NEST models and simulation paradigms ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ States ^^^^^^ We consider here two types of mouse experiments that the `canonical circuit` and each derived model is reproducing in simulation: - The ``in-vitro`` experiments of a slice of isolated mouse cerebellar tissue. - The ``awake`` experiments of behaving mouse. Additionally, the ``awake`` state is derived from the ``in-vitro`` state, as only some cells and connections receive modifications. Neuron models ^^^^^^^^^^^^^ All the default NEST neuron models are available for simulation with BSB. Additionally, BSB allows for the different populations' parameters to be described as distribution or plain values: .. code-block:: yaml simulations: simulation_name: simulator: nest seed: 1234 cell_models: granule_cell: model: eglif_cond_alpha_multisyn constants: t_ref: 1.5 I_e: -0.888 Vinit: distribution: normal mean: -62.0 std: 20.0 The ``distribution`` parameter correspond to one of the functions of the NEST `random module `_. It can be useful to randomize certain parameters such as the initial membrane potential to avoid synchrony of activity at the start of the simulation. .. note:: As in the previous configuration snippet, you can set the NEST random seed with BSB using the ``seed`` parameter. Parrot neurons ############## `Parrot neurons `_ are the most basic neuron model of NEST. They basically emits one spike for every incoming spike they receive. We are using them here to represent the mossy fibers and glomerulus population as these fibers only transmit spikes coming from other regions of the brain. Geminiani EGLIF ############### This model is based on the work of Geminiani et al. [#geminiani_2018]_ [#geminiani_2019]_ [#geminiani_2024]_. Details about the model are available in :doc:`this page `. De Grazia EGLIF ############### This model is based on the work of De Grazia et al. [#degrazia_2026]_. Details about the model are available in :doc:`this page `. Synapse models ^^^^^^^^^^^^^^ Static synapse ############## By default, NEST `static synapses `_ are used to connect the different neurons together. This model only transmit spikes as weights to postsynaptic neurons after a provided delay. Tsodyks Markram Synapse ####################### The Tsodyks-Markram synapse model implements synaptic short-term depression and short-term facilitation according to Tsodyks et al. [#tsodyks_1997]_ and Fuhrman et al. [#fuhrman_2002]_. This connection model merely scales the synaptic weight, based on the spike history parameters of the kinetic model. Two versions of this model are available on NEST: - `tsodyks_synapse `_ - `tsodyks2_synapse `_ Synapse parameters ################## Synaptic parameters depend on the neuron model chosen for the simulation. Find more information on these parameters in the neuron model associated pages. .. _nest-paradigms: Simulation paradigms ^^^^^^^^^^^^^^^^^^^^ Different configuration files are available to reproduce experiments with the cerebellar cortex circuit. As for the circuit reconstructions, the simulations are based on a basic paradigm (see the following section) and can be extended with simulation extensions. To analyze the spiking results of the following simulations, for each neuron population, we define: * the mean firing rate as the mean of each of its (spiking at least once) neurons' total number of spikes over the simulation time, expressed in Hz. * the mean Inter-Spike intervals (ISI) as the mean of each of its (spiking at least twice) neurons mean duration between each of its pair of consecutive spike, expressed in ms. .. _basal-activity: Basal activity ############## The basal activity configuration file `basal.yaml `_ implements to the default activity of the cerebellar cortex circuit. This simulation is set to last ``5000 ms`` (with a ``0.1 ms`` timestep) during which the neurons are only stimulated with ``background noise`` represented as a ``4 Hz`` ``Poisson spike generator`` on the mossy fibers population. Each neuron spiking activity is additionally recorded. This simulation should demonstrate the activity of the network in a stable state. The results of this simulation serves as a baseline for the following ones. Mossy fiber stimulus #################### The mossy fiber stimulus configuration file `stimulus_mossy.yaml `_ is derived from the ``basal_activity`` but adds a stimulus of the mossy fibers (see :ref:`basal-activity`). On top of the basal paradigm, we introduce here a ``stimulus`` represented as a ``150 Hz`` ``Poisson spike generator`` between ``1200`` and ``1250`` ms. This latter targets the ``mossy_fibers`` that are within a ``sphere`` of radius ``90`` :math:`\mu m` and a center at ``(150.0, 65.0, 100.0)``. This mimic the integration of an input on the mossy fibers. Eyeblink Classical Conditioning ############################### To test the functionality of the entire olivocerebellar network (cerebellar cortex + deep cerebellar nuclei + inferior olive), another stimulation protocol was used. It simulates the Eyeblink Classical Conditioning, a Pavlovian conditioning consisting in a conditioned stimulus (CS), typically a light, paired with an unconditioned stimulus (US), usually an air puff to the eye. According to Geminiani et al., 2024 [#geminiani_2024]_, a CS of ``40 Hz`` arrives on ``mossy_fibers`` in the interval ``[1000, 1250] ms``, while a US of ``500 Hz`` arrives as a burst on ``io`` in the interval ``[1250, 1260] ms``. Simulation results ^^^^^^^^^^^^^^^^^^ Simulation results depend on which cells types are included in the ``canonical circuit``, the state, the simulation paradigm, and the neuron and synapse models used. The results for the ``eglif_cond_alpha_multisyn`` with ``static_synapse`` are available in :doc:`this page `. References ^^^^^^^^^^ .. include-nestml:: ../../../../cerebellar_models/nest_models/eglif_cond_alpha_multisyn.nestml :start-after: start-references :end-before: See also .. include-nestml:: ../../../../cerebellar_models/nest_models/eglif_multirec.nestml :start-after: start-references :end-before: See also .. [#geminiani_2019] Geminiani, A., Casellato, C., D’Angelo, E., & Pedrocchi, A. (2019). Complex electroresponsive dynamics in olivocerebellar neurons represented with extended-generalized leaky integrate and fire models. Frontiers in Computational Neuroscience, 13, 35. https://doi.org/10.3389/fncom.2019.00035 .. [#geminiani_2024] Geminiani, A., Casellato, C., Boele, H. J., Pedrocchi, A., De Zeeuw, C. I., & D’Angelo, E. (2024). Mesoscale simulations predict the role of synergistic cerebellar plasticity during classical eyeblink conditioning. PLOS Computational Biology, 20(4), e1011277. https://doi.org/10.1371/journal.pcbi.1011277 .. [#tsodyks_1997] Tsodyks MV, Markram H (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. PNAS, 94(2):719-23. https://doi.org/10.1073/pnas.94.2.719 .. [#fuhrman_2002] Fuhrman, G, Segev I, Markram H, Tsodyks MV (2002). Coding of temporal information by activity-dependent synapses. Journal of Neurophysiology, 87(1):140-8. https://doi.org/10.1152/jn.00258.2001