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Amygdala Circuits for Appetitive Conditioning

Periodic Reporting for period 4 - Amygdala Circuits (Amygdala Circuits for Appetitive Conditioning)

Reporting period: 2020-07-01 to 2020-12-31

The capacity to learn from experience is an essential brain function, which drastically increases an animal’s fitness by enabling rapid, adaptive changes of behavior. Learning to associate a set of stimuli is a simple and fundamental form of memory formation, which has been under intense investigation from various angles for many decades. Associative learning allows organisms to confer significance to novel stimuli predicting aversive or appetitive experiences and to adopt appropriate actions according to the valence of expected outcomes. Understanding how activity in defined neuronal circuits mediates appetitive and aversive learning, as well as how these circuitries might be shared or different, is a central, unanswered question in systems neuroscience.

Our project addresses the fundamental question how the brain encodes and controls behavior. While we have a reasonable understanding of the role of entire brain areas in such processes, and of mechanisms at the molecular and synaptic levels, there is a big gap in our knowledge of how behavior is controlled at the level of defined neuronal circuits.

Experience-dependent changes in behavior are mediated by long-term functional modifications in brain circuits. To investigate the neurobiological basis of learning and memory, we are focusing on how changes in the structure and function of neuronal circuits relates to and drives learning at the behavioral level. In order to investigate the underlying mechanisms in great cellular details, we are using simple learning paradigms including classical (Pavlovian) conditioning and instrumental, goal-directed forms of learning. A large number of studies in animals and humans have identified the amygdala as a key structure embedded in a brain-wide neuronal network mediating multiple forms of associative and instrumental learning. Using a multidisciplinary approach in mice, we investigate the anatomical and functional logic of amygdala circuits, their computations, and their interactions with other brain areas.

Our research shows that functionally, anatomically and genetically defined types of amygdala neurons are precisely connected both within local and within larger-scale neuronal networks, and that they selectively contribute to specific aspects of associative learning about both positive and negative experiences. Using deep brain Ca2+ imaging in freely behaving mice, we have described, for the first time, the rules governing neuronal ensemble dynamics in amygdala circuits during associative learning and during explorative behavior. The results of our investigations bring us a step closer to understanding how the brain implements learning algorithms for complex computations at the level of defined neuronal circuits.

Importantly, the investigation of the neurobiological basis of learning is also key for obtaining a mechanistic understanding and developing new therapeutic strategies for debilitating brain diseases including severe psychiatric and neurological conditions. Mood and anxiety disorders, for instance, are among the greatest societal burdens in terms of impairment and disability. These diseases thus represent one of the greatest preventive and therapeutic challenges in medicine. To meet these challenges, there is an urgent need for a better understanding of the underlying neurobiology. However, the neurobiological mechanisms underlying the ethiology and pathophysiology of such mental disorders is poorly understood. One of the emerging concepts posits that maladaptive neuronal plasticity caused by environmental and genetic factors can give rise to pathological neuronal circuit function, and that this process is key for the progression and manifestation of mental diseases.
Experience-dependent changes in behavior are mediated by long-term functional modifications in brain circuits. To study the underlying mechanisms, we are using Pavlovian and instrumental conditioning paradigms. Studies in animals and humans have identified the amygdala as a key structure embedded in a brain-wide neuronal network mediating multiple forms of associative learning.
In this project, our research has focused on functionally, anatomically and genetically defined types of amygdala (inter-)neurons and their role in regulating circuit plasticity and associative learning. We have identified key inhibitory and dis-inhibitory micro-circuit motifs that function as dynamic learning gates. Further, we investigated how amygdala outputs tap into downstream midbrain circuits, which drive adaptive behavioral responses.
We have used deep brain Ca2+ imaging in freely moving mice to address population level coding of associative learning. These experiments have led us to explore general principles of amygdala coding across different behavioral paradigms. Our current work indicates that the amygdala integrates predictive learning with state signals which, considering the amygdala’s brain-wide connectivity, may be important to orchestrate brain activity for state- and context-dependent perception, action selection and learning.
Memories are not stored by single nerve cells, but most likely by populations of neurons. We however profoundly lack insight into how the activity of individual neurons within a neuronal circuit relates to the dynamics of the rest of the population, to what degree that underlies the encoding of information like stimulus properties or behavioral state and if that relationship is different for distinct neuronal circuits or changes during memory formation. A major technological development is the establishment of live imaging of deep brain structures, such as the amygdala, using a miniature microscope that can be carried by a mouse. This approach has made it possible to image, and live-stream, the activity of large populations of individual neurons in deep brain structures of freely moving animals. We have used this approach for imaging neuronal activity in the amygdala. In a collaboration with a lab at Stanford University in the US, we investigated neuronal population dynamics of amygdala neurons during associative learning. Unforeseen from prior work that suggested that learning leads to increased neuronal responses to the learned stimulus, we found that a combination of up- and down-regulation of individual cells’ responses are as important for storing the learned CS-US association. In the second phase of the ongoing project, we have extended this analysis to different forms of learning including appetitive conditioning and instrumental learning paradigms. Thereby, were able to uncover general principles governing the neuronal implementation of learning and memory processes in the brain.