Periodic Reporting for period 4 - MCircuits (Connectivity, plasticity and function of an olfactory memory circuit)
Período documentado: 2022-04-01 hasta 2023-09-30
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 establish models of the world that enable 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. We can directly examine how learning shapes the connectivity of neuronal circuits, and how coordinated modifications of connectivity change the dynamics of neuronal population activity. These approaches test and possibly refine highly influential theories of information processing and learning in neuronal networks. The results provide mechanistic insights into elementary neuronal computations that are of key importance for higher brain functions and cognition. This knowledge is likely to drive insights into the relationship between aberrant neuronal connectivity and brain dysfunctions in neuropsychiatric conditions. The results will also advance our general understanding of how brains interpret the world and interact with it. Moreover, wiring diagrams of biological memory networks will most likely push progress in machine learning and artificial intelligence.
We have established important methods including techniques for volumetric electron microscopy (EM), methods for dense reconstructions of neuronal circuits from 3D EM image data, and an improved virtual reality (VR) system for adult zebrafish. In WP1, we trained adult zebrafish in an odor discrimination paradigm and analyzed effects on the dynamics of neuronal population activity as planned. These results identified specific, learning-related changes in odor representations, some of which are not consistent with classical memory models. In particular, we found that telencephalic area Dp, the zebrafish homolog of piriform cortex, operates in a state of “precise synaptic balance” that does not support attractor dynamics as predicted by classical memory models. We thus developed data-driven computational models that extend previous models and account for the complex dynamics observed experimentally. Moreover, we found that Dp contains a joint map of odor identity and valence that is updated by experience. In WP2, we acquired two large volumetric EM datasets covering Dp and parts of adjacent forebrain areas from adult zebrafish that were trained in an odor discrimination task. In both fish, odor-evoked activity was recorded from >1000 neurons in Dp prior to volumetric EM imaging. The reconstruction of synaptically connected networks is ongoing. In one of the datasets, the reconstruction of odor-responsive neurons is almost complete. In parallel, we created network simulations constrained by data obtained in WP1 that uncovered new functions of inhibition in memory networks. Moreover, consistent with experimental observations in WP1, the results revealed that Dp-like memory networks do not exhibit discrete attractor dynamics but store information by constraining activity to local activity manifolds. In WP3, we analyzed inhibitory microcircuits and discovered a specific and instructive role of inhibition in the mapping of odor representations onto a low-dimensional representation of valence during associative learning. These results support the emerging concept that inhibition balances excitation with high precision and makes critical contributions to manifold-based representations in memory networks. In addition, we characterized telencephalic cell types and neuromodulatory systems based on gene expression (transcriptomics), and discovered cognitive maps of external environments in the zebrafish telencephalon by imaging of neuronal activity during behavior in a virtual reality.
This study produced results that went beyond the expected outcomes. First, we found that odor representations in Dp exhibit complex dynamics that are inconsistent with classical attractor network models of memory. Follow-up computational work resulted in the discovery of a class of manifold-based models that are, so far, consistent with biological observations. As these memory models are derived from first principles, they are likely to have broad relevance. Second, we discovered unexpected specificity and functions of inhibitory interactions in artificial and biological neuronal networks. These results are inconsistent with the concept of unspecific homeostatic inhibition and instead show that inhibition contributes significantly to the specificity and plasticity of neuronal representations. Together, these conceptual insights lead to the view that higher brain functions are mediated by networks with balanced excitation and inhibition, and that learning involves bidirectional changes in both components. Third, we established a workflow for optical measurements of neuronal activity across large populations of neurons and subsequent reconstructions of their connectivity using volume electron microscopy (“dynamical connectomics”). These tools will be useful for the scientific community. Fourth, using “dynamical connectomics”, we discovered structure-function relationships in neuronal networks that depend on higher-order connectivity. These insights would not have been possible to obtain without dense circuit reconstructions, and confirm that “dynamical connectomics” can provide unique and novel insights into mechanisms of neuronal computation. Moreover, the datasets generated by this project provide an extremely rich resource for future analyses of memory networks. Fifth, using a virtual reality approach that was originally not a main focus of this project, we found that neuronal populations in the telencephalon of adult zebrafish establish internal models of environments that enable cognitive functions. This result changes traditional views of cognition in teleosts. In summary, our results provided unexpected insights into mechanisms of learning and established the zebrafish as a small vertebrate model to study cognition. This paves the way to analyze mechanisms of neuronal computation underlying cognition that are not accessible in larger vertebrates.