Gamma Cycle Research Articles

Enhancing memory capacity by experimentally slowing theta frequency oscillations using combined EEG-tACS

The coupling of gamma oscillation (~ 40+ Hz) amplitude to the phase of ongoing theta (~ 6 Hz) oscillations has been proposed to be directly relevant for memory performance. Current theories suggest that memory capacity scales with number of gamma cycles that can be fitted into the preferred phase of a theta cycle. Following this logic, transcranial alternating current stimulation (tACS) may be used to adjust theta cycles (increasing/decreasing theta frequency) to decrease or increase memory performance during stimulation. Here, we used individualized EEG-informed theta tACS to (1) experimentally “slow down” individual theta frequency (ITF), (2) evaluate cognitive after effects on a battery of memory and learning tasks, and (3) link the cognitive performance changes to tACS-induced effects on theta-band oscillations as measured by post EEG. We found frequency- and task-specific tACS after effects demonstrating a specific enhancement in memory capacity. This tACS-induced cognitive enhancement was specific to the visual memory task performed immediately after tACS offset, and specific to the ITF-1 Hz (slowing) stimulation condition and thus following a protocol specifically designed to slow down theta frequency to enhance memory capacity. Follow-up correlation analyses in this group linked the enhanced memory performance to increased left frontal-parietal theta-band connectivity. Interestingly, resting-state theta power immediately after tACS offset revealed a theta power increase not for the ITF-1 Hz group, but only for the ITF group where the tACS frequency was ‘optimal’ for entrainment. These results suggest that while individually calibrated tACS at peak frequency maximally modulates resting-state oscillatory power, tACS stimulation slightly below this optimal peak theta frequency is better suited to enhance memory capacity performance. Importantly, our results further suggest that such cognitive enhancement effects can last beyond the period of stimulation and are linked to increased network connectivity, opening the door towards more clinical and applied relevance of using tACS in cognitive rehabilitation and/or neurocognitive enhancement.

80 Hz but not 40 Hz, transcranial alternating current stimulation of 80 Hz over right intraparietal sulcus increases visuospatial working memory capacity

Working memory (WM) is a complex cognitive function involved in the temporary storage and manipulation of information, which has been one of the target cognitive functions to be restored in neurorehabilitation. WM capacity is known to be proportional to the number of gamma cycles nested in a single theta cycle. Therefore, gamma-band transcranial alternating current stimulation (tACS) should be dependent of the stimulation frequency; however, the results of previous studies that employed 40 Hz tACS have not been consistent. The optimal locations and injection currents of multiple scalp electrodes were determined based on numerical simulations of electric field. Experiments were conducted with 20 healthy participants. The order of three stimulation conditions (40 Hz tACS, 80 Hz tACS, and sham stimulation) were randomized but counterbalanced. Visual hemifield-specific visual WM capacity was assessed using a delayed visual match to the sample task. High gamma tACS significantly increased WM capacity, while low gamma tACS had no significant effect. Notably, 80 Hz tACS increased WM capacity on both the left and right visual hemifields, while previous tACS studies only reported the effects of tACS on contralateral hemifields. This is the first study to investigate the frequency-dependent effect of gamma-band tACS on WM capacity. Our findings also suggest that high gamma tACS might influence not only WM capacity but also communication between interhemispheric cortical regions. It is expected that high gamma tACS could be a promising neurorehabilitation method to enhance higher-order cognitive functions with similar mechanisms.

Phase-locking patterns underlying effective communication in exact firing rate models of neural networks.

Macroscopic oscillations in the brain have been observed to be involved in many cognitive tasks but their role is not completely understood. One of the suggested functions of the oscillations is to dynamically modulate communication between neural circuits. The Communication Through Coherence (CTC) theory proposes that oscillations reflect rhythmic changes in excitability of the neuronal populations. Thus, populations need to be properly phase-locked so that input volleys arrive at the peaks of excitability of the receiving population to communicate effectively. Here, we present a modeling study to explore synchronization between neuronal circuits connected with unidirectional projections. We consider an Excitatory-Inhibitory (E-I) network of quadratic integrate-and-fire neurons modeling a Pyramidal-Interneuronal Network Gamma (PING) rhythm. The network receives an external periodic input from either one or two sources, simulating the inputs from other oscillating neural groups. We use recently developed mean-field models which provide an exact description of the macroscopic activity of the spiking network. This low-dimensional mean field model allows us to use tools from bifurcation theory to identify the phase-locked states between the input and the target population as a function of the amplitude, frequency and coherence of the inputs. We identify the conditions for optimal phase-locking and effective communication. We find that inputs with high coherence can entrain the network for a wider range of frequencies. Besides, faster oscillatory inputs than the intrinsic network gamma cycle show more effective communication than inputs with similar frequency. Our analysis further shows that the entrainment of the network by inputs with higher frequency is more robust to distractors, thus giving them an advantage to entrain the network and communicate effectively. Finally, we show that pulsatile inputs can switch between attended inputs in selective attention.

Shape analysis of gamma rhythm supports a superlinear inhibitory regime in an inhibition-stabilized network.

Visual inspection of stimulus-induced gamma oscillations (30-70 Hz) often reveals a non-sinusoidal shape. Such distortions are a hallmark of non-linear systems and are also observed in mean-field models of gamma oscillations. A thorough characterization of the shape of the gamma cycle can therefore provide additional constraints on the operating regime of such models. However, the gamma waveform has not been quantitatively characterized, partially because the first harmonic of gamma, which arises because of the non-sinusoidal nature of the signal, is typically weak and gets masked due to a broadband increase in power related to spiking. To address this, we recorded local field potential (LFP) from the primary visual cortex (V1) of two awake female macaques while presenting full-field gratings or iso-luminant chromatic hues that produced huge gamma oscillations with prominent peaks at harmonic frequencies in the power spectra. We found that gamma and its first harmonic always maintained a specific phase relationship, resulting in a distinctive shape with a sharp trough and a shallow peak. Interestingly, a Wilson-Cowan (WC) model operating in an inhibition stabilized mode could replicate this shape, but only when the inhibitory population operated in the super-linear regime, as predicted recently. However, another recently developed model of gamma that operates in a linear regime driven by stochastic noise failed to produce salient harmonics or the observed shape. Our results impose additional constraints on models that generate gamma oscillations and their operating regimes.

Working Memory and Transcranial-Alternating Current Stimulation-State of the Art: Findings, Missing, and Challenges.

Working memory (WM) is a cognitive process that involves maintaining and manipulating information for a short period of time. WM is central to many cognitive processes and declines rapidly with age. Deficits in WM are seen in older adults and in patients with dementia, schizophrenia, major depression, mild cognitive impairment, Alzheimer’s disease, etc. The frontal, parietal, and occipital cortices are significantly involved in WM processing and all brain oscillations are implicated in tackling WM tasks, particularly theta and gamma bands. The theta/gamma neural code hypothesis assumes that retained memory items are recorded via theta-nested gamma cycles. Neuronal oscillations can be manipulated by sensory, invasive- and non-invasive brain stimulations. Transcranial alternating-current stimulation (tACS) and repetitive transcranial magnetic stimulation (rTMS) are frequency-tuned non-invasive brain stimulation (NIBS) techniques that have been used to entrain endogenous oscillations in a frequency-specific manner. Compared to rTMS, tACS demonstrates superior cost, tolerability, portability, and safety profile, making it an attractive potential tool for improving cognitive performance. Although cognitive research with tACS is still in its infancy compared to rTMS, a number of studies have shown a promising WM enhancement effect, especially in the elderly and patients with cognitive deficits. This review focuses on the various methods and outcomes of tACS on WM in healthy and unhealthy human adults and highlights the established findings, unknowns, challenges, and perspectives important for translating laboratory tACS into realistic clinical settings. This will allow researchers to identify gaps in the literature and develop frequency-tuned tACS protocols with promising safety and efficacy outcomes. Therefore, research efforts in this direction should help to consider frequency-tuned tACS as a non-pharmacological tool of cognitive rehabilitation in physiological aging and patients with cognitive deficits.

Is short-term memory capacity (7±2) really predicted by theta to gamma cycle length ratio?

Several studies suggest that EEG parameters, reflecting top-down processes in the brain, may predict cognitive performance, e.g. short-term memory (STM) capacity. According to Lisman and Idiart’s model, STM capacity is predicted by theta and gamma EEG waves and their ratio. This model suggests that the more periods of gamma band waves fit into one period of theta band waves, the more information can be stored. We replicated the study by Kaminski et al. (2011), which recorded spontaneous EEG activity and measured verbal STM capacity with a modified digit span task from the Wechsler battery. Our study included more subjects and two EEG recording sessions. We discuss the possible limits of EEG correlates of STM capacity as EEG parameters were not stable across the two measurements and no correlation was found between the theta/gamma ratio and performance in the digit span task.

Movement-related coupling of human subthalamic nucleus spikes to cortical gamma.

Cortico-basal ganglia interactions continuously shape the way we move. Ideas about how this circuit works are based largely on models those consider only firing rate as the mechanism of information transfer. A distinct feature of neural activity accompanying movement, however, is increased motor cortical and basal ganglia gamma synchrony. To investigate the relationship between neuronal firing in the basal ganglia and cortical gamma activity during movement, we analysed human ECoG and subthalamic nucleus (STN) unit activity during hand gripping. We found that fast reaction times were preceded by enhanced STN spike-to-cortical gamma phase coupling, indicating a role in motor preparation. Importantly, increased gamma phase coupling occurred independent of changes in mean STN firing rates, and the relative timing of STN spikes was offset by half a gamma cycle for ipsilateral vs. contralateral movements, indicating that relative spike timing is as relevant as firing rate for understanding cortico-basal ganglia information transfer.

The speed of parietal theta frequency drives visuospatial working memory capacity.

The speed of theta brain oscillatory activity is thought to play a key role in determining working memory (WM) capacity. Individual differences in the length of a theta cycle (ranging between 4 and 7 Hz) might determine how many gamma cycles (>30 Hz) can be nested into a theta wave. Gamma cycles are thought to represent single memory items; therefore, this interplay could determine individual memory capacity. We directly tested this hypothesis by means of parietal transcranial alternating current stimulation (tACS) set at slower (4 Hz) and faster (7 Hz) theta frequencies during a visuospatial WM paradigm. Accordingly, we found that 4-Hz tACS enhanced WM capacity, while 7-Hz tACS reduced WM capacity. Notably, these effects were found only for items presented to the hemifield contralateral to the stimulation site. This provides causal evidence for a frequency-dependent and spatially specific organization of WM storage, supporting the theta–gamma phase coupling theory of WM capacity.

P187 Individual differences in parietal theta frequency drive spatial working memory capacity

The speed of fronto-parietal theta oscillatory activity is thought to play a key role in determining working memory (WM) capacity. Individual differences in the length of a theta cycle (ranging between 4 Hz and 7 Hz) might act as an envelope for nested gamma cycles (>30 Hz). This would provide the fundamental neurophysiological mechanism for working memory storage accounting for individual differences in the maximum number of items (number of gamma cycles) retained in the working memory buffer (one theta cycle). Accordingly, it would be expected that slower theta frequencies bind a higher number of gamma cycles per memory buffer resulting in increased memory capacity. Conversely, faster theta frequencies would bind a comparatively smaller number of nested gamma cycles, corresponding to decreased memory capacity. We directly tested this hypothesis by means of right parietal transcranial Alternating Current Stimulation (tACS). Specifically, we tested for the impact of down-regulation (4 Hz tACS) and up-regulation (7 Hz tACS) of theta oscillations in a spatial WM paradigm. To this aim eleven participants underwent 4 Hz, 7 Hz and sham stimulation while performing a variation of the Sternberg task. In line with our hypotheses, results showed that down-regulation of right parietal theta by 4 Hz tACS enhanced WM capacity while up-regulation of right parietal theta frequency by 7 Hz tACS reduced WM capacity compared to sham stimulation. These effects were found only for items presented to the left hemifield, i.e. contralateral to the stimulation site, thus providing causal evidence for frequency and spatially tuned WM capacity organization. In conclusion, we provide the first causal evidence that inducing slower parietal theta frequency activity results in higher working memory capacity, while inducing faster parietal theta frequency activity results in reduced working memory capacity, in line with the theta-gamma phase coupling theory of WM capacity.

Theta-Gamma Coding Meets Communication-through-Coherence: Neuronal Oscillatory Multiplexing Theories Reconciled.

Several theories have been advanced to explain how cross-frequency coupling, the interaction of neuronal oscillations at different frequencies, could enable item multiplexing in neural systems. The communication-through-coherence theory proposes that phase-matching of gamma oscillations between areas enables selective processing of a single item at a time, and a later refinement of the theory includes a theta-frequency oscillation that provides a periodic reset of the system. Alternatively, the theta-gamma neural code theory proposes that a sequence of items is processed, one per gamma cycle, and that this sequence is repeated or updated across theta cycles. In short, both theories serve to segregate representations via the temporal domain, but differ on the number of objects concurrently represented. In this study, we set out to test whether each of these theories is actually physiologically plausible, by implementing them within a single model inspired by physiological data. Using a spiking network model of visual processing, we show that each of these theories is physiologically plausible and computationally useful. Both theories were implemented within a single network architecture, with two areas connected in a feedforward manner, and gamma oscillations generated by feedback inhibition within areas. Simply increasing the amplitude of global inhibition in the lower area, equivalent to an increase in the spatial scope of the gamma oscillation, yielded a switch from one mode to the other. Thus, these different processing modes may co-exist in the brain, enabling dynamic switching between exploratory and selective modes of attention.

Gamma band directional interactions between basal forebrain and visual cortex during wake and sleep states

The basal forebrain (BF) is an important regulator of cortical excitability and responsivity to sensory stimuli, and plays a major role in wake-sleep regulation. While the impact of BF on cortical EEG or LFP signals has been extensively documented, surprisingly little is known about LFP activity within BF. Based on bilateral recordings from rats in their home cage, we describe endogenous LFP oscillations in the BF during quiet wakefulness, rapid eye movement (REM) and slow wave sleep (SWS) states. Using coherence and Granger causality methods, we characterize directional influences between BF and visual cortex (VC) during each of these states. We observed pronounced BF gamma activity particularly during wakefulness, as well as to a lesser extent during SWS and REM. During wakefulness, this BF gamma activity exerted a directional influence on VC that was associated with cortical excitation. During SWS but not REM, there was also a robust directional gamma band influence of BF on VC. In all three states, directional influence in the gamma band was only present in BF to VC direction and tended to be regulated specifically within each brain hemisphere. Locality of gamma band LFPs to the BF was confirmed by demonstration of phase locking of local spiking activity to the gamma cycle. We report novel aspects of endogenous BF LFP oscillations and their relationship to cortical LFP signals during sleep and wakefulness. We link our findings to known aspects of GABAergic BF networks that likely underlie gamma band LFP activations, and show that the Granger causality analyses can faithfully recapitulate many known attributes of these networks.

A biologically plausible mechanism for neuronal coding organized by the phase of alpha oscillations

The visual system receives a wealth of sensory information of which only little is relevant for behaviour. We present a mechanism in which alpha oscillations serve to prioritize different components of visual information. By way of simulated neuronal networks, we show that inhibitory modulation in the alpha range (~ 10 Hz) can serve to temporally segment the visual information to prevent information overload. Coupled excitatory and inhibitory neurons generate a gamma rhythm in which information is segmented and sorted according to excitability in each alpha cycle. Further details are coded by distributed neuronal firing patterns within each gamma cycle. The network model produces coupling between alpha phase and gamma (40–100 Hz) amplitude in the simulated local field potential similar to that observed experimentally in human and animal recordings.

Dopamine D3 Receptors Inhibit Hippocampal Gamma Oscillations by Disturbing CA3 Pyramidal Cell Firing Synchrony.

Cortical gamma oscillations are associated with cognitive processes and are altered in several neuropsychiatric conditions such as schizophrenia and Alzheimer’s disease. Since dopamine D3 receptors are possible targets in treatment of these conditions, it is of great importance to understand their role in modulation of gamma oscillations. The effect of D3 receptors on gamma oscillations and the underlying cellular mechanisms were investigated by extracellular local field potential and simultaneous intracellular sharp micro-electrode recordings in the CA3 region of the hippocampus in vitro. D3 receptors decreased the power and broadened the bandwidth of gamma oscillations induced by acetylcholine or kainate. Blockade of the D3 receptors resulted in faster synchronization of the oscillations, suggesting that endogenous dopamine in the hippocampus slows down the dynamics of gamma oscillations by activation of D3 receptors. Investigating the underlying cellular mechanisms for these effects showed that D3 receptor activation decreased the rate of action potentials (APs) during gamma oscillations and reduced the precision of the AP phase coupling to the gamma cycle in CA3 pyramidal cells. The results may offer an explanation how selective activation of D3 receptors may impair cognition and how, in converse, D3 antagonists may exert pro-cognitive and antipsychotic effects.

Temporal Structure of Neuronal Activity among Cortical Neuron Subtypes during Slow Oscillations in Anesthetized Rats.

Patterned activity in neocortical electroencephalograms, including slow waves and gamma oscillations, is thought to reflect the organized activity of neocortical neurons that comprises many specialized neuron subtypes. We found that the timing of action potentials during slow waves in individual cortical neurons was correlated with their laminar positions and axonal targets. Within gamma cycles nested in the slow-wave depolarization, cortical pyramidal cells fired earlier than did interneurons. At the start of slow-wave depolarizations, activity in thalamic neurons receiving inhibition from the basal ganglia occurred earlier than activity in cortical neurons. Together, these findings reveal a temporally ordered pattern of output from diverse neuron subtypes in the frontal cortex and related thalamic nuclei during neocortical oscillations.

Acute intracerebral treatment with amyloid-beta (1-42) alters the profile of neuronal oscillations that accompany LTP induction and results in impaired LTP in freely behaving rats.

Accumulation of amyloid plaques comprises one of the major hallmarks of Alzheimer’s disease (AD). In rodents, acute treatment with amyloid-beta (Aβ; 1–42) elicits immediate debilitating effects on hippocampal long-term potentiation (LTP). Whereas LTP contributes to synaptic information storage, information is transferred across neurons by means of neuronal oscillations. Furthermore, changes in theta-gamma oscillations, that appear during high-frequency stimulation (HFS) to induce LTP, predict whether successful LTP will occur. Here, we explored if intra-cerebral treatment with Aβ(1–42), that prevents LTP, also results in alterations of hippocampal oscillations that occur during HFS of the perforant path-dentate gyrus synapse in 6-month-old behaving rats. HFS resulted in LTP that lasted for over 24 h. In Aβ-treated animals, LTP was significantly prevented. During HFS, spectral power for oscillations below 100 Hz (δ, θ, α, β and γ) was significantly higher in Aβ-treated animals compared to controls. In addition, the trough-to-peak amplitudes of theta and gamma cycles were higher during HFS in Aβ-treated animals. We also observed a lower amount of envelope-to-signal correlations during HFS in Aβ-treated animals. Overall, the characteristic profile of theta-gamma oscillations that accompany successful LTP induction was disrupted. These data indicate that alterations in network oscillations accompany Aβ-effects on hippocampal LTP. This may comprise an underlying mechanism through which disturbances in synaptic information storage and hippocampus-dependent memory occurs in AD.

From neural oscillations to reasoning ability: Simulating the effect of the theta-to-gamma cycle length ratio on individual scores in a figural analogy test

Several existing computational models of working memory (WM) have predicted a positive relationship (later confirmed empirically) between WM capacity and the individual ratio of theta to gamma oscillatory band lengths. These models assume that each gamma cycle represents one WM object (e.g., a binding of its features), whereas the theta cycle integrates such objects into the maintained list. As WM capacity strongly predicts reasoning, it might be expected that this ratio also predicts performance in reasoning tasks. However, no computational model has yet explained how the differences in the theta-to-gamma ratio found among adult individuals might contribute to their scores on a reasoning test. Here, we propose a novel model of how WM capacity constraints figural analogical reasoning, aimed at explaining inter-individual differences in reasoning scores in terms of the characteristics of oscillatory patterns in the brain. In the model, the gamma cycle encodes the bindings between objects/features and the roles they play in the relations processed. Asynchrony between consecutive gamma cycles results from lateral inhibition between oscillating bindings. Computer simulations showed that achieving the highest WM capacity required reaching the optimal level of inhibition. When too strong, this inhibition eliminated some bindings from WM, whereas, when inhibition was too weak, the bindings became unstable and fell apart or became improperly grouped. The model aptly replicated several empirical effects and the distribution of individual scores, as well as the patterns of correlations found in the 100-people sample attempting the same reasoning task. Most importantly, the model’s reasoning performance strongly depended on its theta-to-gamma ratio in same way as the performance of human participants depended on their WM capacity. The data suggest that proper regulation of oscillations in the theta and gamma bands may be crucial for both high WM capacity and effective complex cognition.

Reliability and Precision Are Optimal for Non-Uniform Distributions of Presynaptic Release Probability

Most conceptual and computational models assume that synaptic transmission is reliable, a simplification rarely substantiated by data. The functional consequences of the recruitment of high and low initial release probability synapses on the reliability and precision of their postsynaptic targets are studied in a multi-compartmental model of a hippocampal CA1 pyramidal cell. We show that changes in the firing rate of CA3 afferent inputs (rate remapping) are not reflected in the firing rate of the CA1 cell but in the reliability and precise timing of some of its action potentials, suggesting that a signature of remapping may be found in the precise spike timing of CA1. Our results suggest that about half of the action potentials produced by a CA1 cell can potentially carry reliable information in their precise timing with about 25 ms precision, a time scale on the order of the gamma cycle. We show further that reliable events were primarily elicited by CA3 synapses in a state of low probability of release. Overall, our results suggest that the non-uniform distribution of initial release probabilities observed experimentally achieves an optimum yielding simultaneously high precision and high reliability, and allows large populations of CA3 synapses to contribute to the production of reliable CA1 spiking events.

Fast rhythm cycles as atomic fragments of cortical processing and learning

Neuronal rhythms of different frequencies are ubiquitous in the brain activity. These rhythms are thought to be not just a mere epiphenomenon of neural dynamics, but to play an important role in information processing performed by the brain networks. However, the character of their functional involvement remains still largely elusive. Fast brain rhythms in the gamma frequency range of 40-100 Hz, known to modulate both neuronal activity and synaptic plasticity, were often proposed to provide a reference frame for operations performed by cortical microcircuits [1,2]. More precisely, it was hypothesized that a flexible winner-take-all (WTA) computation is performed in a cycle of gamma oscillation by local fine-scale subnetworks that contain tightly coupled excitatory pyramidal neurons residing in cortical layer II-III. Such operation selects and amplifies a small population of pyramidal cells based on the incoming afferent input while suppressing the rest, rapidly generating a sparse code that represents the current stimulus in a course of a single gamma cycle. This hypothesis leaves open whether learning and memory trace formation as well may rely on fast rhythm cycles as discrete atomic fragments of ongoing processing. We use here a hierarchical recurrent network that employs gamma cycle as an atomic fragment for unsupervised learning of object identity from natural image input [3]. Unsupervised learning runs on the top of a fast winner-take-all (WTA)-like computation performed within a single cycle of the ongoing fast rhythm. If given natural face images, the network is able to create memory traces containing reusable facial visual elements that are linked in associative, generative manner via simultaneously established bottom-up, lateral and top-down connectivity into a global person face identity. If a face image of a memorized person is presented, the network is able to rapidly recall its identity and gender in a single gamma cycle. The operation performed within a single cycle may be interpreted as a probabilistic inference of the latent causes that create the input and an estimation of the parameters of a mixture model with latent causes as its components. This computation has the character of an expectation-maximization procedure, where expectation part is carried out by WTA-like computation and maximization involves plasticity mechanisms that change synaptic strength and neural excitability over many repetitive cycles. Even if decoupled from external input, the network can self-generate activity in an off-line regime, replaying the memory content in a sequence of gamma cycles and improving its organization to generalize better over the novel face images not presented before once back in input-driven regime [4]. Thus, the presented network model provides interpretation of the gamma cycle as an elementary fragment of ongoing processing and learning, where each cycle embeds a winner-take-all-like computation that supports memory trace formation and memory trace maintenance in hierarchical recurrent network pathways of the cortex.

A network that performs brute-force conversion of a temporal sequence to a spatial pattern: relevance to odor recognition.

A classic problem in neuroscience is how temporal sequences (TSs) can be recognized. This problem is exemplified in the olfactory system, where an odor is defined by the TS of olfactory bulb (OB) output that occurs during a sniff. This sequence is discrete because the output is subdivided by gamma frequency oscillations. Here we propose a new class of “brute-force” solutions to recognition of discrete sequences. We demonstrate a network architecture in which there are a small number of modules, each of which provides a persistent snapshot of what occurs in a different gamma cycle. The collection of these snapshots forms a spatial pattern (SP) that can be recognized by standard attractor-based network mechanisms. We will discuss the implications of this strategy for recognizing odor-specific sequences generated by the OB.

Attentional Modulation of Cell-Class-Specific Gamma-Band Synchronization in Awake Monkey Area V4

Selective visual attention is subserved by selective neuronal synchronization, entailing precise orchestration among excitatory and inhibitory cells. We tentatively identified these as broad (BS) and narrow spiking (NS) cells and analyzed their synchronization to the local field potential in two macaque monkeys performing a selective visual attention task. Across cells, gamma phases scattered widely but were unaffected by stimulation or attention. During stimulation, NS cells lagged BS cells on average by ∼60° and gamma synchronized twice as strongly. Attention enhanced and reduced the gamma locking of strongly and weakly activated cells, respectively. During a prestimulus attentional cue period, BS cells showed weak gamma synchronization, while NS cells gamma-synchronized as strongly as with visual stimulation. These analyses reveal the cell-type-specific dynamics of the gamma cycle in macaque visual cortex and suggest that attention affects neurons differentially depending on cell type and activation level.