briar 0.1.1

Spiking neural network models of V1 development using Brian2

briar

Large-Scale SNN framework with dendrites, built on Brian2

Research by Zubin Kane -- Makin Lab at Purdue University

Overview

briar provides pre-built architectures for studying how orientation selectivity and phase diversity emerge in visual cortex through spike-timing-dependent plasticity (STDP). It wraps Brian2 with a declarative layer for defining neuron pools, synapse pools, and learning rules, then handles device setup, compilation, monitoring, and result serialization automatically.

Key features:

• Declarative architecture definition -- define pools and synapses as dataclasses, briar generates the Brian2 equations
• Built-in architectures for common V1 models (feedforward, two-layer simple/complex, efficient coding)
• Custom architecture for building networks from scratch
• cpp_standalone support with incremental compilation for fast repeated runs
• Parameter sweeps with automatic grid search
• Rich result objects with summary dashboards, diffs, and architecture-aware plotting

Installation

pip install briar

Quick Start

The simplest way to run an experiment is to pick an architecture and a task, then call buildrun():

from briar import SimpleComplex, NaturalImageTask

task = NaturalImageTask(n_patterns=500, image_size=32)
arch = SimpleComplex(task)
results = arch.buildrun()

This builds the full Brian2 network, runs the simulation, and returns a Results object containing all parameters, spike monitors, weight histories, and connectivity.

Inspecting results

# In Jupyter, just evaluate `results` to see the rich dashboard
results

# In a script, print() gives the same dashboard in plain text
print(results)

# results.summary() also prints the dashboard (same as print)
results.summary()

# Show only parameters that differ from defaults
results.diff()

# Architecture-aware default plots
results.plot()
results.plot(full=True)    # additional diagnostics
results.plot(debug=True)   # low-level debug panels

Modifying parameters

Every architecture parameter can be overridden at construction. The diff() method shows exactly what changed:

arch = SimpleComplex(
    task,
    eta_ff_simple=1e-3,       # increase simple cell learning rate
    ff_complex_radius=6.0,    # widen complex cell receptive fields
)
results = arch.buildrun()

# diff() highlights only the non-default values
results.diff()

Adding pools to an existing architecture

Any architecture can be extended with additional pools after construction. Added pools are automatically discovered and built:

from briar import SimpleComplex, NaturalImageTask, NeuronPool, SynapsePool

task = NaturalImageTask(n_patterns=500, image_size=32)
arch = SimpleComplex(task)

# Add a new neuron pool and connect it
arch.add(NeuronPool(name='readout', n_neurons=8))
arch.add(SynapsePool(
    name='ff_readout',
    source=arch.simple_layer,
    target=arch.readout,
))

results = arch.buildrun()

Architectures

Simple

Feedforward-only: LGN ON/OFF inputs connect to simple cells via STDP. Tests whether feedforward learning alone can produce orientation selectivity with phase diversity.

input_pool (LGN ON/OFF) -> simple_layer (STDP)

from briar import Simple, RetinalWaveTask

task = RetinalWaveTask(n_waves=200, image_size=32)
arch = Simple(task, eta_ff_simple=5e-4)
results = arch.buildrun()

SimpleComplex

Two-layer architecture inspired by Antolik & Bednar (2011) with dendritic predictive coding from Mikulasch et al. (2021). Simple cells learn from LGN input via somatic STDP; complex cells learn from simple cells via dendritic predictive coding. Includes Mexican hat recurrent connections and feedback.

input_pool (LGN ON/OFF) -> simple_layer (somatic, STDP)
simple_layer -> complex_layer (dendritic, predictive coding)
complex_layer -> complex_layer (Mexican hat recurrent)
complex_layer -> simple_layer (Mexican hat feedback, fixed)

from briar import SimpleComplex, NaturalImageTask

task = NaturalImageTask(n_patterns=500, image_size=32)
arch = SimpleComplex(task)
results = arch.buildrun()

EfficientEncoder

Single-layer efficient coding model with feedforward predictive coding, Hebbian recurrent connections, and a decoder for reconstruction loss monitoring.

input_pool -> layer1 (dendritic, predictive coding)
layer1 -> layer1 (dendritic, Hebbian recurrent)
layer1 -> decoder (reconstruction loss)

from briar import EfficientEncoder, BarTask

task = BarTask(image_size=8, n_patterns=1000)
arch = EfficientEncoder(task)
results = arch.buildrun()

Custom

A blank architecture with no pre-defined pools. Build any network from scratch by adding pools and wiring them manually:

from briar import (
    Custom, NeuronPool, SynapsePool, PoissonPool,
    DecoderPool, SimConfig, PlasticityRule,
)
from briar.tasks import BarTask
from briar.datastructures import Compartment, DendriticRule

task = BarTask(image_size=8, n_patterns=500)
arch = Custom(task)

# Input layer
arch.add(PoissonPool(name='input', task=task))

# Hidden layer
arch.add(NeuronPool(name='hidden', n_neurons=16))

# Feedforward synapses with STDP
arch.add(SynapsePool(
    name='ff',
    source=arch.input,
    target=arch.hidden,
    eta=5e-4,
    plasticity_rule=PlasticityRule.STDP,
))

# Spike monitor
arch.hidden.add_monitor('spikes')

results = arch.buildrun()

Parameter Sweeps

Run multiple experiments varying one or more parameters:

from briar import SimpleComplex, NaturalImageTask, sweep

task = NaturalImageTask(n_patterns=500)

# Sweep a single parameter
results = sweep(
    SimpleComplex, task, use_cpp=False,
    eta_ff_simple=[5e-5, 5e-4, 5e-3, 5e-2, 5e-1],
)

# Mix fixed overrides with swept parameters
results = sweep(
    SimpleComplex, task,
    ff_complex_radius=6.0,                          # scalar -> fixed
    eta_ff_simple=[5e-5, 5e-4, 5e-3, 5e-2, 5e-1],  # list -> swept
)

# Multi-parameter grid (Cartesian product)
results = sweep(
    SimpleComplex, task,
    eta_ff_simple=[1e-4, 1e-3],
    eta_ff_complex=[5e-5, 5e-4],
)  # 4 runs total

Comparison Plots

Compare results from a sweep (or manually loaded pickles) side-by-side:

from briar import plot_raster_comparison, plot_rate_ridgeline, Results

# Stacked raster plots
plot_raster_comparison(results, layer='simple')
plot_raster_comparison(results, layer='complex')

# Ridgeline firing rate distributions
plot_rate_ridgeline(results, layer='simple')
plot_rate_ridgeline(results, layer='complex')

# Works with manually loaded results too
r1 = Results.load('dumps/SimpleComplex/NaturalImageTask/20260304_091654.pkl')
r2 = Results.load('dumps/SimpleComplex/NaturalImageTask/20260304_131307.pkl')
plot_raster_comparison([r1, r2], labels=['eta=1e-4', 'eta=1e-3'])

Testing

# All tests
pytest

# Fast tests only - runtime mode, fastest in sequential (not parallel)
pytest -m "not slow" --override-ini="addopts="

# Slow tests only - cpp_standalone compile+run
pytest -m slow

# Slow test *examples* for a specific architecture
pytest -m encoder
pytest -m "slow and simple_complex"