DPointNet#

DPointNet is a BMTK engine for the training and simulation of large-scale biorealistic neuronal circuits utilizing deep-learning techniques. Users can take a novel or existing network and train and save synpatic weights using pre-determined inputs and expected outputs. It can also be used for purely inference/simulation of large scale networks often in a way that is much faster than other simulators.

For more information see paper on using software for building and simulation of cortical circuit model:

Ito et. al., 2026

Installation#

DPointNet must be installed using the base bmtk package as seen in the instllation guid.

Besides the base dependencies, it requires tensorflow 2.14 to 2.16, and may work with gpu or without. To install with gpu run the following in your environment:

$ pip install tensorflow[and-cuda]

or without a gpu:

$ pip install tensorflow

Overview#

SONATA

Python

DPointNet Initialization#

Setting up a simulation environment/workspace#

Initializing DPointNet instance#

First we must instantiate a DPointNet simulator instance, which passing in parameters required to build and run the model. Some of these parameters may be changed later on.

SONATA

{
  "target_simulator": "DPointNet",

  "run": {
    "seq_len": 500,
    "dt": 1.0,
    "default_seed": 3000,
    "batch_size": 10,
    "train_recurrent_weights": true,
    "dtype": "float32",
    "single_gpu_strategy": "one_device"
  }
}

Python

from bmtk.simulator import dpointnet

rnn = dpointnet.RNN(
    seqlen=500.0,
    dt=1.0,
    default_seed=3000,
    batch_size=10,
    dtype="float32",
    train_recurrent_weights=True,
    single_gpu_strategy="one_device",
)

Available “run” options

option

description

default

seq_len

The number of time steps that will be used in training and inference

dt

The time interval, in milliseconds, for each sequence step

1.0

default_seed

The default RNG seed to use when building the model and any of DPointNet functions (like spike generators or training) - when not explicity stated.

batch_size

The default number of simulataneous batches for processing during feed-forward input into the RNN. May be overridden for training and inference.

1

Setting hyper-parameters for training/inference#

Next we must set hyper-parameters that are used by the back-end deep-learning model. You must first specify the cell_model which takes care of reproducing the internal simulation output plus rules for determining gradient calculations. Current DPointNet only supports GLIF3Cell model that reproduces the GLIF point-neuron models.

SONATA

{
  "rnn_cell_params": {
    "cell_model": "GLIF3Cell",
    "<parameter_1>": <value_1>,
    "<parameter_2>": <value_2>,
    "<parameter_3>": <value_3>,
    ...
  }
}

Python

Available “rnn_cell_params” options

GLIF3Cell

option	description	default
gauss_std		0.5
dampening_factor		0.3
recurrent_dampening_factor		0.5
voltage_gradient_dampening		0.5
recurrent_weight_scale		1.0
lr_scale		1.0
max_delay		5
pseudo_gauss		False
train_recurrent		True
train_recurrent_per_type		True
noise_seed		0
hard_reset		False
tau_basis		<None>
synaptic_basis_weights		<None>

Setting the Network Model#

Before either training or inference can begin, DPointNet must have instantiated network files that describes the cells, synapses, and all the required properties. You can build a network from scratch using the BMTK Network Builder, or pre-built models like the ones developed at the Allen Institute or from other labs.

SONATA

"networks": {
  "nodes": [
    {
      "nodes_file": "$NETWORK_DIR/glifs_nodes.h5",
      "node_types_file": "$NETWORK_DIR/glifs_node_types.csv"
    },
    {
      "nodes_file": "$NETWORK_DIR/virts_nodes.h5",
      "node_types_file": "$NETWORK_DIR/virts_node_types.csv"
    }
    ],
    "edges": [
    {
      "edges_file": "$NETWORK_DIR/glifs_glifs_edges.h5",
      "edge_types_file": "$NETWORK_DIR/glifs_glifs_edge_types.csv"
    },
    {
      "edges_file": "$NETWORK_DIR/virts_glifs_edges.h5",
      "edge_types_file": "$NETWORK_DIR/virts_glifs_edge_types.csv"
    }
  ]
}

Python

import numpy

Network requirements#

instantiating the Network#

Networking options#

Network Components#

Combining multiple networks together#

Filtering for subnetworks#

Input Stimuli#

Spiking Stimulus#

SONATA

{
  "inputs": {
    "<INPUT_NAME1>": {
      "input": "spikes",
      "module": "<INPUT_MOD1>"
      "node_set": "<virtual_pop_1>",
      "<module_params_1>": <value_1>,
      "<module_params_2>": <value_2>,
      ...
    },
    "<INPUT_NAME2>": {
      "input": "spikes",
      "module": "<INPUT_MOD1>"
      "node_set": "<virtual_pop_1>",
      "<module_params_1>": <value_1>,
      "<module_params_2>": <value_2>,
      ...
    }
  }
}

Python

Spike-inputs modules and options#

Building your own inputs module#

Initial Conditions#

SONATA

{
    "initial_states": {
        "<NAME1>": {
            "module": "<INIT_MOD1>",
            "run_on": "all",
            "<module_params_1>": <value_1>,
            "<module_params_2>": <value_2>,
            ...
        },
        "<NAME2>": {
            "module": "<INIT_MOD2>",
            "run_on": "epoch",
            "<module_params_1>": <value_1>,
            "<module_params_2>": <value_2>,
            ...
        }
   }
}

Python

Training Options#

Training hyper-parameters#

Callbacks#

Default Callbacks class#

SONATA

{
  "callbacks": {
    "class": "Callbacks",
    "starting_epoch": 0,
    "callbacks_dir": "training_callbacks_intro_l4_overall_distribution",
    "verbose": "on_step",
    "epoch_store_weights": "latest",
    "epoch_cache_weights": false,
    "sonata_output_dir": "network.trained_weights.best",
    "losses_table_csv": "losses.csv",
    "performance_table_csv": "performance.csv"
  }
}

Python

Available “Callback” options

module

description

default

callbacks_dir

callbacks_outputs

starting_epoch

0

verbose

full

time_fmt

‘%d-%m-%Y %H:%M’

epoch_cache_weights

False

epoch_store_weights

best

sonata_output_dir

trained_weights

losses_table_csv

losses.csv

performance_table_csv

performance.csv

Building your own Callbacks class#

Parameters#

Training Inputs#

Initial State#

Loss Functions#

Built-in Loss Modules

module

description

SpikeRateDistributionTarget

TargetFiringRate

OrientationSelectivityLoss

VoltageRegularization

SynchronizationLoss

EMDWeightRegularization

Training Output#

Running Inference#

Running an Inference#

Results/Output#

SONATA

{
    "output": {
        "output_dir": "$OUTPUT_DIR",
        "log_file": "$OUTPUT_DIR/log.txt",
        "log_level": "INFO",
        "spikes_file": "$OUTPUT_DIR/spikes.h5",
        "overwrite_results": true
    }
}

Python

module	description
random
bernoulli_spikes
poisson_spikes
lgn_tf
spikes_files
custom_spikes_functions

module	description
zero_state
random_state
from_input
cached_states

option	description	default
seq_len	The number of time steps that will be used in training and inference
dt	The time interval, in milliseconds, for each sequence step	1.0
default_seed	The default RNG seed to use when building the model and any of DPointNet functions (like spike generators or training) - when not explicity stated.
batch_size	The default number of simulataneous batches for processing during feed-forward input into the RNN. May be overridden for training and inference.	1

module	description	default
callbacks_dir		callbacks_outputs
starting_epoch		0
verbose		full
time_fmt		‘%d-%m-%Y %H:%M’
epoch_cache_weights		False
epoch_store_weights		best
sonata_output_dir		trained_weights
losses_table_csv		losses.csv
performance_table_csv		performance.csv

module	description
SpikeRateDistributionTarget
TargetFiringRate
OrientationSelectivityLoss
VoltageRegularization
SynchronizationLoss
EMDWeightRegularization