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A critical component of anesthesia is the loss of sensory perception. Propofol is the most widely used drug for general anesthesia, but the neural mechanisms of how and when it disrupts sensory processing are not fully understood. We analyzed local field potential and spiking recorded from Utah arrays in auditory cortex, associative cortex, and cognitive cortex of nonhuman primates before and during propofol-mediated unconsciousness. Sensory stimuli elicited robust and decodable stimulus responses and triggered periods of stimulus-related synchronization between brain areas in the local field potential of Awake animals. By contrast, propofol-mediated unconsciousness eliminated stimulus-related synchrony and drastically weakened stimulus responses and information in all brain areas except for auditory cortex, where responses and information persisted. However, we found stimuli occurring during spiking Up states triggered weaker spiking responses than in Awake animals in auditory cortex, and little or no spiking responses in higher order areas. These results suggest that propofol's effect on sensory processing is not just because of asynchronous Down states. Rather, both Down states and Up states reflect disrupted dynamics. [ABSTRACT FROM AUTHOR]

Working memory has long been thought to arise from sustained spiking/attractor dynamics. However, recent work has suggested that short-term synaptic plasticity (STSP) may help maintain attractor states over gaps in time with little or no spiking. To determine if STSP endows additional functional advantages, we trained artificial recurrent neural networks (RNNs) with and without STSP to perform an object working memory task. We found that RNNs with and without STSP were able to maintain memories despite distractors presented in the middle of the memory delay. However, RNNs with STSP showed activity that was similar to that seen in the cortex of a non-human primate (NHP) performing the same task. By contrast, RNNs without STSP showed activity that was less brain-like. Further, RNNs with STSP were more robust to network degradation than RNNs without STSP. These results show that STSP can not only help maintain working memories, it also makes neural networks more robust and brain-like. Author summary: Working memory has been thought to depend on sustained spiking alone. But recent evidence shows that spiking is often sparse, not sustained. Short-term synaptic plasticity (STSP) could help by maintaining memories between spiking. To test this, we compared artificial recurrent neural networks (RNNs) with and without short-term synaptic plasticity (STSP). Both types of RNNs could maintain working memories. But RNNs with STSP functioned better. They were more robust to network degradation. Plus, their activity that was more brain-like than RNNs without STSP. These results support a role for STSP in working memory. [ABSTRACT FROM AUTHOR]

Oscillatory dynamics in cortex seem to organize into traveling waves that serve a variety of functions. Recent studies show that propofol, a widely used anesthetic, dramatically alters cortical oscillations by increasing slow-delta oscillatory power and coherence. It is not known how this affects traveling waves. We compared traveling waves across the cortex of non-human primates before, during, and after propofol-induced loss of consciousness (LOC). After LOC, traveling waves in the slow-delta (∼1 Hz) range increased, grew more organized, and traveled in different directions relative to the awake state. Higher frequency (8–30 Hz) traveling waves, by contrast, decreased, lost structure, and switched to directions where the slow-delta waves were less frequent. The results suggest that LOC may be due, in part, to increases in the strength and direction of slow-delta traveling waves that, in turn, alter and disrupt traveling waves in the higher frequencies associated with cognition. [ABSTRACT FROM AUTHOR]

Neural oscillations are evident across cortex but their spatial structure is not well- explored. Are oscillations stationary or do they form "traveling waves", i.e., spatially organized patterns whose peaks and troughs move sequentially across cortex? Here, we show that oscillations in the prefrontal cortex (PFC) organized as traveling waves in the theta (4-8Hz), alpha (8-12Hz) and beta (12-30Hz) bands. Some traveling waves were planar but most rotated. The waves were modulated during performance of a working memory task. During baseline conditions, waves flowed bidirectionally along a specific axis of orientation. Waves in different frequency bands could travel in different directions. During task performance, there was an increase in waves in one direction over the other, especially in the beta band. Author summary: We found that oscillations in the prefrontal cortex form "traveling waves". Traveling waves are spatially extended patterns in which aligned peaks of activity move sequentially across the cortical surface. Some traveling waves were planar but most rotated. The prefrontal cortex is important for working memory. The traveling waves changed when monkeys performed a working memory task. There was an increase in waves in one direction over the other, especially in the beta band. Traveling waves can serve specific functions. For example, they help maintain network status and help control timing relationships between spikes. Given their functional advantages, a greater understanding of traveling waves should lead to a greater understanding of cortical function. [ABSTRACT FROM AUTHOR]

Adaptive memory requires the organism to form associations that bridge between events separated in time. Many studies show interactions between hippocampus (HPC) and prefrontal cortex (PFC) during formation of such associations. We analyze neural recording from monkey HPC and PFC during a memory task that requires the monkey to associate stimuli separated by about a second in time. After the first stimulus was presented, large numbers of units in both HPC and PFC fired in sequence. Many units fired only when a particular stimulus was presented at a particular time in the past. These results indicate that both HPC and PFC maintain a temporal record of events that could be used to form associations across time. This temporal record of the past is a key component of the temporal coding hypothesis, a hypothesis in psychology that memory not only encodes what happened, but when it happened. [ABSTRACT FROM AUTHOR]

Many large-scale functional connectivity studies have emphasized the importance of communication through increased inter-region correlations during task states. In contrast, local circuit studies have demonstrated that task states primarily reduce correlations among pairs of neurons, likely enhancing their information coding by suppressing shared spontaneous activity. Here we sought to adjudicate between these conflicting perspectives, assessing whether co-active brain regions during task states tend to increase or decrease their correlations. We found that variability and correlations primarily decrease across a variety of cortical regions in two highly distinct data sets: non-human primate spiking data and human functional magnetic resonance imaging data. Moreover, this observed variability and correlation reduction was accompanied by an overall increase in dimensionality (reflecting less information redundancy) during task states, suggesting that decreased correlations increased information coding capacity. We further found in both spiking and neural mass computational models that task-evoked activity increased the stability around a stable attractor, globally quenching neural variability and correlations. Together, our results provide an integrative mechanistic account that encompasses measures of large-scale neural activity, variability, and correlations during resting and task states. Author summary: Statistical estimates of correlated neural activity and variability are widely used to characterize neural systems during different states. However, there is a conceptual gap between the use and interpretation of these measures between the human neuroimaging and non-human primate electrophysiology literature. For example, in the human neuroimaging literature, "functional connectivity" is often used to refer to correlated activity, while in the non-human primate electrophysiology literature, the equivalent term is "noise correlation". In an effort to unify these two perspectives under a single theoretical framework, we provide empirical evidence from human functional magnetic resonance imaging and non-human primate mean-field spike rate data that functional connectivity and noise correlations reveal similar statistical patterns during task states. In short, we found that task states primarily quench neural variability and correlations in both data sets. To provide a theoretically rigorous account capable of explaining this phenomena across both data sets, we use mean-field dynamical systems modeling to demonstrate the deterministic relationship between task-evoked activity, neural variability and correlations. Together, we provide an integrative account, showing that task-evoked activity quenches neural variability and correlations in large-scale neural systems. [ABSTRACT FROM AUTHOR]

Large-scale neuronal recording techniques have enabled discoveries of population-level mechanisms for neural computation. However, it is not clear how these mechanisms form by trial-and-error learning. In this article, we present an initial effort to characterize the population activity in monkey prefrontal cortex (PFC) and hippocampus (HPC) during the learning phase of a paired-associate task. To analyze the population data, we introduce the normalized distance, a dimensionless metric that describes the encoding of cognitive variables from the geometrical relationship among neural trajectories in state space. It is found that PFC exhibits a more sustained encoding of the visual stimuli, whereas HPC only transiently encodes the identity of the associate stimuli. Surprisingly, after learning, the neural activity is not reorganized to reflect the task structure, raising the possibility that learning is accompanied by some "silent" mechanism that does not explicitly change the neural representations. We did find partial evidence on the learning-dependent changes for some of the task variables. This study shows the feasibility of using normalized distance as a metric to characterize and compare population-level encoding of task variables and suggests further directions to explore learning-dependent changes in the neural circuits. [ABSTRACT FROM AUTHOR]

Working memory (WM) activity is not as stationary or sustained as previously thought. There are brief bursts of gamma (~50–120 Hz) and beta (~20–35 Hz) oscillations, the former linked to stimulus information in spiking. We examined these dynamics in relation to readout and control mechanisms of WM. Monkeys held sequences of two objects in WM to match to subsequent sequences. Changes in beta and gamma bursting suggested their distinct roles. In anticipation of having to use an object for the match decision, there was an increase in gamma and spiking information about that object and reduced beta bursting. This readout signal was only seen before relevant test objects, and was related to premotor activity. When the objects were no longer needed, beta increased and gamma decreased together with object spiking information. Deviations from these dynamics predicted behavioral errors. Thus, beta could regulate gamma and the information in WM. [ABSTRACT FROM AUTHOR]

The problem of identifying functional connectivity from multiple time series data recorded in each of two or more brain areas arises in many neuroscientific investigations. For a single stationary time series in each of two brain areas statistical tools such as cross-correlation and Granger causality may be applied. On the other hand, to examine multivariate interactions at a single time point, canonical correlation, which finds the linear combinations of signals that maximize the correlation, may be used. We report here a new method that produces interpretations much like these standard techniques and, in addition, 1) extends the idea of canonical correlation to 3-way arrays (with dimensionality equal to number of signals by number of time points by number of trials), 2) allows for nonstationarity, 3) also allows for nonlinearity, 4) scales well as the number of signals increases, and 5) captures predictive relationships, as is done with Granger causality. We demonstrate the effectiveness of the method through simulation studies and illustrate by analyzing local field potentials recorded from a behaving primate. NEW & NOTEWORTHY Multiple signals recorded from each of multiple brain regions may contain information about cross-region interactions. This article provides a method for visualizing the complicated interdependencies contained in these signals and assessing them statistically. The method combines signals optimally but allows the resulting measure of dependence to change, both within and between regions, as the responses evolve dynamically across time. We demonstrate the effectiveness of the method through numerical simulations and by uncovering a novel connectivity pattern between hippocampus and prefrontal cortex during a declarative memory task. [ABSTRACT FROM AUTHOR]

Memories are assumed to be represented by groups of co-activated neurons, called neural ensembles. Describing ensembles is a challenge: complexity of the underlying micro-circuitry is immense. Current approaches use a piecemeal fashion, focusing on single neurons and employing local measures like pairwise correlations. We introduce an alternative approach that identifies ensembles and describes the effective connectivity between them in a holistic fashion. It also links the oscillatory frequencies observed in ensembles with the spatial scales at which activity is expressed. Using unsupervised learning, biophysical modeling and graph theory, we analyze multi-electrode LFPs from frontal cortex during a spatial delayed response task. We find distinct ensembles for different cues and more parsimonious connectivity for cues on the horizontal axis, which may explain the oblique effect in psychophysics. Our approach paves the way for biophysical models with learned parameters that can guide future Brain Computer Interface development. [ABSTRACT FROM AUTHOR]

As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with "internal" memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)--regions critical for sensory associations--of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (~11-27 Hz) oscillatory power and synchrony associated with "top-down" or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing. [ABSTRACT FROM AUTHOR]

Much of our knowledge of the world depends on learning associations (for example, face-name), for which the hippocampus (HPC) and prefrontal cortex (PFC) are critical. HPC-PFC interactions have rarely been studied in monkeys, whose cognitive and mnemonic abilities are akin to those of humans. We found functional differences and frequency-specific interactions between HPC and PFC of monkeys learning object pair associations, an animal model of human explicit memory. PFC spiking activity reflected learning in parallel with behavioral performance, whereas HPC neurons reflected feedback about whether trial-and-error guesses were correct or incorrect. Theta-band HPC-PFC synchrony was stronger after errors, was driven primarily by PFC to HPC directional influences and decreased with learning. In contrast, alpha/beta-band synchrony was stronger after correct trials, was driven more by HPC and increased with learning. Rapid object associative learning may occur in PFC, whereas HPC may guide neocortical plasticity by signaling success or failure via oscillatory synchrony in different frequency bands. [ABSTRACT FROM AUTHOR]

Object perception seems effortless to us, but it depends on intensive neural processing across multiple stages in ventral pathway visual cortex. Shape information at the retinal level is hopelessly complex, variable and implicit. The ventral pathway must somehow transform retinal signals into much more compact, stable and explicit representations of object shape. Recent findings highlight key aspects of this transformation: higher-order contour derivatives, structural representation in object-based coordinates, composite shape tuning dimensions, and long-term storage of object knowledge. These coding principles could help to explain our remarkable ability to perceive, distinguish, remember and understand a virtual infinity of objects. [Copyright &y& Elsevier]

Summary: How does the brain synthesize low-level neural signals for simple shape parts into coherent representations of complete objects? Here, we present evidence for a dynamic process of object part integration in macaque posterior inferotemporal cortex (IT). Immediately after stimulus onset, neural responses carried information about individual object parts (simple contour fragments) only. Subsequently, information about specific multipart configurations emerged, building gradually over the course of ∼60 ms, producing a sparser and more explicit representation of object shape. We show that this gradual transformation can be explained by a recurrent network process that effectively compares parts signals across neurons to generate inferences about multipart shape configurations. [Copyright &y& Elsevier]

Object perception depends on shape processing in the ventral visual pathway, which in monkeys culminates in inferotemporal cortex (IT). Here we provide a description of fundamental quantitative principles governing neural selectivity for complex shape in IT. By measuring responses to large, parametric sets of two-dimensional (2D) silhouette shapes, we found that neurons in posterior IT (Brodmann's areas TEO and posterior TE) integrate information about multiple contour elements (straight and curved edge fragments of the type represented in lower-level areas) using both linear and nonlinear mechanisms. This results in complex, distributed response patterns that cannot be characterized solely in terms of example stimuli. We explained these response patterns with tuning functions in multidimensional shape space and accurately predicted neural responses to the widely varying shapes in our stimulus set. Integration of contour element information in earlier stages of IT represents an important step in the transformation from low-level shape signals to complex object representation. [ABSTRACT FROM AUTHOR]

Visual working memory (WM) storage is largely independent between the left and right visual hemifields/cerebral hemispheres, yet somehow WM feels seamless. We studied how WM is integrated across hemifields by recording neural activity bilaterally from lateral prefrontal cortex. An instructed saccade during the WM delay shifted the remembered location from one hemifield to the other. Before the shift, spike rates and oscillatory power showed clear signatures of memory laterality. After the shift, the lateralization inverted, consistent with transfer of the memory trace from one hemisphere to the other. Transferred traces initially used different neural ensembles from feedforward-induced ones, but they converged at the end of the delay. Around the time of transfer, synchrony between the two prefrontal hemispheres peaked in theta and beta frequencies, with a directionality consistent with memory trace transfer. This illustrates how dynamics between the two cortical hemispheres can stitch together WM traces across visual hemifields. [Display omitted] • A gaze shift swapped a working memory's location between visual hemifields • This induced transfer of the working memory trace between the right and left PFC • Trace transfer was accompanied by directed interhemispheric theta/beta synchrony • Transferred traces activated different ensembles than feedforward-induced traces Brincat et al. use bilateral recording to show working memories transferring between the right and left prefrontal cortex. Transferred memories engage different ensembles than feedforward-induced memory traces. Trace transfer is accompanied by directed interhemispheric theta/beta synchrony. [ABSTRACT FROM AUTHOR]

Summary A meta-analysis of non-human primates performing three different tasks (Object-Match, Category-Match, and Category-Saccade associations) revealed signatures of explicit and implicit learning. Performance improved equally following correct and error trials in the Match (explicit) tasks, but it improved more after correct trials in the Saccade (implicit) task, a signature of explicit versus implicit learning. Likewise, error-related negativity, a marker for error processing, was greater in the Match (explicit) tasks. All tasks showed an increase in alpha/beta (10–30 Hz) synchrony after correct choices. However, only the implicit task showed an increase in theta (3–7 Hz) synchrony after correct choices that decreased with learning. In contrast, in the explicit tasks, alpha/beta synchrony increased with learning and decreased thereafter. Our results suggest that explicit versus implicit learning engages different neural mechanisms that rely on different patterns of oscillatory synchrony. [ABSTRACT FROM AUTHOR]

Summary Working memory is thought to result from sustained neuron spiking. However, computational models suggest complex dynamics with discrete oscillatory bursts. We analyzed local field potential (LFP) and spiking from the prefrontal cortex (PFC) of monkeys performing a working memory task. There were brief bursts of narrow-band gamma oscillations (45–100 Hz), varied in time and frequency, accompanying encoding and re-activation of sensory information. They appeared at a minority of recording sites associated with spiking reflecting the to-be-remembered items. Beta oscillations (20–35 Hz) also occurred in brief, variable bursts but reflected a default state interrupted by encoding and decoding. Only activity of neurons reflecting encoding/decoding correlated with changes in gamma burst rate. Thus, gamma bursts could gate access to, and prevent sensory interference with, working memory. This supports the hypothesis that working memory is manifested by discrete oscillatory dynamics and spiking, not sustained activity. [ABSTRACT FROM AUTHOR]

Large-scale neuronal recording techniques have enabled discoveries of population-level mechanisms for neural computation. However, it is not clear how these mechanisms form by trial-and-error learning. In this article, we present an initial effort to characterize the population activity in monkey prefrontal cortex (PFC) and hippocampus (HPC) during the learning phase of a paired-associate task. To analyze the population data, we introduce the normalized distance, a dimensionless metric that describes the encoding of cognitive variables from the geometrical relationship among neural trajectories in state space. It is found that PFC exhibits a more sustained encoding of the visual stimuli, whereas HPC only transiently encodes the identity of the associate stimuli. Surprisingly, after learning, the neural activity is not reorganized to reflect the task structure, raising the possibility that learning is accompanied by some "silent" mechanism that does not explicitly change the neural representations. We did find partial evidence on the learning-dependent changes for some of the task variables. This study shows the feasibility of using normalized distance as a metric to characterize and compare population-level encoding of task variables and suggests further directions to explore learning-dependent changes in the neural circuits.

A meta-analysis of non-human primates performing three different tasks (Object-Match, Category-Match, and Category-Saccade associations) revealed signatures of explicit and implicit learning. Performance improved equally following correct and error trials in the Match (explicit) tasks, but it improved more after correct trials in the Saccade (implicit) task, a signature of explicit versus implicit learning. Likewise, error-related negativity, a marker for error processing, was greater in the Match (explicit) tasks. All tasks showed an increase in alpha/beta (10-30 Hz) synchrony after correct choices. However, only the implicit task showed an increase in theta (3-7 Hz) synchrony after correct choices that decreased with learning. In contrast, in the explicit tasks, alpha/beta synchrony increased with learning and decreased thereafter. Our results suggest that explicit versus implicit learning engages different neural mechanisms that rely on different patterns of oscillatory synchrony., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Objective: To date, invasive brain-computer interface (BCI) research has largely focused on replacing lost limb functions using signals from the hand/arm areas of motor cortex. However, the oculomotor system may be better suited to BCI applications involving rapid serial selection from spatial targets, such as choosing from a set of possible words displayed on a computer screen in an augmentative and alternative communication (AAC) application. Here we aimed to demonstrate the feasibility of a BCI utilizing the oculomotor system., Approach: We developed a chronic intracortical BCI in monkeys to decode intended saccadic eye movement direction using activity from multiple frontal cortical areas., Main Results: Intended saccade direction could be decoded in real time with high accuracy, particularly at contralateral locations. Accurate decoding was evident even at the beginning of the BCI session; no extensive BCI experience was necessary. High-frequency (80-500 Hz) local field potential magnitude provided the best performance, even over spiking activity, thus simplifying future BCI applications. Most of the information came from the frontal and supplementary eye fields, with relatively little contribution from dorsolateral prefrontal cortex., Significance: Our results support the feasibility of high-accuracy intracortical oculomotor BCIs that require little or no practice to operate and may be ideally suited for 'point and click' computer operation as used in most current AAC systems.