ALLEN INSTITUTE

# PointNet

PointNet is a simulation engine that utilizes NEST to run large-scale point neuron network models. Features including:

- Run the same simulation on a single core or in parallel on an HPC cluster with no extra programming required.

- Supports any spiking neuron models (rates models in development) available in NEST or with user contributed modules.

- Records neuron spiking, multi-meter recorded variables into the optimized SONATA data format.

## Inputs

Inputs can be specified in the “inputs” sections of the simulation config, following the rules specified in the SONATA Data format.

### Spike-Trains

Cells with `model_type` value `virtual` are equivalent to NEST’s spike_generator models which will play a pre-recorded series of spikes throughout the simulation. You may use either a SONATA spike-train file, an NWB file, or a space-separated csv file with columns node_id, population, and timestamps. Examples of how to create your own spike-train files can be found here.

    {
        "LGN_spikes": {
            "input_type": "spikes",
            "module": "sonata",
            "input_file": "./inputs/lgn_spikes.h5",
            "node_set": {"population": "lgn"}
        }
    }

- module: either sonata, hdf5, csv, or nwb: depending on the format of the spikes file

- node_set: used to filter which cells will receive the inputs

- input_file: path to file contain spike-trains for one or mode node

### Extracelluar ElectroPhysiology (ECEPhys) Probe Data (NWB 2.0) Spikes

An increasing number of ECEPhys electrode experimental data is being release to the public in NWB format, such as the Allen Visual Coding - Neuropixels dataset or through DANDI. While it is possible to manually convert this data into SONATA spike-trains to encorpate into your simulations, the ecephys_probe spikes module can do this automatically; fetching spikes from ECEPhys units and converting them to virtual cells for network input into your model.

For example, using a session NWB downloaded using the AllenSDK, the below example wil randomly l map “LGd” cells from the session onto our “LGN” population, and filter out only spikes that occur between 10.0 and 12.0 seconds

    {
      "inputs": {
        "LGN_spikes": {
          "input_type": "spikes",
          "module": "ecephys_probe",
          "input_file": "./session_715093703.nwb",
          "node_set": {"population": "LGN"},
          "mapping": "sample_with_replacement",
          "units": {
            "location": "LGd"
          },
          "interval": [10000.0, 12000.0]
        }
      }
    }

See the documentation for more information and advanced features.

### Current Clamps

May use one step current clamp on multiple nodes, or have one node receive multiple current injections.

    {
        "current_clamp_1": {
            "input_type": "current_clamp",
            "module": "IClamp",
            "node_set": "biophys_cells",
            "amp": 0.1500,
            "delay": 500.0,
            "duration": 500.0
        }
    }

See documentation for more details on using current clamp inputs.

## Outputs

### Spikes

By default all non-virtual cells in the circuit will have all their spikes at the soma recorded.

### Membrane and Intracellular Variables

Used to record the time trace of specific cell variables, usually the membrane potential (v). This is equivalent to NEST’s multimeter object.

    {
        "membrane_potential": {
            "module": "multimeter_report",
            "cells": {"population": "V1"},
            "variable_name": "V_m"
            "file_name": "cai_traces.h5"
        }
    }

- module: either mutlimeter_report or membrane_report, both the same

- variable_name: name of variable being recorded, will depend on the nest cell model.

- cells: a node_set filter out what cells to record.

- file_name: name of file where traces will be recorded, under the “output_dir”. If not specified the the report title

    will be used, eg “calcium_concentration.h5” and “membrane_potential.h5”

### Recording Synaptic Weights

Used to record the changes to synaptic weight changes throughout the simulation lifetime. Useful for measuring changes plastic synapse models like “stdp_synapse” or “tsodyks_synapses” (can be used for static synapses though values will never change). To create a recorder add the following section to the “reports” section in the simulation config json:

    {
        "reports": {
            "<name>": {
                "module": "weight_recorder",
                "nest_model": "<original-nest-model>",
                "model_template": "<recorder-name>",
                "file_name": "<file-name>.csv"
            }
        }
    }

Which will create a special synpase model called “<recorder-name>”, which is just a version of <original-nest-model> that will save a trace of synapic changes to the csv file output/<file-name>.csv. Just set model_template property value to “<recorder-name>” in the edge-types file.

For example, to record the changes to a subset of the stdp_synapse type NEST models, add the following to the configuration:

    {
        "reports": {
            "weight_recorder_stdp_1": {
                "module": "weight_recorder",
                "nest_model": "stdp_synapse",
                "model_template": "stdp_synapse_recorder",
                "file_name": "stdp_weights.csv",
            }
        }
    }

Then make changes to edge_types.csv file

edge_type_id | model_template | dynamics_params | …
--- | --- | --- | ---
100 | stdp_synapse_recorder | stdp_params.json | …