Models

AbstractPopulation

An abstract type representing a population. Any struct inheriting from this type must implement:

Methods

• integrate!(p::NeuronModel, param::NeuronModelParam, dt::Float32): Integrates the neuron model over a time step dt using the given parameters.

This is a struct representing a spiking neural network model that include two dendrites and a soma based on the adaptive exponential integrate-and-fire model (AdEx)

Fields

• t::VIT : tracker of simulation index [0]

• param::AdExSoma : Parameters for the AdEx model.

• N::Int32 : The number of neurons in the network.

• d::VDT: Dendritic compartment parameters.

• v_s::VFT : Somatic membrane potential.

• w_s::VFT : Adaptation variables for each soma.

• v_d::VFT: Dendritic membrane potential for dendrite.

• g_s::MFT , g_d::MFT : Conductance of somatic and dendritic synapses.

• h_s::MFT, h_d::MFT : Synaptic gating variables.

• fire::VBT : Boolean array indicating which neurons have fired.

• after_spike::VFT : Post-spike timing.

• postspike::PST : Model for post-spike behavior.

• θ::VFT : Individual neuron firing thresholds.

• records::Dict : A dictionary to store simulation results.

• Δv::VFT , Δv_temp::VFT : Variables to hold temporary voltage changes.

• cs::VFT , is::VFT : Temporary variables for currents.

Hodgkin–Huxley Neuron

[Integrate-And-Fire Neuron](https://neuronaldynamics.epfl.ch/online/Ch1.S3.html)

Izhikevich Neuron

Morris-Lecar Neuron

Poisson Neuron

Rate Neuron

This is a struct representing a spiking neural network model that include two dendrites and a soma based on the adaptive exponential integrate-and-fire model (DendNeuronParameter)

Fields

• t::VIT : tracker of simulation index [0]
• param::AdExSoma : Parameters for the AdEx model.
• N::Int32 : The number of neurons in the network.
• d1::VDT, d2::VDT : Dendrite structures.
• NMDA::NMDAT : Specifies the properties of NMDA (N-methyl-D-aspartate) receptors.
• gax1::VFT, gax2::VFT : Axial conductance (reciprocal of axial resistance) for dendrite 1 and 2 respectively.
• cd1::VFT, cd2::VFT : Capacitance for dendrite 1 and 2.
• gm1::VFT, gm2::VFT : Membrane conductance for dendrite 1 and 2.
• v_s::VFT : Somatic membrane potential.
• w_s::VFT : Adaptation variables for each soma.
• v_d1::VFT , v_d2::VFT : Dendritic membrane potential for dendrite 1 and 2.
• g_s::MFT , g_d1::MFT, g_d2::MFT : Conductance of somatic and dendritic synapses.
• h_s::MFT, h_d1::MFT, h_d2::MFT : Synaptic gating variables.
• fire::VBT : Boolean array indicating which neurons have fired.
• after_spike::VFT : Post-spike timing.
• postspike::PST : Model for post-spike behavior.
• θ::VFT : Individual neuron firing thresholds.
• records::Dict : A dictionary to store simulation results.
• Δv::VFT , Δv_temp::VFT : Variables to hold temporary voltage changes.
• cs::VFT , is::VFT : Temporary variables for currents.

AbstractPopulationParameter <: AbstractParameter

An abstract type representing a population parameter.

DendNeuronParameter{FT, IT, DT, ST, NMDAT, PST, PT}

A parameter struct for the Tripod neuron model, implementing an Adaptive Exponential Integrate-and-Fire (AdEx) model with dendritic compartments.

Fields

• C::FT: Membrane capacitance (default: 281pF)
• gl::FT: Leak conductance (default: 40nS)
• R::FT: Total membrane resistance (default: nS/gl * GΩ)
• τm::FT: Membrane time constant (default: C/gl)
• Er::FT: Resting potential (default: -70.6mV)
• Vr::FT: Reset potential (default: -55.6mV)
• Vt::FT: Rheobase threshold (default: -50.4mV)
• ΔT::FT: Slope factor (default: 2mV)
• τw::FT: Adaptation current time constant (default: 144ms)
• a::FT: Subthreshold adaptation conductance (default: 4nS)
• b::FT: Spike-triggered adaptation increment (default: 80.5pA)
• AP_membrane::FT: After-potential membrane parameter (default: 10.0f0mV)
• BAP::FT: Backpropagating action potential parameter (default: 1.0f0mV)
• up::FT: Spike upstroke duration (default: 1ms)
• τabs::FT: Absolute refractory period (default: 2ms)
• postspike::PST: Post-spike dynamics (default: PostSpike(A=10, τA=30ms))
• ds::DT: Dendritic segment lengths (default: [200um, (200um, 400um)])
• physiology::PT: Dendritic physiology (default: human_dend)
• soma_syn::ST: Soma synapse type (default: TripodSomaSynapse)
• dend_syn::ST: Dendritic synapse type (default: TripodDendSynapse)
• NMDA::NMDAT: NMDA voltage dependency parameters (default: NMDAVoltageDependency(mg=MgmM, b=nmdab, k=nmda_k))

Type Parameters

• FT: Floating-point type for membrane parameters (default: Float32)
• IT: Integer type for time-related parameters (default: Int64)
• DT: Type for dendritic segment lengths (default: Vector{DendLength})
• ST: Synapse type (default: Synapse)
• NMDAT: NMDA voltage dependency type (default: NMDAVoltageDependency{Float32})
• PST: Post-spike dynamics type (default: PostSpike{Float32})
• PT: Physiology type (default: Physiology)

Examples

julia> TripodParameter = DendNeuronParameter(ds = [200um, (200um, 400um)])
DendNeuronParameter{Float32, Int64, Vector{DendLength}, Synapse, NMDAVoltageDependency{Float32}, PostSpike{Float32}, Physiology}(281.0, 40.0, 25.0, 7.03125, -70.6, -55.6, -50.4, 2.0, 144.0, 4.0, 80.5, 10.0, 1.0, 1, 2, PostSpike{Float32}(10, 30.0), [200.0, (200.0, 400.0)], human_dend, TripodSomaSynapse, TripodDendSynapse, NMDAVoltageDependency{Float32}(0.001, 0.062, 3.57))

julia> BallAndStickParameter = DendNeuronParameter(ds = [(150um, 400um)])
DendNeuronParameter{Float32, Int64, Vector{DendLength}, Synapse, NMDAVoltageDependency{Float32}, PostSpike{Float32}, Physiology}(281.0, 40.0, 25.0, 7.03125, -70.6, -55.6, -50.4, 2.0, 144.0, 4.0, 80.5, 10.0, 1.0, 1, 2, PostSpike{Float32}(10, 30.0), [(150.0, 400.0)], human_dend, TripodSomaSynapse, TripodDendSynapse, NMDAVoltageDependency{Float32}(0.001, 0.062, 3.57))

AbstractConnection

An abstract type representing a connection. Any struct inheriting from this type must implement:

Methods

• forward!(c::Synapse, param::SynapseParameter): Propagates the signal through the synapse.
• plasticity!(c::Synapse, param::SynapseParameter, dt::Float32, T::Time): Updates the synapse parameters based on plasticity rules.

AbstractNormalization <: AbstractConnection

An abstract type representing a normalization connection.

AbstractSparseSynapse <: AbstractConnection

An abstract type representing a sparse synapse connection.

SynapseNormalization(N; param, kwargs...)

Constructor function for the SynapseNormalization struct.

• N: The number of synapses.
• param: Normalization parameter, can be either MultiplicativeNorm or AdditiveNorm.
• kwargs: Other optional parameters.

Returns a SynapseNormalization object with the specified parameters.

Force Learning Sparse Synapse

Force Learning Full Synapse

PINing Sparse Synapse

PINing Sparse Synapse

Rate Synapse

Rate Synapse

SpikingSynapse to connect neuronal populations

SynapseNormalization{VFT = Vector{Float32}, VIT = Vector{Int32}, MFT = Matrix{Float32}}

A struct that holds parameters for synapse normalization, including:

• param: Normalization parameter, can be either MultiplicativeNorm or AdditiveNorm.
• t: A vector of integer values representing time points.
• W0: A vector of initial weights before simulation.
• W1: A vector of weights during the simulation.
• μ: A vector of mean synaptic weights.
• records: A dictionary for storing additional data.

SynapseNormalization(N; param, kwargs...)

Constructor function for the SynapseNormalization struct.

• N: The number of synapses.
• param: Normalization parameter, can be either MultiplicativeNorm or AdditiveNorm.
• kwargs: Other optional parameters.

Returns a SynapseNormalization object with the specified parameters.

AbstractStimulus

An abstract type representing a stimulus. Any struct inheriting from this type must implement:

Methods

• stimulate!(p::Stimulus, param::StimulusParameter, time::Time, dt::Float32): Applies the stimulus to the population.

BalancedStimulus(post::T, sym::Symbol, r::Union{Function, Float32}, neurons=[]; N_pre::Int=50, p_post::R=0.05f0, μ::R=1.f0, param=BalancedParameter()) where {T <: AbstractPopulation, R <: Number}

Constructs a BalancedStimulus object for a spiking neural network.

Arguments

• post::T: The target population for the stimulus.
• sym::Symbol: The symbol representing the synaptic conductance or current.
• r::Union{Function, Float32}: The firing rate of the stimulus. Can be a constant value or a function of time.
• neurons=[]: The indices of the neuronsin the target population that receive the stimulus. If empty, neuronsare randomly selected based on the probability p_post.
• N::Int=200: The number of Balanced neurons neurons.
• N_pre::Int=5: The number of presynaptic connected.
• p_post::R=0.05f0: The probability of connection between presynaptic and postsynaptic neurons.
• μ::R=1.f0: The scaling factor for the synaptic weights.
• param=BalancedParameter(): The parameters for the Balanced distribution.

Returns

A BalancedStimulus object.

AbstractStimulusParameter <: AbstractParameter

An abstract type representing a stimulus parameter.