Modeling Synaptic Plasticity in the HippocampalTrisynaptic Circuit: Integration to the Brain Simulation Platform
The hippocampus is a part of the brain which is crucial for learning, memory and spatial navigation. It is not fully understood how ongoing concurrent synaptic plasticity shapes the connections in the trisynaptic circuit of the hippocampus. The trisynaptic circuit involves 3 main synaptic connections between 4 major regions within the hippocampus:
1. synaptic connection: perforant path synapses between the entorhinal cortex (EC) and the dentate gyrus (DG)
2. synaptic connection: mossy fibre synapses between the DG and the CA3
3. synaptic connection - Schaffer collateral synapses between the CA3 and the CA1.
Recurrent synapses between CA3 pyramidal cells as well as EC-CA1 and EC-CA3 synapses represent additional important synaptic connections in the circuit.
Previously we focused on developing models of synaptic plasticity modules within the CA1 or the DG subcircuit of the hippocampus. Here, our main objective is to move beyond subcircuits toward trisynaptic hippocampal circuit by integrating and testing the models of short-term and long-term synaptic plasticity at the above mentioned synaptic connections. This is crucial to understand memory storage in three major cell groups of the hippocampus: granule cells in the dentate gyrus, pyramidal cells in the CA3, and pyramidal cells in the CA1.
The project includes three key steps:
First, we will extract short-term and long-term synaptic plasticity data for the above mentioned synaptic connections (EC-DG, DG-CA3, CA3-CA3, CA3-CA1) from the available literature (e.g. Buchanan and Mellor, 2010; Mishra et al., 2016), from our own previous work (e.g. Jedlicka, Benuskova, Abraham, 2015; Saudargiene and Graham, 2015; Jungenitz et al., 2018) and from the Hippocampome database (hippocampome.org, Moradi and Ascoli, 2019).
Second, based on these experimental data, we will implement both phenomenological as well as detailed voltage-dependent models of long-term synaptic plasticity (e.g. Jedlicka, Benuskova, Abraham, 2015; Saudargiene and Graham, 2015; Ebner, Clopath, Jedlicka, Cuntz 2019, minor revision) in the trisynaptic circuit model of the hippocampus thereby extending the Brain Simulation Platform of the HBP.
Third, we will implement Markram-Tsodyks-like models (Tsodyks & Markram, 1997) of short-term plasticity in the Brain Simulation Platform to capture fast frequency-dependent synaptic changes in the hippocampal canonical circuit.
In summary, the main goal of the project is to explore the interactions and synergies between specific plasticity rules at distinct synaptic connections in the trisynaptic hippocampal circuit using cutting-edge computational modeling techniques. This project will allow us to test the hypotheses concerning synaptic network changes with unparalleled complexity of biological details, precision and speed. Using the new trisynaptic model we will make predictions on the functional properties of the network, which can then be verified again experimentally in the future. Thus, we hope to understand how short-term and long-term synaptic plasticity affects the functional properties of a well-defined neuronal network.
Background and state of the art
The perforant path EC-DG, EC-CA1 inputs, mossy fiber DG-CA3 inputs, CA3-CA3 recurrent inputs as well as Schaffer collateral CA3-CA1 inputs represent major excitatory synaptic connections in the entorhinal–hippocampal system which is a key brain system involved in the computation and storage of spatial representation. This became evident after the discovery of space coding place cells in the hippocampus and grid cells in the medial entorhinal cortex. Short-term and long-term synaptic plasticity has been thoroughly studied in this system. However, the interaction between ongoing concurrent synaptic plasticity changes at anatomically distinct connections and their impact on input-output properties of the trisynaptic circuit of the hippocampus is not sufficiently understood. Therefore, in this project we are focusing on developing models of synaptic plasticity in all major subcircuits of the hippocampus.
Our previous efforts were focused on 2 isolated subcircuits: CA1 (Saudargiene and Graham, 2015) and DG (Jungenitz et al., 2018). Our current goal is to move beyond subcircuits by developing and integrating short-term and long-term synaptic plasticity models in the hippocampal EC-DG-CA3-CA1 trisynaptic network model. The questions adressed in this study are important for understanding mechanisms of synaptic plasticity and learning in hippocampal networks.
Platform the project will use
The models we and our HBP contact partners have developed (Jedlicka, Benuskova, Abraham, 2015; Saudargiene et al., 2015; Saudargiene and Graham, 2015; Ebner, Clopath, Jedlicka, Cuntz 2019, minor revision; Solinas et al., 2019) are based on modelling the hippocampal functions in detail, using data-driven biophysical approaches. Therefore, the Brain Simulation Platform (which will be integrated into the SC3 EBRAINS Platform) is a natural choice to integrate our work as part of the HBP. We need better understanding and practical guidance on how to incorporate the HBP tools and how to implement the models developed by us as part of the EBRAINS Platform.
Proposed work going beyond state of the art
The benefits for the HBP will be twofold: 1) The EBRAINS platform will be populated with a set of new models for hippocampal circuits, and 2) the models will help extending the recently published model of the CA1 synaptic plasticity (Migliore et al., 2019) to the other neuron populations composing the trisynaptic network. This will be the first model of the hippocampal trisynaptic microcircuit including synaptic plasticity.
The long-term benefits of integrating our work and models on plasticity are to prepare the EBRAINS platform for testing and validating community plasticity models. Our models, on top of those developed within the HBP, could be the driving examples for validation, comparison, reproducibility, and further development of synaptic plasticity models in general.
We propose an approach that has not, to the best of our knowledge, been before used elsewhere in hippocampal simulation models.
The novelty of the project is the integration of short-term and long-term plasticity models in the trisynaptic hippocampal EC-DG-CA3-CA1 network. This has not been done previously in any of the hippocampal models.
The proposed project will shed light on learning and memory formation in the hippocampal circuit. Data-driven models of plasticity at major synaptic inputs are critical for realistic simulations of the involvement of the DG, CA3 and CA1 in the separation, memorizing, transfer and completion of afferent patterns of activity from the EC to the downstream hippocampal regions. Furthermore, realistic models of these synaptic connections are also clinically relevant because pathological alterations of entorhinal-dentate connections are involved in major neurological disorders such as epilepsy and Alzheimer’s disease. In the future, the model might be used to investigate the learning deficits in Alzheimer’s disease.
Buchanan KA, Mellor JR. The activity requirements for spike timing-dependent plasticity in the hippocampus. Front Synaptic Neurosci. 2010; 2:11.
Ebner, Clopath, Jedlicka, Cuntz. Unifying long-term plasticity rules for excitatory synapses by modeling dendrites of cortical pyramidal neurons. Cell Reports, 2019; minor revision.
Saudargiene A, Cobb S, Graham BP. A computational study on plasticity during theta cycles at Schaffer collateral synapses on CA1 pyramidal cells in the hippocampus. Hippocampus. 2015; 25(2):208-18.
Saudargiene A, Graham BP. Inhibitory control of site-specific synaptic plasticity in a model CA1 pyramidal neuron. Biosystems. 2015; 130:37-50.
Solinas SMG, Edelmann E, Leßmann V, Migliore M (2019) A kinetic model for Brain-Derived Neurotrophic Factor mediated spike timing-dependent LTP. PLoS Comput Biol 15(4): e1006975.
Mishra RK, Kim S, Guzman SJ, Jonas P. Symmetric spike timing-dependent plasticity at CA3-CA3 synapses optimizes storage and recall in autoassociative networks. Nat Commun. 2016; 7:11552.
Moradi K1, Ascoli GA. A comprehensive knowledge base of synaptic electrophysiology in the rodent hippocampal formation. Hippocampus. 2019; doi: 10.1002/hipo.23148.
Jedlicka P, Benuskova L, Abraham WC. A voltage-based STDP rule combined with fast BCM-like metaplasticity accounts for LTP and concurrent “heterosynaptic” LTD in the dentate gyrus in vivo. PLOS Comput Biol. 2015; 11:e1004588.
Tsodyks MV, Markram H. The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proc Natl Acad Sci U S A., 1997; 94(2):719-23.
Jungenitz T, Beining M, Radic T, Deller T, Cuntz H, Jedlicka P, Schwarzacher SW. Structural homo- and heterosynaptic plasticity in mature and adult newborn rat hippocampal granule cells. Proc Natl Acad Sci U S A., 2018; 115(20):E4670-E4679.
Time frame: 2020-2021
Origin: Voucher programme