monitoring hypothesis and examine its implications. The first study tests the sufficiency of the hypothesis to account for brain activation data, applying a measure of conflict to existing models of tasks shown to engage the anterior cingulate. The second study implements a feedback loop connecting conflict monitoring to cognitive control, using this to simulate a number of important behavioral phenomena.

Does the cerebellum influence nonmotor behavior? Recent anatomical studies demonstrate that the output of the cerebellum targets multiple nonmotor areas in the prefrontal and posterior parietal cortex, as well as the cortical motor areas. The projections to different cortical areas originate from distinct output channels within the cerebellar nuclei. The cerebral cortical area that is the main target of each output channel is a major source of input to the channel. Thus, a closed-loop circuit represents the major architectural unit of cerebro-cerebellar interactions. The outputs of these loops provide the cerebellum with the anatomical substrate to influence the control of movement and cognition. Neuroimaging and neuropsychological data supply compelling support for this view. The range of tasks associated with cerebellar activation is remarkable and includes tasks designed to assess attention, executive control, language, working memory, learning, pain, emotion, and addiction. These data, along with the revelations about cerebro-cerebellar circuitry, provide a new framework for exploring the contribution of the cerebellum to diverse aspects of behavior.

The space around us is represented not once but many times in parietal cortex. These multiple representations encode locations and objects of interest in several egocentric reference frames. Stimulus representations are transformed from the coordinates of receptor surfaces, such as the retina or the cochlea, into the coordinates of effectors, such as the eye, head, or hand. The transformation is accomplished by dynamic updating of spatial representations in conjunction with voluntary movements. This direct sensory-to-motor coordinate transformation obviates the need for a single representation of space in environmental coordinates. In addition to representing object locations in motoric coordinates, parietal neurons exhibit strong modulation by attention. Both top-down and bottom-up mechanisms of attention contribute to the enhancement of visual responses. The saliance of a stimulus is the primary factor in determining the neural response to it. Although parietal neurons represent objects in motor coordinates, visual responses are independent of the intention to perform specific motor acts.

We used transneuronal transport of neurotropic viruses to examine the topographic organization of circuits linking the cerebellar cortex with the arm area of the primary motor cortex (M1) and with area 46 in dorsolateral prefrontal cortex of monkeys. Retrograde transneuronal transport of the CVS-11 (challenge virus strain 11) strain of rabies virus in cerebello-thalamocortical pathways revealed that the arm area of M1 receives input from Purkinje cells located primarily in lobules IV-VI of the cerebellar cortex. In contrast, transneuronal transport of rabies from area 46 revealed that it receives input from Purkinje cells located primarily in Crus II of the ansiform lobule. Thus, both M1 and area 46 are the targets of output from the cerebellar cortex. However, the output to each area of the cerebral cortex originates from Purkinje cells in different regions of the cerebellar cortex. Anterograde transneuronal transport of the H129 strain of herpes simplex virus type 1 (HSV1) revealed that neurons in the arm area of M1 project via the pons to granule cells primarily in lobules IV-VI, whereas neurons in area 46 project to granule cells primarily in Crus II. Together, the findings from rabies and HSV1 experiments indicate that the regions of the cerebellar cortex that receive input from M1 are the same as those that project to M1. Similarly, the regions of the cerebellar cortex that receive input from area 46 are the same as those that project to area 46. Thus, our observations suggest that multiple closed-loop circuits represent a fundamental architectural feature of cerebrocerebellar interactions.

Traditional views of visual processing suggest that early visual neurons in areas V1 and V2 are static spatiotemporal filters that extract local features from a visual scene. The extracted information is then channeled through a feedforward chain of modules in successively higher visual areas for further analysis. Recent electrophysiological recordings from early visual neurons in awake behaving monkeys reveal that there are many levels of complexity in the information processing of the early visual cortex, as seen in the long-latency responses of its neurons. These new findings suggest that activity in the early visual cortex is tightly coupled and highly interactive with the rest of the visual system. They lead us to propose a new theoretical setting based on the mathematical framework of hierarchical Bayesian inference for reasoning about the visual system. In this framework, the recurrent feedforward/feedback loops in the cortex serve to integrate top-down contextual priors and bottom-up observations so as to implement concurrent probabilistic inference along the visual hierarchy. We suggest that the algorithms of particle filtering and Bayesian-belief propagation might model these interactive cortical computations. We review some recent neurophysiological evidences that support the plausibility of these ideas.

The anterior cingulate cortex (ACC) has been shown to respond to conflict between simultaneously active, incompatible response tendencies. This area is active during high-conflict correct trials and also when participants make errors. Here, we use the temporal resolution of high-density event-related potentials (ERPs) in combination with source localization to investigate the timing of ACC activity during conflict and error detection. We predicted that the same area of the ACC is active prior to high-conflict correct responses and following erroneous responses. Dipole modeling supported this prediction: The frontocentral N2, occurring prior to the response on correct conflict trials, and the ERN, occurring immediately following error responses, could both be modeled as having a generator in the caudal ACC, suggesting the same process to underlie both peaks. Modeling further suggested that the rostral area of the ACC was also active following errors, but later in time, contributing to the error positivity (P(E)), and peaking at 200-250 msec following the ERN peak. Despite the inherent limitations of source localization, these data may begin to shed light on the timing of action-monitoring processes. First, the time course of caudal ACC activity follows the time course as predicted by the conflict theory of this region. Second, caudal ACC activity might be temporally dissociated from rostral ACC activity during error trials, which possibly reflects a separate, affective component of the evaluative functions of the ACC.

The detection of neural spike activity is a technical challenge that is a prerequisite for studying many types of brain function. Measuring the activity of individual neurons accurately can be difficult due to large amounts of background noise and the difficulty in distinguishing the action potentials of one neuron from those of others in the local area. This article reviews algorithms and methods for detecting and classifying action potentials, a problem commonly referred to as spike sorting. The article first discusses the challenges of measuring neural activity and the basic issues of signal detection and classification. It reviews and illustrates algorithms and techniques that have been applied to many of the problems in spike sorting and discusses the advantages and limitations of each and the applicability of these methods for different types of experimental demands. The article is written both for the physiologist wanting to use simple methods that will improve experimental yield and minimize the selection biases of traditional techniques and for those who want to apply or extend more sophisticated algorithms to meet new experimental challenges.

Research suggests that the basal ganglia complex is a major component of the neural circuitry that mediates reward-related processing. However, human studies have not yet characterized the response of the basal ganglia to an isolated reward, as has been done in animals. We developed an event-related functional magnetic resonance imaging paradigm to identify brain areas that are activated after presentation of a reward. Subjects guessed whether the value of a card was higher or lower than the number 5, with monetary rewards as an incentive for correct guesses. They received reward, punishment, or neutral feedback on different trials. Regions in the dorsal and ventral striatum were activated by the paradigm, showing differential responses to reward and punishment. Activation was sustained following a reward feedback, but decreased below baseline following a punishment feedback.

In an overcomplete basis, the number of basis vectors is greater than the dimensionality of the input, and the representation of an input is not a unique combination of basis vectors. Overcomplete representations have been advocated because they have greater robustness in the presence of noise, can be sparser, and can have greater flexibility in matching structure in the data. Overcomplete codes have also been proposed as a model of some of the response properties of neurons in primary visual cortex. Previous work has focused on finding the best representation of a signal using a fixed overcomplete basis (or dictionary). We present an algorithm for learning an overcomplete basis by viewing it as probabilistic model of the observed data. We show that overcomplete bases can yield a better approximation of the underlying statistical distribution of the data and can thus lead to greater coding efficiency. This can be viewed as a generalization of the technique of independent component analysis and provides a method for Bayesian reconstruction of signals in the presence of noise and for blind source separation when there are more sources than mixtures.

In vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep within the brains of freely moving animals. However, it is computationally challenging to extract single-neuronal activity from microendoscopic data, because of the very large background fluctuations and high spatial overlaps intrinsic to this recording modality. Here, we describe a new constrained matrix factorization approach to accurately separate the background and then demix and denoise the neuronal signals of interest. We compared the proposed method against previous independent components analysis and constrained nonnegative matrix factorization approaches. On both simulated and experimental data recorded from mice, our method substantially improved the quality of extracted cellular signals and detected more well-isolated neural signals, especially in noisy data regimes. These advances can in turn significantly enhance the statistical power of downstream analyses, and ultimately improve scientific conclusions derived from microendoscopic data.

In a previous report, we described the visual response properties in the ventral intraparietal area (area VIP) of the awake macaque. Here we describe the somatosensory response properties in area VIP and the patterns of correspondence between the responses of single neurons to independently administered tactile and visual stimulation. VIP neurons responded to visual stimulation only or to visual and tactile stimulation. Of 218 neurons tested, 153 (70%) were bimodal in the sense that they responded to stimuli that were independently applied in either sensory modality. Unimodal visual and bimodal neurons were intermingled within the recording area and could not be distinguished on the basis of their visual response properties alone. Most of the cells with a tactile receptive field (RF) responded well to light touch or air puffs. The distribution of RF locations principally emphasized the head (85%), with approximately equivalent representations of the upper and lower face areas. The tactile and visual RFs were aligned in a congruent manner, with the intersection of the visual vertical and horizontal meridian having its tactile counterpart in the nose/mouth area. Small foveal visual RFs were paired with small tactile RFs on the muzzle, and peripheral visual RFs were associated with tactile RFs on the side of the head or body. Most cells showed a strong sensitivity to moving stimuli, and the preferred directions of visual and tactile motion coincided in 85% of bimodal cells. In some cases, bimodal responses patterns were complementary: cells responding to motion in depth toward the monkey had responses, whereas cells responding to motion in depth away form the monkey had responses. Other forms of bimodal response congruence included orientation selectivity, and , , and / response types. The large proportion of bimodal tactile and visual neurons with congruent response properties in area VIP indicates that there are important functional differences between area VIP and other dorsal stream areas involved in the analysis of motion. We suggest that VIP is involved in the construction of a multisensory, head-centered representation of near extrapersonal space.

The basal ganglia and cerebellum are major subcortical structures that influence not only movement, but putatively also cognition and affect. Both structures receive input from and send output to the cerebral cortex. Thus, the basal ganglia and cerebellum form multisynaptic loops with the cerebral cortex. Basal ganglia and cerebellar loops have been assumed to be anatomically separate and to perform distinct functional operations. We investigated whether there is any direct route for basal ganglia output to influence cerebellar function that is independent of the cerebral cortex. We injected rabies virus (RV) into selected regions of the cerebellar cortex in cebus monkeys and used retrograde transneuronal transport of the virus to determine the origin of multisynaptic inputs to the injection sites. We found that the subthalamic nucleus of the basal ganglia has a substantial disynaptic projection to the cerebellar cortex. This pathway provides a means for both normal and abnormal signals from the basal ganglia to influence cerebellar function. We previously showed that the dentate nucleus of the cerebellum has a disynaptic projection to an input stage of basal ganglia processing, the striatum. Taken together these results provide the anatomical substrate for substantial two-way communication between the basal ganglia and cerebellum. Thus, the two subcortical structures may be linked together to form an integrated functional network.

Intracortical microstimulation of the somatosensory cortex offers the potential for creating a sensory neuroprosthesis to restore tactile sensation. Whereas animal studies have suggested that both cutaneous and proprioceptive percepts can be evoked using this approach, the perceptual quality of the stimuli cannot be measured in these experiments. We show that microstimulation within the hand area of the somatosensory cortex of a person with long-term spinal cord injury evokes tactile sensations perceived as originating from locations on the hand and that cortical stimulation sites are organized according to expected somatotopic principles. Many of these percepts exhibit naturalistic characteristics (including feelings of pressure), can be evoked at low stimulation amplitudes, and remain stable for months. Further, modulating the stimulus amplitude grades the perceptual intensity of the stimuli, suggesting that intracortical microstimulation could be used to convey information about the contact location and pressure necessary to perform dexterous hand movements associated with object manipulation.

This review discusses how neuroimaging can contribute to our understanding of a fundamental aspect of skilled reading: the ability to pronounce a visually presented word. One contribution of neuroimaging is that it provides a tool for localizing brain regions that are active during word reading. To assess the extent to which similar results are obtained across studies, a quantitative review of nine neuroimaging investigations of word reading was conducted. Across these studies, the results converge to reveal a set of areas active during word reading, including left-lateralized regions in occipital and occipitotemporal cortex, the left frontal operculum, bilateral regions within the cerebellum, primary motor cortex, and the superior and middle temporal cortex, and medial regions in the supplementary motor area and anterior cingulate. Beyond localization, the challenge is to use neuroimaging as a tool for understanding how reading is accomplished. Central to this challenge will be the integration of neuroimaging results with information from other methodologies. To illustrate this point, this review will highlight the importance of spelling-to-sound consistency in the transformation from orthographic (word form) to phonological (word sound) representations, and then explore results from three neuroimaging studies in which the spelling-to-sound consistency of the stimuli was deliberately varied. Emphasis is placed on the pattern of activation observed within the left frontal cortex, because the results provide an example of the issues and benefits involved in relating neuroimaging results to behavioral results in normal and brain damaged subjects, and to theoretical models of reading.

We have used retrograde transneuronal transport of neurotropic viruses to examine the organization of the projections from the dentate nucleus of the cerebellum to "motor" and "nonmotor" areas of the cerebral cortex. To perform this analysis we created an unfolded map of the dentate. Plotting the results from current and prior experiments on this unfolded map revealed important features about the topography of function in the dentate. We found that the projections to the primary motor and premotor areas of the cerebral cortex originated from dorsal portions of the dentate. In contrast, projections to prefrontal and posterior parietal areas of cortex originated from ventral portions of the dentate. Thus the dentate contains anatomically separate and functionally distinct motor and nonmotor domains.

Electrophysiological recording studies in the dorsocaudal region of medial entorhinal cortex (dMEC) of the rat reveal cells whose spatial firing fields show a remarkably regular hexagonal grid pattern (Fyhn et al., 2004; Hafting et al., 2005). We describe a symmetric, locally connected neural network, or spin glass model, that spontaneously produces a hexagonal grid of activity bumps on a two-dimensional sheet of units. The spatial firing fields of the simulated cells closely resemble those of dMEC cells. A collection of grids with different scales and/or orientations forms a basis set for encoding position. Simulations show that the animal's location can easily be determined from the population activity pattern. Introducing an asymmetry in the model allows the activity bumps to be shifted in any direction, at a rate proportional to velocity, to achieve path integration. Furthermore, information about the structure of the environment can be superimposed on the spatial position signal by modulation of the bump activity levels without significantly interfering with the hexagonal periodicity of firing fields. Our results support the conjecture of Hafting et al. (2005) that an attractor network in dMEC may be the source of path integration information afferent to hippocampus.

How does one create an intelligent machine? This problem has proven difficult. Over the past several decades, scientists have taken one of three approaches: In the first, which is knowledge-based, an intelligent machine in a laboratory is directly programmed to perform a given task. In a second, learning-based approach, a computer is "spoon-fed" human-edited sensory data while the machine is controlled by a task-specific learning program. Finally, by a "genetic search," robots have evolved through generations by the principle of survival of the fittest, mostly in a computer-simulated virtual world. Although notable, none of these is powerful enough to lead to machines having the complex, diverse, and highly integrated capabilities of an adult brain, such as vision, speech, and language. Nevertheless, these traditional approaches have

The spiking activity of cortical neurons is correlated. For instance, trial-to-trial fluctuations in response strength are shared between neurons, and spikes often occur synchronously. Understanding the properties and mechanisms that generate these forms of correlation is critical for determining their role in cortical processing. We therefore investigated the spatial extent and functional specificity of correlated spontaneous and evoked activity. Because feedforward, recurrent, and feedback pathways have distinct extents and specificity, we reasoned that these measurements could elucidate the contribution of each type of input. We recorded single unit activity with microelectrode arrays which allowed us to measure correlation in many hundreds of pairings, across a large range of spatial scales. Our data show that correlated evoked activity is generated by two mechanisms that link neurons with similar orientation preferences on different spatial scales: one with high temporal precision and a limited spatial extent (approximately 3 mm), and a second that gives rise to correlation on a slow time scale and extends as far as we were able to measure (10 mm). The former is consistent with common input provided by horizontal connections; the latter likely involves feedback from extrastriate cortex. Spontaneous activity was correlated over a similar spatial extent, but approximately twice as strongly as evoked activity. Visual stimuli thus caused a substantial decrease in correlation, particularly at response onset. These properties and the circuit mechanism they imply provide new constraints on the functional role that correlation may play in visual processing.

Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations, but extracting the activity of each neuron from raw fluorescence calcium imaging data is a nontrivial problem. We present a fast online active set method to solve this sparse non-negative deconvolution problem. Importantly, the algorithm 3progresses through each time series sequentially from beginning to end, thus enabling real-time online estimation of neural activity during the imaging session. Our algorithm is a generalization of the pool adjacent violators algorithm (PAVA) for isotonic regression and inherits its linear-time computational complexity. We gain remarkable increases in processing speed: more than one order of magnitude compared to currently employed state of the art convex solvers relying on interior point methods. Unlike these approaches, our method can exploit warm starts; therefore optimizing model hyperparameters only requires a handful of passes through the data. A minor modification can further improve the quality of activity inference by imposing a constraint on the minimum spike size. The algorithm enables real-time simultaneous deconvolution of O(105) traces of whole-brain larval zebrafish imaging data on a laptop.

Recognizing printed words requires the mapping of graphic forms, which vary with writing systems, to linguistic forms, which vary with languages. Using a newly developed meta-analytic approach, aggregated Gaussian-estimated sources (AGES; Chein et al. [2002]: Psychol Behav 77:635-639), we examined the neuroimaging results for word reading within and across writing systems and languages. To find commonalities, we compiled 25 studies in English and other Western European languages that use an alphabetic writing system, 9 studies of native Chinese reading, 5 studies of Japanese Kana (syllabic) reading, and 4 studies of Kanji (morpho-syllabic) reading. Using the AGES approach, we created meta-images within each writing system, isolated reliable foci of activation, and compared findings across writing systems and languages. The results suggest that these writing systems utilize a common network of regions in word processing. Writing systems engage largely the same systems in terms of gross cortical regions, but localization within those regions suggests differences across writing systems. In particular, the region known as the visual word form area (VWFA) shows strikingly consistent localization across tasks and across writing systems. This region in the left mid-fusiform gyrus is critical to word recognition across writing systems and languages.