Parietal low beta rhythm provides a dynamical substrate for a working memory buffer

Multi-timescale neural dynamics for multisensory integration

Carrying out any everyday task, be it driving in traffic, conversing with friends or playing basketball, requires rapid selection, integration and segregation of stimuli from different sensory modalities. At present, even the most advanced artificial intelligence-based systems are unable to replicate the multisensory processes that the human brain routinely performs, but how neural circuits in the brain carry out these processes is still not well understood. In this Perspective, we discuss recent findings that shed fresh light on the oscillatory neural mechanisms that mediate multisensory integration (MI), including power modulations, phase resetting, phase-amplitude coupling and dynamic functional connectivity. We then consider studies that also suggest multi-timescale dynamics in intrinsic ongoing neural activity and during stimulus-driven bottom-up and cognitive top-down neural network processing in the context of MI. We propose a new concept of MI that emphasizes the critical role of neural dynamics at multiple timescales within and across brain networks, enabling the simultaneous integration, segregation, hierarchical structuring and selection of information in different time windows. To highlight predictions from our multi-timescale concept of MI, real-world scenarios in which multi-timescale processes may coordinate MI in a flexible and adaptive manner are considered.

Abnormal trial-to-trial variability in P300 time-varying directed eeg network of schizophrenia

Cognitive disturbance in identifying, processing, and responding to salient or novel stimuli are typical attributes of schizophrenia (SCH), and P300 has been proven to serve as a reliable psychosis endophenotype. The instability of neural processing across trials, i.e., trial-to-trial variability (TTV), is getting increasing attention in uncovering how the SCH "noisy" brain organizes during cognition processes. Nevertheless, the TTV in the brain network remains unrevealed, notably how it varies in different task stages. In this study, resorting to the time-varying directed electroencephalogram (EEG) network, we investigated the time-resolved TTV of the functional organizations subserving the evoking of P300. Results revealed anomalous TTV in time-varying networks across the delta, theta, alpha, beta1, and beta2 bands of SCH. The TTV of cross-band time-varying network properties can efficiently recognize SCH (accuracy: 83.39%, sensitivity: 89.22%, and specificity: 74.55%) and evaluate the psychiatric symptoms (i.e., Hamilton's depression scale-24, r = 0.430, p = 0.022, RMSE = 4.891; Hamilton's anxiety scale-14, r = 0.377, p = 0.048, RMSE = 4.575). Our study brings new insights into probing the time-resolved functional organization of the brain, and TTV in time-varying networks may provide a powerful tool for mining the substrates accounting for SCH and diagnostic evaluation of SCH.

Reward-based option competition in human dorsal stream and transition from stochastic exploration to exploitation in continuous space

Primates exploring and exploiting a continuous sensorimotor space rely on dynamic maps in the dorsal stream. Two complementary perspectives exist on how these maps encode rewards. Reinforcement learning models integrate rewards incrementally over time, efficiently resolving the exploration/exploitation dilemma. Working memory buffer models explain rapid plasticity of parietal maps but lack a plausible exploration/exploitation policy. The reinforcement learning model presented here unifies both accounts, enabling rapid, information-compressing map updates and efficient transition from exploration to exploitation. As predicted by our model, activity in human frontoparietal dorsal stream regions, but not in MT+, tracks the number of competing options, as preferred options are selectively maintained on the map, while spatiotemporally distant alternatives are compressed out. When valuable new options are uncovered, posterior β1/α oscillations desynchronize within 0.4 to 0.7 s, consistent with option encoding by competing β1-stabilized subpopulations. Together, outcomes matching locally cached reward representations rapidly update parietal maps, biasing choices toward often-sampled, rewarded options.

Cross-modal implicit learning of random time patterns

Perception is sensitive to statistical regularities in the environment, including temporal characteristics of sensory inputs. Interestingly, implicit learning of temporal patterns in one modality can also improve their processing in another modality. However, it is unclear how cross-modal learning transfer affects neural responses to sensory stimuli. Here, we recorded neural activity of human volunteers using electroencephalography (EEG), while participants were exposed to brief sequences of randomly timed auditory or visual pulses. Some trials consisted of a repetition of the temporal pattern within the sequence, and subjects were tasked with detecting these trials. Unknown to the participants, some trials reappeared throughout the experiment across both modalities (Transfer) or only within a modality (Control), enabling implicit learning in one modality and its transfer. Using a novel method of analysis of single-trial EEG responses, we showed that learning temporal structures within and across modalities is reflected in neural learning curves. These putative neural correlates of learning transfer were similar both when temporal information learned in audition was transferred to visual stimuli and vice versa. The modality-specific mechanisms for learning of temporal information and general mechanisms which mediate learning transfer across modalities had distinct physiological signatures: temporal learning within modalities relied on modality-specific brain regions while learning transfer affected beta-band activity in frontal regions.

Cognitive neuroscience perspective on memory: overview and summary

This paper explores memory from a cognitive neuroscience perspective and examines associated neural mechanisms. It examines the different types of memory: working, declarative, and non-declarative, and the brain regions involved in each type. The paper highlights the role of different brain regions, such as the prefrontal cortex in working memory and the hippocampus in declarative memory. The paper also examines the mechanisms that underlie the formation and consolidation of memory, including the importance of sleep in the consolidation of memory and the role of the hippocampus in linking new memories to existing cognitive schemata. The paper highlights two types of memory consolidation processes: cellular consolidation and system consolidation. Cellular consolidation is the process of stabilizing information by strengthening synaptic connections. System consolidation models suggest that memories are initially stored in the hippocampus and are gradually consolidated into the neocortex over time. The consolidation process involves a hippocampal-neocortical binding process incorporating newly acquired information into existing cognitive schemata. The paper highlights the role of the medial temporal lobe and its involvement in autobiographical memory. Further, the paper discusses the relationship between episodic and semantic memory and the role of the hippocampus. Finally, the paper underscores the need for further research into the neurobiological mechanisms underlying non-declarative memory, particularly conditioning. Overall, the paper provides a comprehensive overview from a cognitive neuroscience perspective of the different processes involved in memory consolidation of different types of memory.

Isometric Arm Forces Exerted by Females at Different Levels of Physical Comfort and Their EEG Signatures

A variety of subjective measures have traditionally been used to assess the perception of physical exertion at work and related body responses. However, the current understanding of physical comfort experienced at work is very limited. The main objective of this study was first to investigate the magnitude of isometric arm forces exerted by females at different levels of physical comfort measured on a new comfort scale and, second, to assess their corresponding neural signatures expressed in terms of power spectral density (PSD). The study assessed PSDs of four major electroencephalography (EEG) frequency bands, focusing on the brain regions controlling motor and perceptual processing. The results showed statistically significant differences in exerted arm forces and the rate of perceived exertion at the various levels of comfort. Significant differences in power spectrum density at different physical comfort levels were found for the beta EEG band. Such knowledge can be useful in incorporating female users' force requirements in the design of consumer products, including tablets, laptops, and other hand-held information technology devices, as well as various industrial processes and work systems.

Parietal alpha underlies slower cognitive responses during interference processing in adolescents

Adolescence is a critical period when cognitive control is rapidly maturing across several core dimensions. Here, we evaluated how healthy adolescents (13-17 years of age, n = 44) versus young adults (18-25 years of age, n = 49) differ across a series of cognitive assessments with simultaneous electroencephalography (EEG) recordings. Cognitive tasks included selective attention, inhibitory control, working memory, as well as both non-emotional and emotional interference processing. We found that adolescents displayed significantly slower responses than young adults specifically on the interference processing tasks. Evaluation of EEG event-related spectral perturbations (ERSPs) on the interference tasks showed that adolescents consistently had greater event-related desynchronization in alpha/beta frequencies in parietal regions. Midline frontal theta activity was also greater in the flanker interference task in adolescents, suggesting greater cognitive effort. Parietal alpha activity predicted age-related speed differences during non-emotional flanker interference processing, and frontoparietal connectivity, specifically midfrontal theta - parietal alpha functional connectivity predicted speed effects during emotional interference. Overall, our neuro-cognitive results illustrate developing cognitive control in adolescents particularly for interference processing, predicted by differential alpha band activity and connectivity in parietal brain regions.

Principles of large-scale neural interactions

What mechanisms underlie flexible inter-areal communication in the cortex? We consider four mechanisms for temporal coordination and their contributions to communication: (1) Oscillatory synchronization (communication-through-coherence); (2) communication-through-resonance; (3) non-linear integration; and (4) linear signal transmission (coherence-through-communication). We discuss major challenges for communication-through-coherence based on layer- and cell-type-specific analyses of spike phase-locking, heterogeneity of dynamics across networks and states, and computational models for selective communication. We argue that resonance and non-linear integration are viable alternative mechanisms that facilitate computation and selective communication in recurrent networks. Finally, we consider communication in relation to cortical hierarchy and critically examine the hypothesis that feedforward and feedback communication use fast (gamma) and slow (alpha/beta) frequencies, respectively. Instead, we propose that feedforward propagation of prediction errors relies on the non-linear amplification of aperiodic transients, whereas gamma and beta rhythms represent rhythmic equilibrium states that facilitate sustained and efficient information encoding and amplification of short-range feedback via resonance.

Interacting rhythms enhance sensitivity of target detection in a fronto-parietal computational model of visual attention

Even during sustained attention, enhanced processing of attended stimuli waxes and wanes rhythmically, with periods of enhanced and relatively diminished visual processing (and subsequent target detection) alternating at 4 or 8 Hz in a sustained visual attention task. These alternating attentional states occur alongside alternating dynamical states, in which lateral intraparietal cortex (LIP), the frontal eye field (FEF), and the mediodorsal pulvinar (mdPul) exhibit different activity and functional connectivity at α, β, and γ frequencies-rhythms associated with visual processing, working memory, and motor suppression. To assess whether and how these multiple interacting rhythms contribute to periodicity in attention, we propose a detailed computational model of FEF and LIP. When driven by θ-rhythmic inputs simulating experimentally-observed mdPul activity, this model reproduced the rhythmic dynamics and behavioral consequences of observed attentional states, revealing that the frequencies and mechanisms of the observed rhythms allow for peak sensitivity in visual target detection while maintaining functional flexibility.

Large-scale frontoparietal theta, alpha, and beta phase synchronization: A set of EEG differential characteristics for freezing of gait in Parkinson's disease?

Freezing of gait (FOG) is a complex gait disturbance in Parkinson's disease (PD), during which the patient is not able to effectively initiate gait or continue walking. The mystery of the FOG phenomenon is still unsolved. Recent studies have revealed abnormalities in cortical activities associated with FOG, which highlights the importance of cortical and cortical-subcortical network dysfunction in PD patients with FOG. In this paper, phase-locking value (PLV) of eight frequency sub-bands between 0.05 Hz and 35 Hz over frontal, motor, and parietal areas [during an ankle dorsiflexion (ADF) task] is used to investigate EEG phase synchronization. PLV was investigated over both superficial and deeper networks by analyzing EEG signals preprocessed with and without Surface Laplacian (SL) spatial filter. Four groups of participants were included: PD patients with severe FOG (N = 5, 5 males), PD patients with mild FOG (N = 7, 6 males), PD patients without FOG (N = 14, 13 males), and healthy age-matched controls (N = 13, 10 males). Fifteen trials were recorded from each participant. At superficial layers, frontoparietal theta phase synchrony was a unique feature present in PD with FOG groups. At deeper networks, significant dominance of interhemispheric frontoparietal alpha phase synchrony in PD with FOG, in contrast to beta phase synchrony in PD without FOG, was identified. Alpha phase synchrony was more distributed in PD with severe FOG, with higher levels of frontoparietal alpha phase synchrony. In addition to FOG-related abnormalities in PLV analysis, phase-amplitude coupling (PAC) analysis was also performed on frequency bands with PLV abnormalities. PAC analysis revealed abnormal coupling between theta and low beta frequency bands in PD with severe FOG at the superficial layers over frontal areas. At deeper networks, theta and alpha frequency bands show high PAC over parietal areas in PD with severe FOG. Alpha and low beta also presented PAC over frontal areas in PD groups with FOG. The results introduced significant phase synchrony differences between PD with and without FOG and provided important insight into a possible unified underlying mechanism for FOG. These results thus suggest that PLV and PAC can potentially be used as EEG-based biomarkers for FOG.

Connectivity-Defined Subdivisions of the Intraparietal Sulcus Respond Differentially to Abstraction during Decision-Making

The intraparietal sulcus (IPS) has been implicated in numerous functions that range from representation of visual stimuli to action planning, but its role in abstract decision-making has been unclear, in part because low-level functions often act as confounds. Here, we address this problem using a task that dissociates abstract decision-making from sensory salience, attentional control, motor planning, and motor output. Functional MRI data were collected from healthy female and male human subjects while they performed a policy abstraction task requiring use of a more abstract (second-order) rule to select between two less abstract (first-order) rules that informed the motor response. By identifying IPS subdivisions with preferential connectivity to prefrontal regions that are differentially responsive to task abstraction, we found that a caudal IPS (cIPS) subregion with strongest connectivity to the pre-premotor cortex was preferentially active for second-order cues, whereas a rostral IPS subregion (rIPS) with strongest connectivity to the dorsal premotor cortex was active during attentional control over first-order cues. These effects for abstraction were seen in addition to cIPS activity that was specific to sensory salience, and rIPS activity that was specific to motor output. Notably, topographic responses to the second-order cue were detected along the caudal-rostral axis of IPS, mirroring the broader organization seen in lateral prefrontal cortex. Together, these data demonstrate that subregions within IPS exhibit activity responsive to policy abstraction, and they suggest that IPS may be organized into frontoparietal subnetworks that support hierarchical cognitive control.SIGNIFICANCE STATEMENT Abstract decision-making allows us to flexibly adapt our behavior to new contexts. Although much previous work has focused on the role of lateral prefrontal cortex in such decisions, the contributions of parietal cortex have been relatively understudied. Here, we demonstrate that spatially segregated subregions of human IPS with strong functional connections to lateral prefrontal cortex demonstrate activity selective for abstract decisions. This activity can be distinguished from responses resulting from cognitive processes related to sensory salience, attentional control, motor planning, and movement. Together, these findings indicate that different task demands are reflected in the topography of IPS, and they explicitly reveal a role in abstract decision-making.

Prefrontal cortical plasticity during learning of cognitive tasks

Training in working memory tasks is associated with lasting changes in prefrontal cortical activity. To assess the neural activity changes induced by training, we recorded single units, multi-unit activity (MUA) and local field potentials (LFP) with chronic electrode arrays implanted in the prefrontal cortex of two monkeys, throughout the period they were trained to perform cognitive tasks. Mastering different task phases was associated with distinct changes in neural activity, which included recruitment of larger numbers of neurons, increases or decreases of their firing rate, changes in the correlation structure between neurons, and redistribution of power across LFP frequency bands. In every training phase, changes induced by the actively learned task were also observed in a control task, which remained the same across the training period. Our results reveal how learning to perform cognitive tasks induces plasticity of prefrontal cortical activity, and how activity changes may generalize between tasks.

Neonatal Isoflurane Does Not Affect Sleep Architecture and Minimally Alters Neuronal Beta Oscillations in Adolescent Rats

General anesthetics are neurotoxic to the developing rodent and primate brains leading to neurocognitive and socio-affective impairment later in life. In addition, sleep patterns are important predictors of cognitive outcomes. Yet, little is known about how anesthetics affect sleep-wake behaviors and their corresponding oscillations. Here we examine how neonatal general anesthesia affects sleep and wake behavior and associated neuronal oscillations. We exposed male and female rat pups to either 6 h of continuous isoflurane or sham anesthesia (compressed air) at the peak of their brain development (postnatal day 7). One cohort of animals was used to examine neurotoxic insult 2 h post-anesthesia exposure. At weaning age, a second cohort of rats was implanted with cortical electroencephalogram electrodes and allowed to recover. During adolescence, we measured sleep architecture (divided into wake, non-rapid eye movement, and rapid eye movement sleep) and electroencephalogram power spectra over a 24 h period. We found that exposure to neonatal isoflurane caused extensive neurotoxicity but did not disrupt sleep architecture in adolescent rats. However, these animals had a small but significant reduction in beta oscillations, specifically in the 12-20 Hz beta 1 range, associated with wake behavior. Furthermore, beta oscillations play a critical role in cortical development, cognitive processing, and homeostatic sleep drive. We speculate that dysregulation of beta oscillations may be implicated in cognitive and socio-affective outcomes associated with neonatal anesthesia.

Neural Dynamics in Primate Cortex during Exposure to Subanesthetic Concentrations of Nitrous Oxide

Nitrous oxide (N₂O) is a hypnotic gas with antidepressant and psychedelic properties at subanesthetic concentrations. Despite long-standing clinical use, there is insufficient understanding of its effect on neural dynamics and cortical processing, which is important for mechanistic understanding of its therapeutic effects. We administered subanesthetic (70%), inhaled N₂O and studied the dynamic changes of spiking rate, spectral content, and somatosensory information representation in primary motor cortex (M1) in two male rhesus macaques implanted with Utah microelectrode arrays in the hand area of M1. The average sorted multiunit spiking rate in M1 increased from 8.1 ± 0.99 to 10.6 ± 1.3 Hz in Monkey W (p < 0.001) and from 5.6 ± 0.87 to 7.0 ± 1.1 Hz in Monkey N (p = 0.003). Power spectral densities increased in beta- and gamma-band power. To evaluate somatosensory content in M1 as a surrogate of information transfer, fingers were lightly brushed and classified using a naive Bayes classifier. In both monkeys, the proportion of correctly classified fingers dropped from 0.50 ± 0.06 before N₂O inhalation to 0.34 ± 0.03 during N₂O inhalation (p = 0.018), although some fingers continued to be correctly classified (p = 0.005). The decrease in correct classifications corresponded to decreased modulation depth for the population (p = 0.005) and fewer modulated units (p = 0.046). However, the increased single-unit firing rate was not correlated with its modulation depth (R² < 0.001, p = 0.93). These data suggest that N₂O degrades information transfer, although no clear relationship was found between neuronal tuning and N₂O-induced changes in firing rate.

Impaired reach-to-grasp kinematics in parkinsonian patients relates to dopamine-dependent, subthalamic beta bursts

Excessive beta-band oscillations in the subthalamic nucleus are key neural features of Parkinson’s disease. Yet the distinctive contributions of beta low and high bands, their dependency on striatal dopamine, and their correlates with movement kinematics are unclear. Here, we show that the movement phases of the reach-to-grasp motor task are coded by the subthalamic bursting activity in a maximally-informative beta high range. A strong, three-fold correlation linked beta high range bursts, imbalanced inter-hemispheric striatal dopaminergic tone, and impaired inter-joint movement coordination. These results provide new insight into the neural correlates of motor control in parkinsonian patients, paving the way for more informative use of beta-band features for adaptive deep brain stimulation devices.

Frontoparietal Beta Amplitude Modulation and its Interareal Cross-frequency Coupling in Visual Working Memory

Visual working memory (VWM) relies on sustained neural activities that code information via various oscillatory frequencies. Previous studies, however, have emphasized time-frequency power changes, while overlooking the possibility that rhythmic amplitude variations can also code frequency-specific VWM information in a completely different dimension. Here, we employed the recently-developed Holo-Hilbert spectral analysis to characterize such nonlinear amplitude modulation(s) (AM) underlying VWM in the frontoparietal systems. We found that the strength of AM in mid-frontal beta and gamma oscillations during late VWM maintenance and VWM retrieval correlated with people's VWM performance. When behavioral performance was altered with transcranial electric stimulation, AM power changes during late VWM maintenance in beta, but not gamma, tracked participants' VWM variations. This beta AM likely codes information by varying its amplitude in theta period for long-range propagation, as our connectivity analysis revealed that interareal theta-beta couplings-bidirectional between mid-frontal and right-parietal during VWM maintenance and unidirectional from right-parietal to left-middle-occipital during late VWM maintenance and retrieval-underpins VWM performance and individual differences.

Maintaining verbal short-term memory representations in non-perceptual parietal regions

Buffer accounts of verbal short-term memory (STM) assume dedicated buffers for maintaining different types of information (e.g., phonological, visual) whereas embedded processes accounts argue against the existence of buffers and claim that STM consists of the activated portion of long-term memory (LTM). We addressed this debate by determining whether STM recruits the same neural substrate as LTM, or whether additional regions are involved in short-term storage. Using fMRI with representational similarity analysis (RSA), we examined the representational correspondence of multi-voxel neural activation patterns with the theoretical predictions for the maintenance of both phonological and semantic codes in STM. We found that during the delay period of a phonological STM task, phonological representations could be decoded in the left supramarginal gyrus (SMG) but not the superior temporal gyrus (STG), a speech processing region, for word stimuli. Whereas the pattern in the SMG was specific to phonology, a different region in the left angular gyrus showed RSA decoding evidence for the retention of either phonological or semantic codes, depending on the task context. Taken together, the results provide clear support for a dedicated buffer account of phonological STM, although evidence for a semantic buffer is equivocal.

EEG Correlates of Cognitive Functions and Neuropsychiatric Disorders: A Review of Oscillatory Activity and Neural Synchrony Abnormalities

Background:

A large body of evidence suggested that disruption of neural rhythms and synchronization of brain oscillations are correlated with a variety of cognitive and perceptual processes. Cognitive deficits are common features of psychiatric disorders that complicate treatment of the motivational, affective and emotional symptoms.

Objective:

Electrophysiological correlates of cognitive functions will contribute to understanding of neural circuits controlling cognition, the causes of their perturbation in psychiatric disorders and developing novel targets for the treatment of cognitive impairments.

Methods:

This review includes a description of brain oscillations in Alzheimer’s disease, bipolar disorder, attention-deficit/hyperactivity disorder, major depression, obsessive compulsive disorders, anxiety disorders, schizophrenia and autism.

Results:

The review clearly shows that the reviewed neuropsychiatric diseases are associated with fundamental changes in both spectral power and coherence of EEG oscillations.

Conclusion:

In this article, we examined the nature of brain oscillations, the association of brain rhythms with cognitive functions and the relationship between EEG oscillations and neuropsychiatric diseases. Accordingly, EEG oscillations can most likely be used as biomarkers in psychiatric disorders.

Layer and rhythm specificity for predictive routing

An established theoretical model, predictive coding, states that the brain is constantly building models (signifying changing predictions) of the environment. The brain does this by forming predictions and signaling sensory inputs which deviate from predictions (“prediction errors”). Various hypotheses exist about how predictive coding could be implemented in the brain. We recorded neural spiking and oscillations with laminar resolution in a network of cortical areas as monkeys performed a working memory task with changing stimulus predictability. Predictability modulated the patterns of feedforward/feedback flow, cortical layers, and oscillations used to process a visual stimulus. These data support the theory of predictive coding but suggest an alternate model for its neural implementation: predictive routing.

In predictive coding, experience generates predictions that attenuate the feeding forward of predicted stimuli while passing forward unpredicted “errors.” Different models have suggested distinct cortical layers, and rhythms implement predictive coding. We recorded spikes and local field potentials from laminar electrodes in five cortical areas (visual area 4 [V4], lateral intraparietal [LIP], posterior parietal area 7A, frontal eye field [FEF], and prefrontal cortex [PFC]) while monkeys performed a task that modulated visual stimulus predictability. During predictable blocks, there was enhanced alpha (8 to 14 Hz) or beta (15 to 30 Hz) power in all areas during stimulus processing and prestimulus beta (15 to 30 Hz) functional connectivity in deep layers of PFC to the other areas. Unpredictable stimuli were associated with increases in spiking and in gamma-band (40 to 90 Hz) power/connectivity that fed forward up the cortical hierarchy via superficial-layer cortex. Power and spiking modulation by predictability was stimulus specific. Alpha/beta power in LIP, FEF, and PFC inhibited spiking in deep layers of V4. Area 7A uniquely showed increases in high-beta (∼22 to 28 Hz) power/connectivity to unpredictable stimuli. These results motivate a conceptual model, predictive routing. It suggests that predictive coding may be implemented via lower-frequency alpha/beta rhythms that “prepare” pathways processing-predicted inputs by inhibiting feedforward gamma rhythms and associated spiking.

Interactions of multiple rhythms in a biophysical network of neurons

Neural oscillations, including rhythms in the beta1 band (12-20 Hz), are important in various cognitive functions. Often neural networks receive rhythmic input at frequencies different from their natural frequency, but very little is known about how such input affects the network's behavior. We use a simplified, yet biophysical, model of a beta1 rhythm that occurs in the parietal cortex, in order to study its response to oscillatory inputs. We demonstrate that a cell has the ability to respond at the same time to two periodic stimuli of unrelated frequencies, firing in phase with one, but with a mean firing rate equal to that of the other. We show that this is a very general phenomenon, independent of the model used. We next show numerically that the behavior of a different cell, which is modeled as a high-dimensional dynamical system, can be described in a surprisingly simple way, owing to a reset that occurs in the state space when the cell fires. The interaction of the two cells leads to novel combinations of properties for neural dynamics, such as mode-locking to an input without phase-locking to it.

Phase of firing coding of learning variables across the fronto-striatal network during feature-based learning

The prefrontal cortex and striatum form a recurrent network whose spiking activity encodes multiple types of learning-relevant information. This spike-encoded information is evident in average firing rates, but finer temporal coding might allow multiplexing and enhanced readout across the connected network. We tested this hypothesis in the fronto-striatal network of nonhuman primates during reversal learning of feature values. We found that populations of neurons encoding choice outcomes, outcome prediction errors, and outcome history in their firing rates also carry significant information in their phase-of-firing at a 10–25 Hz band-limited beta frequency at which they synchronize across lateral prefrontal cortex, anterior cingulate cortex and anterior striatum when outcomes were processed. The phase-of-firing code exceeds information that can be obtained from firing rates alone and is evident for inter-areal connections between anterior cingulate cortex, lateral prefrontal cortex and anterior striatum. For the majority of connections, the phase-of-firing information gain is maximal at phases of the beta cycle that were offset from the preferred spiking phase of neurons. Taken together, these findings document enhanced information of three important learning variables at specific phases of firing in the beta cycle at an inter-areally shared beta oscillation frequency during goal-directed behavior.

The average spiking frequency in the fronto-striatal network encodes multiple types of learning-relevant information. Here, the authors show that populations of neurons in non-human primates also carry significant information in their phase-of-firing when learning-relevant outcomes are processed.

Temporal codes of visual working memory in the human cerebral cortex: Brain rhythms associated with high memory capacity

Visual working memory (vWM) is an important ability required for various cognitive tasks although its neural underpinnings remain unclear. While many studies have focused on theta (4-7 Hz) and gamma (> 30 Hz) rhythms as a substrate of vWM, here we show that temporal signals embedded in alpha (8-12 Hz) and beta (13-30 Hz) bands can be a good predictor of vWM capacity. Neural activity of healthy human participants was recorded with magnetoencephalography when they performed a classical vWM task (change detection). We analyzed changes in inter-peak intervals (IPIs) of oscillatory signals along with an increase in WM load (a number of to-be-memorized items, 1-6). Results showed a load-dependent reduction of IPIs in the parietal and frontal regions, indicating that alpha/beta rhythms became faster when multiple items were stored in vWM. Furthermore, this reduction in IPIs was positively correlated with individual vWM capacity, especially in the frontal cortex. Those results indicate that vWM is represented as a change in oscillation frequency in the human cerebral cortex.