Struct SpikingNeuron 

pub struct SpikingNeuron {
    pub membrane_potential: f64,
    pub threshold: f64,
    pub refractory_period: f64,
    pub time_since_spike: f64,
    pub leak_constant: f64,
    pub input_current: f64,
    pub position: Vec<f64>,
    pub learning_rate: f64,
}

Spiking neuron model using leaky integrate-and-fire dynamics

This neuron model integrates input currents over time and generates spikes when the membrane potential exceeds a threshold. After spiking, the neuron enters a refractory period during which it cannot spike again.

Model Dynamics

The membrane potential follows the equation: dV/dt = -leak_constant * V + I(t)

Where V is membrane potential and I(t) is input current.

Example

use scirs2_spatial::neuromorphic::core::SpikingNeuron;

let mut neuron = SpikingNeuron::new(vec![0.0, 0.0]);

// Simulate neuron for several time steps
let dt = 0.1;
let input_current = 1.5;

for _ in 0..20 {
    let spiked = neuron.update(dt, input_current);
    if spiked {
        println!("Neuron spiked!");
    }
}

Fields

membrane_potential: f64

Membrane potential

threshold: f64

Spike threshold

refractory_period: f64

Refractory period

time_since_spike: f64

Time since last spike

leak_constant: f64

Leak constant

input_current: f64

Input current

position: Vec<f64>

Neuron position in space

learning_rate: f64

Learning rate

Implementations

impl SpikingNeuron

pub fn new(position: Vec<f64>) -> Self

Create new spiking neuron

Arguments

• position - Spatial position of the neuron in N-dimensional space

Returns

A new SpikingNeuron with default parameters

pub fn with_params( position: Vec<f64>, threshold: f64, refractory_period: f64, leak_constant: f64, learning_rate: f64, ) -> Self

Create a new spiking neuron with custom parameters

Arguments

• position - Spatial position of the neuron
• threshold - Spike threshold
• refractory_period - Duration of refractory period
• leak_constant - Membrane leak constant
• learning_rate - Learning rate for adaptation

pub fn update(&mut self, dt: f64, input_current: f64) -> bool

Update neuron state and check for spike

Integrates the neuron dynamics for one time step and determines if a spike should be generated.

Arguments

• dt - Time step size
• input_current - Input current for this time step

Returns

True if the neuron spiked, false otherwise

pub fn calculate_influence(&self, other_position: &[f64]) -> f64

Calculate distance-based influence on another neuron

Computes the spatial influence this neuron has on another neuron based on their relative positions using a Gaussian function.

Arguments

• other_position - Position of the other neuron

Returns

Influence strength (0 to 1)

pub fn position(&self) -> &[f64]

Get neuron position

pub fn set_position(&mut self, position: Vec<f64>)

Set neuron position

pub fn membrane_potential(&self) -> f64

Get membrane potential

pub fn set_membrane_potential(&mut self, potential: f64)

Set membrane potential

pub fn threshold(&self) -> f64

Get spike threshold

pub fn set_threshold(&mut self, threshold: f64)

Set spike threshold

pub fn refractory_period(&self) -> f64

Get refractory period

pub fn set_refractory_period(&mut self, period: f64)

Set refractory period

pub fn leak_constant(&self) -> f64

Get leak constant

pub fn set_leak_constant(&mut self, leak: f64)

Set leak constant

pub fn learning_rate(&self) -> f64

Get learning rate

pub fn set_learning_rate(&mut self, rate: f64)

Set learning rate

pub fn is_refractory(&self) -> bool

Check if neuron is in refractory period

pub fn time_since_spike(&self) -> f64

Get time since last spike

pub fn reset(&mut self)

Reset neuron to initial state

pub fn inject_current(&mut self, current: f64)

Inject current into the neuron

pub fn distance_to(&self, other: &SpikingNeuron) -> Option<f64>

Calculate distance to another neuron

Arguments

• other - Reference to another neuron

Returns

Euclidean distance between neurons, or None if dimensions don’t match

pub fn adapt_threshold( &mut self, target_rate: f64, actual_rate: f64, adaptation_rate: f64, )

Adapt threshold based on recent activity (homeostatic plasticity)

Arguments

• target_rate - Target firing rate
• actual_rate - Actual firing rate
• adaptation_rate - Rate of threshold adaptation

pub fn adapt_learning_rate( &mut self, performance_factor: f64, adaptation_rate: f64, )

Update learning rate based on recent performance

Arguments

• performance_factor - Factor indicating learning performance (0-1)
• adaptation_rate - Rate of learning rate adaptation

Trait Implementations

impl Clone for SpikingNeuron

fn clone(&self) -> SpikingNeuron

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for SpikingNeuron

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.