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The neocortical microcircuit, a local network of excitatory and inhibitory neurons, is a highly complex information processing unit, which can flexibly be modulated to adapt to external context and internal state such as motivation or attention. The mechanisms underlying these adaptations for flexible processing are not sufficiently understood yet. The aim of this study is to further elucidate the role of inhibitory and excitatory components of the local neocortical microcircuit for the processing of sensory information in an awake, behaving animal.
Layer 1 of the neocortex is of particular importance because it contains afferents from the thalamus and more distant cortical regions, which relay top-down information that is important for processes such as learning and attention. The dendrites of the excitatory pyramidal neurons located in deeper layers extend into layer 1, and in addition to that layer 1 contains inhibitory neurons, as well as axons from inhibitory somatostatin expressing (SOM) neurons located in lower layers. These layer 1 inhibitory neurons and SOM axons are therefore well positioned to control top-down information transfer at the pyramidal dendrites, and thus to flexibly regulate information processing in the local circuit. To further investigate this, the stimulus responses in inhibitory (SOM axons) and excitatory (layer 2/3 pyramidal neurons) components of the neocortical microcircuit were measured in primary auditory cortex during learning, when auditory stimuli gain relevance.
For this purpose, I first established a suitable learning behaviour, an auditory GO-NOGO discrimination task, which can be performed by head-fixed mice under the microscope. The task also contains a visual start cue, which signals the start of every trial, as a multimodal element. Mice learn to distinguish two auditory stimuli by being rewarded with water after the GO stimulus and receiving no reward after the NOGO stimulus. They indicate that they have identified the stimuli accordingly by licking at a water dispenser during the GO stimulus and not during the NOGO stimulus. Licking during the NOGO stimulus is punished by an aversive air puff. As the mice learn this behaviour, the stimuli gain relevance. The activity in the same neuronal structures was observed over the course of all training sessions via 2-photon imaging in awake, behaving mice, and their stimulus responses were measured throughout the learning process, acquiring a comprehensive dataset. In these data, short-term and long-term plasticity of the stimulus responses can be detected and these changes in the stimulus responses differ for SOM axons and pyramidal neurons. Already from the first training day, stimulus responses change in the course of a single session, both in SOM axons and in pyramidal cells. With time over the course of task acquisition, the stimulus representation in a group of pyramidal neurons in layer 2/3 is enhanced and distal dendrites are less inhibited over training through reduced activation of the SOM axons, so that the integration of information along the somatodendritic axis shifts, increasing the relative impact of top-down information. This shift is even stronger for the NOGO stimulus in correct trials compared to the GO stimulus. This is the first study to show that this somato-dendritic shift by SOM-axon responses occurs at different strengths for the GO and NOGO stimulus, probably due to the different learned responses (action or refraining), which require different forms of circuit control. After learning, the neuronal responses to GO and NOGO stimuli also differ in pyramidal neurons, with the GO stimulus evoking stronger responses than the NOGO stimulus. This learned distinction is reversed in passive trials during which the mice have no possibility to respond to the stimuli, in both SOM axons and pyramidal neurons, resulting in similar response sizes for both stimuli. This indicates that not only learning over the long term, but also short-term changes regarding the state (active execution of the discrimination task or no active participation during the stimulus presentations) affect the processing of the stimuli in the local circuit. In addition, on an even shorter time scale pyramidal neurons show a modulation of responses from trial to trial, probably due to anticipation of reward, which is absent from SOM axon responses. Thus, there are various levels of plasticity that develop over the course of training: long-term changes in the response size of both the excitatory and inhibitory components that facilitate stimulus recognition when engaged, and short-term modulation (possibly in anticipation of reward) in excitatory neurons that could underlie sensorimotor transformation. Both pyramidal neurons and SOM axons in the primary auditory cortex respond to multimodal and reinforcement-related stimuli, likely contributing to the optimisation of circuit dynamics for goal-directed information processing. This shows that the circuit flexibly adjusts information processing under different circumstances, depending on the relevance the stimuli carry and whether the mouse is active or inactive and can use the presented information to achieve a goal.
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.