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API Reference

Complete API reference for brainpy.state.

Note

brainpy.state is built on top of brainstate, brainunit, and braintools.

Organization

The API is organized into the following categories:

.. grid:: 1 2 2 3

   .. grid-item-card:: :material-regular:`psychology;2em` Neurons
      :link: neurons.html

      Spiking neuron models (LIF, ALIF, Izhikevich, HH, etc.)

   .. grid-item-card:: :material-regular:`timeline;2em` Synapses
      :link: synapses.html

      Synaptic dynamics (Expon, Alpha, AMPA, GABA, NMDA)

   .. grid-item-card:: :material-regular:`account_tree;2em` Projections
      :link: projections.html

      Connect neural populations (AlignPostProj, DeltaProj, etc.)

   .. grid-item-card:: :material-regular:`output;2em` Synaptic Outputs
      :link: synouts.html

      Convert conductances to currents (COBA, CUBA, MgBlock)

   .. grid-item-card:: :material-regular:`psychology_alt;2em` Short-Term Plasticity
      :link: stp.html

      Short-term synaptic plasticity (STP, STD)

   .. grid-item-card:: :material-regular:`sensors;2em` Readouts
      :link: readouts.html

      Readout layers (LeakyRateReadout, LeakySpikeReadout)

   .. grid-item-card:: :material-regular:`input;2em` Input Generators
      :link: inputs.html

      Spike and current generators (PoissonSpike, SpikeTime)

Example Reference

Neurons

import brainpy
import brainunit as u

# Leaky Integrate-and-Fire
brainpy.state.LIF(100, V_rest=-70*u.mV, V_th=-50*u.mV, tau=20*u.ms)

# Adaptive LIF
brainpy.state.ALIF(100, tau=20*u.ms, tau_w=144*u.ms, a=4*u.nS, b=0.0805*u.nA)

# Izhikevich
brainpy.state.Izhikevich(100, a=0.02/u.ms, b=0.2/u.ms, c=-65*u.mV, d=8*u.mV/u.ms)

# Hodgkin-Huxley
brainpy.state.HH(100, ENa=50*u.mV, EK=-77*u.mV, EL=-54.387*u.mV)

Synapses

# Exponential synapse
brainpy.state.Expon.desc(100, tau=5*u.ms)

# Dual exponential synapse
brainpy.state.DualExpon.desc(100, tau_rise=1*u.ms, tau_decay=10*u.ms)

# Alpha synapse
brainpy.state.Alpha.desc(100, tau=8*u.ms)

# AMPA receptor
brainpy.state.AMPA.desc(100, alpha=0.98/(u.ms*u.mM), beta=0.18/u.ms)

# GABAa receptor
brainpy.state.GABAa.desc(100, alpha=0.53/(u.ms*u.mM), beta=0.18/u.ms)

# NMDA receptor
brainpy.state.BioNMDA.desc(100, alpha1=2.0/u.ms, beta1=0.01/u.ms)

Projections

# AlignPost projection with communication, synapse, and output
brainpy.state.AlignPostProj(
    comm=brainstate.nn.EventFixedProb(n_pre, n_post, prob=0.1, weight=0.5*u.mS),
    syn=brainpy.state.Expon.desc(n_post, tau=5*u.ms),
    out=brainpy.state.COBA.desc(E=0*u.mV),
    post=post_neurons
)

# Delta projection for direct input
brainpy.state.DeltaProj(
    comm=lambda x: x * 10*u.mV,
    post=post_neurons
)

# Current projection
brainpy.state.CurrentProj(
    comm=lambda x: x * 0.5,
    out=brainpy.state.CUBA.desc(scale=1*u.nA),
    post=post_neurons
)

Synaptic Outputs

# Conductance-based output
brainpy.state.COBA.desc(E=0*u.mV)  # excitatory reversal potential

# Current-based output
brainpy.state.CUBA.desc(scale=1*u.mV)

# NMDA with magnesium block
brainpy.state.MgBlock.desc(E=0*u.mV, cc_Mg=1.2*u.mM, alpha=0.062, beta=3.57)

Short-Term Plasticity

# Short-term plasticity (facilitation + depression)
brainpy.state.STP.desc(100, U=0.15, tau_f=1500*u.ms, tau_d=200*u.ms)

# Short-term depression only
brainpy.state.STD.desc(100, tau=200*u.ms, U=0.07)

Input Generators

# Spike times
brainpy.state.SpikeTime(100, times=[10, 20, 30]*u.ms, indices=[0, 10, 20])

# Poisson spike generator
brainpy.state.PoissonSpike(100, freqs=50*u.Hz)

# Poisson encoder (dynamic rates)
encoder = brainpy.state.PoissonEncoder(100)
spikes = encoder.update(rates)  # rates: array of firing rates

# Poisson input to a state variable
brainpy.state.PoissonInput(
    target=neuron.V,
    indices=None,
    num_input=200,
    freq=50*u.Hz,
    weight=0.1*u.mV
)

Readout Layers

# Leaky rate-based readout
brainpy.state.LeakyRateReadout(in_size=100, out_size=10, tau=5*u.ms)

# Leaky spiking readout
brainpy.state.LeakySpikeReadout(in_size=100, tau=5*u.ms, V_th=1*u.mV)
.. toctree::
   :hidden:
   :maxdepth: 2

   neurons
   synapses
   projections
   synouts
   stp
   readouts
   inputs