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Dendritic encoding of movement in space

Periodic Reporting for period 2 - SUBDECODE (Dendritic encoding of movement in space)

Reporting period: 2018-11-01 to 2019-12-31

Understanding the function of dendrites is the key to predict the computations that a neuron performs within a network during behaviour. Dendrites are computational subunits of individual neurons. If we want to understand how neural networks give rise to behavior and cognition, we neet to understand the principles of intra- and inter-neuronal computations. Dendrites integrate synaptic inputs in the form of synaptic potentials. This integration defines the transformation of synaptic input to action potential output and is a fundamental function of every neuron in the brain. In this project we aim at
understanding the rules that define the output of a neuron from identified input patterns.
Our main goals are (1) to understand how different inputs contribute to neuronal output firing of subicular neurons. The subiculum is a brain region at the interface between the hippocampal formation, a brain region important for learning and memroy processes, and the neocortex. It generates an important hippocampal output code that is interpreted by other brain regions. Moreover (2), we aim at monitoring free behavior at unprecedented detail to investigate how behavioral modules contribute to dendritic integration on the single cell level. Finally (3) we want to use behavioral observation exclusively to predict the membrane potential fluctuations of subicular neurons.
As a first step, we have determined the relative distribution of subicular neurons with different spatial output tuning focusing on different glutamatergic cell populations. Then we have monitored how and where on the dendritic tree of individual neurons different inputs from areas CA1, entorhinal cortex and other regions are integrated. We have traced the inputs to subiculum from other brain regions using a single-cell initiated tracing protocol. We have already identified several distinct local and long-range synaptic inputs onto dorsal subiculum glutamatergic neurons. We mapped the precise neuroanatomical distribution of the labelled pre-synaptic neurons at a whole-brain scale, and generated a transparent 3D brain model for a clearly visualization of the neuronal circuit mapping. Projections from several structures were observed. Then we used specific ligand (DREADD-)based manipulation of individual input pathways to understand which information is transferred via different input streams to subicular neurons. In this part of the project we have focused on main input regions of subicular neurons (CA1, entorhinal, local connections). Now, we use realistic data-driven biophysical models of subicular pyramidal neurons to understand the principles of input output transformation. Currently,
we use several experimental steps to build a detailed, data-driven biophysical model of subicular neurons. This general model will be the basis for the generation of cell-type specific models during the optimization process. It is based on our data on somatic and dendritic electrophysiological properties and two-photon imaging of dendritic and somatic activity during head-fixed and free behavior. Our main goal is to generate a model that can use dense behavioral information to generate a prediction of neuronal output, which we test against experimental data using in-vivo single cell (patch clamp) recordings. Using this approach we aim at generating computational models of subicular neurons that are capable of processing realistic spatially tuned inputs. With the knowledge of the movement trajectory and head direction of the animal these models may allow an estimation of the membrane potential of single subicular pyramidal cells during an unconstrained behaviour.
"The success of this project will be the first demonstration that the rules that define the relationship of realistic dendritic in-vivo input to output during a specific behaviour can be formulated and validated. These rules should define the input patterns and predict the patterns of neuronal output. At the current stage the parameter space is being further constrained in all 5 objectives. The major milestone in the second half of the project will be the implenetation of the experimental results in to teh computational model. At the end of the project the experimental data are expected to provide suffcient information to generate and optimize computational models that can predict membrane potential fluctuations (the function) if single neurons exclusively from behavioral observation, allowing an beyond the state of the art estimation of the contribution of ""internal states"" to neuronal function in the subiculum - a key interface region between hippocampus and neocortex"