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Abstract: Fine-scale temporal organization of cortical activity in the gamma range (∼25–80Hz) may play a significant role in information processing, for example by neural grouping ('binding') and phase coding. Recent experimental studies have shown that the precise frequency of gamma oscillations varies with input drive (e.g. visual contrast) and that it can differ among nearby cortical locations. This has challenged theories assuming widespread gamma synchronization at a fixed common frequency. In the present study, we investigated which principles govern gamma synchronization in the presence of input-dependent frequency modulations and whether they are detrimental for meaningful input-dependent gamma-mediated temporal organization. To this aim, we constructed a biophysically realistic excitatory-inhibitory network able to express different oscillation frequencies at nearby spatial locations. Similarly to cortical networks, the model was topographically organized with spatially local connectivity and spatially-varying input drive. We analyzed gamma synchronization with respect to phase-locking, phase-relations and frequency differences, and quantified the stimulus-related information represented by gamma phase and frequency. By stepwise simplification of our models, we found that the gamma-mediated temporal organization could be reduced to basic synchronization principles of weakly coupled oscillators, where input drive determines the intrinsic (natural) frequency of oscillators. The gamma phase-locking, the precise phase relation and the emergent (measurable) frequencies were determined by two principal factors: the detuning (intrinsic frequency difference, i.e. local input difference) and the coupling strength. In addition to frequency coding, gamma phase contained complementary stimulus information. Crucially, the phase code reflected input differences, but not the absolute input level. This property of relative input-to-phase conversion, contrasting with latency codes or slower oscillation phase codes, may resolve conflicting experimental observations on gamma phase coding. Our modeling results offer clear testable experimental predictions. We conclude that input-dependency of gamma frequencies could be essential rather than detrimental for meaningful gamma-mediated temporal organization of cortical activity.
Author Summary: Almost 350 years ago the physicist and polymath Christiaan Huygens first observed the synchronization between two pendulum clocks attached to a common support. Since then synchronization has been recognized as a universal phenomenon from astronomy to biology. The phase-locking (synchrony) and the phase-relation between the two pendulums are determined by two principal forces: the synchronization force exerted over the connection and the tendency to desynchronize due to frequency (speed) differences. We propose that gamma synchronization (25–80Hz) among oscillating cortical neurons in the brain can be understood according to the same principles—like a field of many connected pendula—with the critical addition that input changes the frequency of gamma oscillations, as shown by recent experimental studies. It has been assumed that input-dependent changes in oscillation frequency are detrimental for a meaningful role of gamma synchronization in neural processing. To the contrary, our theoretical analysis demonstrates that because input can change the frequency of the oscillation, phase-locking and phase-relations among neurons relate systematically to input. By analogy, it is because a local push to a pendulum will change its frequency, that resulting changes in phase-locking and phase-relation among the pendula can be used to derive the external force applied.
Humans and other primates are highly visual animals. Our daily visual activities such as recognizing familiar faces, interacting with objects, or reading, are supported by an extensive system of interacting brain areas. The interactions between the many individual nerve cells both within and between brain areas need to be coordinated. One possible solution to achieve flexible coordination between cells in the network is rhythmic activity, or oscillations. The focus of the thesis will be activity in the largest visual area, V1, in non-human primates. In V1, high-frequency activity, so-called gamma-band activity (“gamma”, ca. 30-90 Hz) can be frequently observed and has been suggested to play a role in coordinating activity in the visual system. In Chapter 1, the coordination problem, the primate visual system and gamma-band oscillations are introduced in detail. The following chapters explore the dependence of gamma on contextual influences. Does V1 use contextual information to optimize co-ordination? In the first part, the short-term consequences of repeated encounters with visual stimuli on V1 responses are explored (Chapters 2 and 3). Inspired by results from colored, naturalistic images in the first part, the second part tests the dependence of gamma on spatial and chromatic stimulus aspects (Chapters 4 and 5).
Stimulus repetition is a simple yet powerful way to tap into our brains’ ability to learn and adapt to our environment. Repeated presentation of a visual stimulus tends to decrease responses to this stimulus. Is this accompanied by changes in the coordination of brain activity? In Chapter 2, the stimulus-specificity of repetition effects on gamma was tested using naturalistic stimuli. V1 is most typically studied using black-and-white, artificial stimuli that are very familiar to the animals. Here, colored natural images were repeatedly presented that were initially novel to the animals, to provide a wider and more naturalistic range of stimulation. Both multi-unit spiking activity (MUA) and gamma showed stimulus-specific repetition effects. MUA responses de-creased most strongly for initial repetitions and less for later repetitions. In contrast, gamma could increase or decrease for initial repetitions, but tended to increase for later repetitions. This points to the operation of multiple plasticity mechanisms. One process may rapidly decrease MUA and gamma and be related to initial novelty or adaptation. The other increases gamma, is active for more repetitions, and could constitute a form of refinement of coordination over time. Moreover, based on the spacing of stimulus repetitions, stimulus memory in V1 persisted for tens of seconds.
In the following Chapter 3, the stimulus location specificity and persistence of the repetition effects for longer timescales were tested. To this end, the observation that the increase in gamma with repetition was strongest for the first tens of repetitions was used to test for location specificity and memory. Using simple artificial stimuli that were repeated many times at two alternating locations, both location specificity and memory on the order of minutes was observed. Due to the structure of the primate visual system, location specificity suggests that the repetition effects involve early to mid-level visual areas such as V1. Memory for previous stimulus presentations on the order of minutes has not been previously reported for V1 gamma. Taken together, these experiments demonstrate short-term plasticity of gamma that is stimulus- and location specific and persists on the timescale of minutes.
In Chapter 2, the average gamma-band response to the large, naturalistic stimuli was highly stimulus dependent. Relative increases in gamma-band activity scaled between tens and thousands of percent change depending on the stimulus. Particularly the color of the stimuli appeared to play a strong role, although the stimulus set was too limited and uncontrolled to draw strong conclusions. In Chapters 4 and 5, underlying mechanisms for the stimulus specificity of gamma were explored using more well-controlled, artificial stimuli that varied in color and spatial structure.
Much of vision relies on the analysis of spatial structure. Each nerve cell in V1 only responds to visual stimuli in a particular, small part of the visual field, its so-called “receptive field” (RF). Compared to isolated RF stimulation, nearby cells that are stimulated by a similar structure from different parts of visual space can show response decreases, commonly known as “surround suppression”, and may show coordinated activity in the gamma band. In Chapter 3, responses to large, uniformly colored disks are contrasted with responses to black or white (achromatic) disks. A first experiment showed that gamma-band responses were stronger for colored than achromatic stimuli, whereas MUA responses could decrease below baseline for colored stimuli. To test whether these phenomena were related to surround suppression, stimulus size was manipulated in a second experiment. When stimuli were of sufficient size to induce surround suppression, clear gamma-band responses emerged. Surround suppression and gamma were stronger for chromatic stimuli. However, the change of stimulus size could have changed not only surround suppression but also stimulus saliency. Therefore, in a third experiment, the overall size of the stimulus was kept constant, and the spatial structure of the stimulus was manipulated. In comparison to uniform, predictable stimulus structure, mismatches between the center of the stimulus and the surrounding visual space led to strong increases in MUA responses and strong de-creases in gamma-band activity. These effects were restricted to the recording sites with RFs at the mismatch location. These experiments underpin the strong role of both spatial structure and color for gamma in V1.
In Chapter 4, responses to different color hues are studied in more detail. Gamma response strength depended on hue, being strongest for red compared to blue and green stimuli when measured with a gray background. To better understand the underlying mechanisms of the differential responses, the spatio-temporal context in the form of the background color was manipulated. Background color had a strong influence on gamma strength. Using differently colored backgrounds, different parts of the color signaling pathways could be adapted. Response differences to different color hues could be explained well with a model that incorporates differences in adaptation between pathways involving long- compared to medium-wavelength cone signals.
Taken together, these experiments indicate a strong role of both spatial context (stimulus size and structure) and temporal context and drive (repetition, adaptation) for the generation of gamma-band activity in V1. Functional implications of these dependencies are considered in the final Chapter 6, and a role for gamma-band syn-chronization in a coding regime for visual inputs that generate strong drive and high predictability is suggested.
When a visual stimulus is repeated, average neuronal responses typically decrease, yet they might maintain or even increase their impact through increased synchronization. Previous work has found that many repetitions of a grating lead to increasing gamma-band synchronization. Here, we show in awake macaque area V1 that both repetition-related reductions in firing rate and increases in gamma are specific to the repeated stimulus. These effects show some persistence on the timescale of minutes. Gamma increases are specific to the presented stimulus location. Further, repetition effects on gamma and on firing rates generalize to images of natural objects. These findings support the notion that gamma-band synchronization subserves the adaptive processing of repeated stimulus encounters.
When a visual stimulus is repeated, average neuronal responses typically decrease, yet they might maintain or even increase their impact through increased synchronization. Previous work has found that many repetitions of a grating lead to increasing gamma-band synchronization. Here we show in awake macaque area V1 that both, repetition-related reductions in firing rate and increases in gamma are specific to the repeated stimulus. These effects showed some persistence on the timescale of minutes. Further, gamma increases were specific to the presented stimulus location. Importantly, repetition effects on gamma and on firing rates generalized to natural images. These findings suggest that gamma-band synchronization subserves the adaptive processing of repeated stimulus encounters, both for generating efficient stimulus responses and possibly for memory formation.
Surface color and predictability determine contextual modulation of V1 firing and gamma oscillations
(2019)
The integration of direct bottom-up inputs with contextual information is a core feature of neocortical circuits. In area V1, neurons may reduce their firing rates when their receptive field input can be predicted by spatial context. Gamma-synchronized (30–80 Hz) firing may provide a complementary signal to rates, reflecting stronger synchronization between neuronal populations receiving mutually predictable inputs. We show that large uniform surfaces, which have high spatial predictability, strongly suppressed firing yet induced prominent gamma synchronization in macaque V1, particularly when they were colored. Yet, chromatic mismatches between center and surround, breaking predictability, strongly reduced gamma synchronization while increasing firing rates. Differences between responses to different colors, including strong gamma-responses to red, arose from stimulus adaptation to a full-screen background, suggesting prominent differences in adaptation between M- and L-cone signaling pathways. Thus, synchrony signaled whether RF inputs were predicted from spatial context, while firing rates increased when stimuli were unpredicted from context.
Under natural conditions, the visual system often sees a given input repeatedly. This provides an opportunity to optimize processing of the repeated stimuli. Stimulus repetition has been shown to strongly modulate neuronal-gamma band synchronization, yet crucial questions remained open. Here we used magnetoencephalography in 30 human subjects and find that gamma decreases across ≈10 repetitions and then increases across further repetitions, revealing plastic changes of the activated neuronal circuits. Crucially, increases induced by one stimulus did not affect responses to other stimuli, demonstrating stimulus specificity. Changes partially persisted when the inducing stimulus was repeated after 25 minutes of intervening stimuli. They were strongest in early visual cortex and increased interareal feedforward influences. Our results suggest that early visual cortex gamma synchronization enables adaptive neuronal processing of recurring stimuli. These and previously reported changes might be due to an interaction of oscillatory dynamics with established synaptic plasticity mechanisms.
Under natural conditions, the visual system often sees a given input repeatedly. This provides an opportunity to optimize processing of the repeated stimuli. Stimulus repetition has been shown to strongly modulate neuronal-gamma band synchronization, yet crucial questions remained open. Here we used magnetoencephalography in 30 human subjects and find that gamma decreases across ~10 repetitions and then increases across further repetitions, revealing plastic changes of the activated neuronal circuits. Crucially, changes induced by one stimulus did not affect responses to other stimuli, demonstrating stimulus specificity. Changes partially persisted when the inducing stimulus was repeated after 25 minutes of intervening stimuli. They were strongest in early visual cortex and increased interareal feedforward influences. Our results suggest that early visual cortex gamma synchronization enables adaptive neuronal processing of recurring stimuli. These and previously reported changes might be due to an interaction of oscillatory dynamics with established synaptic plasticity mechanisms.
Parallel multisite recordings in the visual cortex of trained monkeys revealed that the responses of spatially distributed neurons to natural scenes are ordered in sequences. The rank order of these sequences is stimulus-specific and maintained even if the absolute timing of the responses is modified by manipulating stimulus parameters. The stimulus specificity of these sequences was highest when they were evoked by natural stimuli and deteriorated for stimulus versions in which certain statistical regularities were removed. This suggests that the response sequences result from a matching operation between sensory evidence and priors stored in the cortical network. Decoders trained on sequence order performed as well as decoders trained on rate vectors but the former could decode stimulus identity from considerably shorter response intervals than the latter. A simulated recurrent network reproduced similarly structured stimulus-specific response sequences, particularly once it was familiarized with the stimuli through non-supervised Hebbian learning. We propose that recurrent processing transforms signals from stationary visual scenes into sequential responses whose rank order is the result of a Bayesian matching operation. If this temporal code were used by the visual system it would allow for ultrafast processing of visual scenes.