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Connectivity, plasticity and function of an olfactory memory circuit

Periodic Reporting for period 2 - MCircuits (Connectivity, plasticity and function of an olfactory memory circuit)

Reporting period: 2019-04-01 to 2020-09-30

One of the fundamental assumptions in neuroscience is that brains store information in the synaptic connectivity between neurons in a network. Paradigmatic theories propose that experience drives coordinated changes in synaptic connections that record information about relevant experiences in the “wiring diagram” of a network and optimize network responses to future inputs. This process is thought to endow brains with the ability to create models of the world, which are core components of intelligent behavior. Although a large amount of knowledge has been accumulated about the function and plasticity of individual synapses it remains unclear whether and how modifications of multiple synapses are coordinated, and whether experience-driven plasticity of network structure and function is consistent with existing theories of memory. Direct experimental tests of these theories will ultimately require dense reconstructions of wiring diagrams with synaptic resolution, which remains a major technical challenge in neuroscience. We address this issue using serial block face scanning electron microscopy (SBEM), a technique for imaging the ultrastructure of biological samples with nanometer resolution throughout large volumes. Datasets obtained with this method allow for the dense annotation of neurons and their synaptic connections to reconstruct wiring diagrams of neuronal circuits. This approach is combined with large-scale optical measurements of neuronal activity patterns in the intact brain, with behavioral discrimination learning paradigms, and with computer simulations of structured neuronal networks. We use the olfactory system of adult zebrafish as an experimental model, taking advantage of the small size and genetic accessibility of the zebrafish brain. Our approach will allow us to directly examine how learning shapes the connectivity of neuronal circuits, and how coordinated modifications of connectivity change the dynamics of neuronal population activity. Additional approaches will manipulate neuronal activity and analyze behavior to further explore causal relationships between experience, changes in neuronal activity patterns and behavior. These approaches can test and possibly refine highly influential theories of information processing and learning in neuronal networks. The results are expected to provide mechanistic insights into elementary neuronal computations that are of key importance for higher brain functions and cognition. This knowledge is likely to be highly valuable as a basis to understand how aberrant neuronal connectivity can cause brain dysfunctions in neuropsychiatric conditions. The results will also be of philosophical interest because they will advance our understanding of how brains interpret the world and interact with it. Moreover, detailed insights into the connectivity of biological memory networks is highly desired to push progress in machine learning and artificial intelligence.
During the first half of the project we have established important methodologies including techniques to prepare brain samples for volumetric electron microscopy and to acquire very large image datasets. In test experiments, high-quality images were acquired continuously in a fully automated fashion for up to seven weeks, demonstrating the feasibility of the approach. In addition, we established methods to train adult zebrafish in a behavioral odor discrimination task, and to measure neuronal activity through the intact skull in a virtual reality. The ability to perform exhaustive measurements of neuronal population activity in adult zebrafish, rather than in larvae, opens a wide range of opportunities to analyze the dynamics of neuronal activity patterns underlying cognitive brain functions at high resolution. To understand how learning modifies neuronal circuit function we trained adult zebrafish in our behavioral odor discrimination task and analyzed the resulting changes in neuronal activity patterns in the forebrain. The results revealed highly specific changes in neuronal population activity that contain information not only about the identity of meaningful odors but also about their valence. Furthermore, we found that these learning-related modifications of circuit function involve unexpected and highly specific contributions from inhibitory interneurons. Additional work demonstrated that even in untrained animals, inhibitory signals in a memory network show an unexpected specificity that is tightly coordinated with specific excitatory signals. Together, these results shed new light on the possible function of inhibition in memory networks and demonstrate that the connectivity between excitatory and inhibitory neurons in memory networks must be highly coordinated. By examining odor-evoked activity patterns in a memory network before and after learning we further found signatures in the dynamics of population activity that are consistent with specific classes of theoretical memory models. Further mechanistic insights into the functions of memory networks in zebrafish are expected to come from detailed reconstructions of their wiring diagrams, which is the focus of the second part of the project.
A main focus of future work will be the reconstruction of synaptic wiring diagrams in an olfactory memory circuit after training adult zebrafish in an olfactory discrimination task. The results will be analyzed using a variety of approaches including basic graph theory, computational models, and specific analyses that relate the connectivity of neurons to their activity. In a recent project focusing on the olfactory bulb of zebrafish larvae, we found that detailed reconstructions of wiring diagrams are highly informative about the computational function of the network. The structure of the wiring diagram directly reflected information of molecular relationships between odorants that has probably been integrated into the network structure during evolution. These results provide proof-of-principle that specific information contained in biological circuits can, at least in some cases, be read out by reconstructing their synaptic wiring diagrams. The “functional connectomics” approach taken in this project is therefore promising to provide important insights into the mechanisms by which an individual’s experience is recorded in memory networks. Other parts of the project will further examine the structure of inhibitory subnetworks in an olfactory memory area and explore the mechanisms that control network plasticity. These approaches will exploit the virtual reality developed during the first part of the project and involve optical manipulations of neuronal activity.