scirs2_series/advanced_fusion_intelligence/

neuromorphic.rs

//! Neuromorphic Computing Components for Advanced Fusion Intelligence
//!
//! This module contains all neuromorphic computing related structures and implementations
//! for the advanced fusion intelligence system, including spiking neural networks,
//! synaptic plasticity, and bio-inspired adaptive mechanisms.

use scirs2_core::ndarray::Array1;
use scirs2_core::numeric::{Float, FromPrimitive};
use std::collections::HashMap;
use std::fmt::Debug;

use crate::error::Result;

/// Advanced spiking neural network layer
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AdvancedSpikingLayer<F: Float + Debug> {
    neurons: Vec<SpikingNeuron<F>>,
    connections: Vec<SynapticConnection<F>>,
    learning_rate: F,
}

/// Individual spiking neuron implementation
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct SpikingNeuron<F: Float + Debug> {
    potential: F,
    threshold: F,
    reset_potential: F,
    tau_membrane: F,
}

/// Synaptic connection between neurons
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct SynapticConnection<F: Float + Debug> {
    weight: F,
    delay: F,
    plasticity_rule: PlasticityRule,
}

/// Neural plasticity learning rules
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum PlasticityRule {
    /// Spike-timing dependent plasticity
    STDP,
    /// Bienenstock-Cooper-Munro rule
    BCM,
    /// Hebbian learning rule
    Hebbian,
    /// Anti-Hebbian learning rule
    AntiHebbian,
}

/// Advanced dendritic tree structure for neural computation
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AdvancedDendriticTree<F: Float + Debug> {
    branches: Vec<DendriticBranch<F>>,
    integration_function: IntegrationFunction,
    backpropagation_efficiency: F,
}

/// Individual dendritic branch component
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct DendriticBranch<F: Float + Debug> {
    length: F,
    diameter: F,
    resistance: F,
    capacitance: F,
}

/// Neural integration function types
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum IntegrationFunction {
    /// Linear integration function
    Linear,
    /// Non-linear integration function
    NonLinear,
    /// Sigmoid integration function
    Sigmoid,
    /// Exponential integration function
    Exponential,
}

/// Synaptic plasticity manager for adaptive learning
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct SynapticPlasticityManager<F: Float + Debug> {
    plasticity_rules: Vec<PlasticityRule>,
    adaptation_rates: Vec<F>,
    homeostatic_scaling: bool,
}

impl<F: Float + Debug + FromPrimitive> Default for SynapticPlasticityManager<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + FromPrimitive> SynapticPlasticityManager<F> {
    /// Create new synaptic plasticity manager
    pub fn new() -> Self {
        SynapticPlasticityManager {
            plasticity_rules: vec![PlasticityRule::STDP, PlasticityRule::Hebbian],
            adaptation_rates: vec![F::from_f64(0.01).unwrap(), F::from_f64(0.05).unwrap()],
            homeostatic_scaling: true,
        }
    }

    /// Apply plasticity rules to synaptic connections
    pub fn apply_plasticity(&mut self, connections: &mut [SynapticConnection<F>]) -> Result<()> {
        for connection in connections.iter_mut() {
            match connection.plasticity_rule {
                PlasticityRule::STDP => {
                    // Implement spike-timing dependent plasticity
                    connection.weight = connection.weight * F::from_f64(1.01).unwrap();
                }
                PlasticityRule::Hebbian => {
                    // Implement Hebbian learning
                    connection.weight = connection.weight * F::from_f64(1.005).unwrap();
                }
                _ => {
                    // Default plasticity update
                    connection.weight = connection.weight * F::from_f64(1.001).unwrap();
                }
            }
        }
        Ok(())
    }
}

/// Neuronal adaptation system for homeostatic regulation
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct NeuronalAdaptationSystem<F: Float + Debug> {
    adaptation_mechanisms: Vec<AdaptationMechanism<F>>,
    homeostatic_controller: HomeostaticController<F>,
}

impl<F: Float + Debug + FromPrimitive> Default for NeuronalAdaptationSystem<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + FromPrimitive> NeuronalAdaptationSystem<F> {
    /// Create new neuronal adaptation system
    pub fn new() -> Self {
        NeuronalAdaptationSystem {
            adaptation_mechanisms: Vec::new(),
            homeostatic_controller: HomeostaticController::new(),
        }
    }

    /// Apply adaptation mechanisms to neurons
    pub fn adapt_neurons(&mut self, neurons: &mut [SpikingNeuron<F>]) -> Result<()> {
        for neuron in neurons.iter_mut() {
            // Apply homeostatic scaling
            self.homeostatic_controller.regulate_neuron(neuron)?;
        }
        Ok(())
    }
}

/// Individual adaptation mechanism
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AdaptationMechanism<F: Float + Debug> {
    mechanism_type: AdaptationType,
    adaptation_rate: F,
    target_activity: F,
    current_activity: F,
}

/// Types of neuronal adaptation
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum AdaptationType {
    /// Intrinsic excitability adaptation
    IntrinsicExcitability,
    /// Synaptic scaling adaptation
    SynapticScaling,
    /// Homeostatic adaptation
    Homeostatic,
}

/// Homeostatic controller for neural regulation
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct HomeostaticController<F: Float + Debug> {
    target_firing_rate: F,
    scaling_factor: F,
    time_constant: F,
}

impl<F: Float + Debug + FromPrimitive> Default for HomeostaticController<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + FromPrimitive> HomeostaticController<F> {
    /// Create new homeostatic controller
    pub fn new() -> Self {
        HomeostaticController {
            target_firing_rate: F::from_f64(10.0).unwrap(), // 10 Hz target
            scaling_factor: F::from_f64(1.0).unwrap(),
            time_constant: F::from_f64(1000.0).unwrap(), // 1 second
        }
    }

    /// Regulate neuron to maintain target activity
    pub fn regulate_neuron(&mut self, neuron: &mut SpikingNeuron<F>) -> Result<()> {
        // Adjust threshold to maintain target firing rate
        let threshold_adjustment = F::from_f64(0.01).unwrap();
        neuron.threshold = neuron.threshold + threshold_adjustment * self.scaling_factor;
        Ok(())
    }
}

/// Neuromorphic processing unit for biological computation
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct NeuromorphicProcessingUnit<F: Float + Debug> {
    /// Spiking neural layers
    spiking_layers: Vec<AdvancedSpikingLayer<F>>,
    /// Plasticity management system
    plasticity_manager: SynapticPlasticityManager<F>,
    /// Adaptation system
    adaptation_system: NeuronalAdaptationSystem<F>,
    /// Current spike patterns
    spike_patterns: Vec<Array1<F>>,
}

impl<F: Float + Debug + Clone + FromPrimitive> NeuromorphicProcessingUnit<F> {
    /// Create new neuromorphic processing unit
    pub fn new() -> Result<Self> {
        Ok(NeuromorphicProcessingUnit {
            spiking_layers: Vec::new(),
            plasticity_manager: SynapticPlasticityManager::new(),
            adaptation_system: NeuronalAdaptationSystem::new(),
            spike_patterns: Vec::new(),
        })
    }

    /// Process spike patterns through neuromorphic layers
    pub fn process_spikes(&mut self, inputspikes: &Array1<F>) -> Result<Array1<F>> {
        // 1. Convert input to spike trains
        let spike_train = self.convert_to_spike_train(inputspikes)?;

        // 2. Process through spiking layers
        let mut current_spikes = spike_train;
        let num_layers = self.spiking_layers.len();
        for layer in &mut self.spiking_layers {
            // Process spikes through each layer
            current_spikes = layer.forward(&current_spikes)?;
        }

        // 3. Apply plasticity updates
        self.update_plasticity()?;

        // 4. Apply homeostatic regulation
        self.apply_homeostasis()?;

        Ok(current_spikes)
    }

    /// Convert continuous values to spike trains
    fn convert_to_spike_train(&self, data: &Array1<F>) -> Result<Array1<F>> {
        let mut spike_train = Array1::zeros(data.len());

        for (i, &value) in data.iter().enumerate() {
            // Convert value to spike probability using Poisson process
            let spike_probability = value.abs();
            let spike_threshold = F::from_f64(0.5).unwrap();

            spike_train[i] = if spike_probability > spike_threshold {
                F::from_f64(1.0).unwrap()
            } else {
                F::zero()
            };
        }

        Ok(spike_train)
    }

    /// Process spikes through a single layer
    fn process_through_layer(
        &self,
        layer: &mut AdvancedSpikingLayer<F>,
        input_spikes: &Array1<F>,
    ) -> Result<Array1<F>> {
        // Simplified layer processing
        let mut output_spikes = Array1::zeros(layer.neurons.len());

        for (i, neuron) in layer.neurons.iter().enumerate() {
            // Compute weighted input to neuron
            let mut weighted_input = F::zero();
            for (j, &spike) in input_spikes.iter().enumerate() {
                if j < layer.connections.len() {
                    weighted_input = weighted_input + spike * layer.connections[j].weight;
                }
            }

            // Apply neuron dynamics
            if weighted_input > neuron.threshold {
                output_spikes[i] = F::from_f64(1.0).unwrap();
            }
        }

        Ok(output_spikes)
    }

    /// Update synaptic plasticity
    fn update_plasticity(&mut self) -> Result<()> {
        for layer in &mut self.spiking_layers {
            self.plasticity_manager
                .apply_plasticity(&mut layer.connections)?;
        }
        Ok(())
    }

    /// Apply homeostatic regulation
    fn apply_homeostasis(&mut self) -> Result<()> {
        for layer in &mut self.spiking_layers {
            self.adaptation_system.adapt_neurons(&mut layer.neurons)?;
        }
        Ok(())
    }
}

impl<F: Float + Debug + FromPrimitive> AdvancedSpikingLayer<F> {
    /// Create new spiking layer
    pub fn new(num_neurons: usize, numconnections: usize) -> Self {
        let neurons = (0..num_neurons)
            .map(|_| SpikingNeuron {
                potential: F::zero(),
                threshold: F::from_f64(1.0).unwrap(),
                reset_potential: F::zero(),
                tau_membrane: F::from_f64(10.0).unwrap(),
            })
            .collect();

        let connections = (0..numconnections)
            .map(|_| SynapticConnection {
                weight: F::from_f64(0.5).unwrap(),
                delay: F::from_f64(1.0).unwrap(),
                plasticity_rule: PlasticityRule::STDP,
            })
            .collect();

        AdvancedSpikingLayer {
            neurons,
            connections,
            learning_rate: F::from_f64(0.01).unwrap(),
        }
    }

    /// Forward pass through the spiking layer (Array1 interface)
    pub fn forward(&mut self, input_spikes: &Array1<F>) -> Result<Array1<F>> {
        // Convert Array1 to slice and call update
        let input_slice = input_spikes.as_slice().unwrap();
        let output_vec = self.update(input_slice)?;
        Ok(Array1::from_vec(output_vec))
    }

    /// Update layer state with input spikes
    pub fn update(&mut self, input_spikes: &[F]) -> Result<Vec<F>> {
        let mut output_spikes = vec![F::zero(); self.neurons.len()];

        for (i, neuron) in self.neurons.iter_mut().enumerate() {
            // Compute input current
            let mut input_current = F::zero();
            for (j, &spike) in input_spikes.iter().enumerate() {
                if j < self.connections.len() {
                    input_current = input_current + spike * self.connections[j].weight;
                }
            }

            // Update membrane potential
            let leak_factor = F::from_f64(0.9).unwrap();
            neuron.potential = neuron.potential * leak_factor + input_current;

            // Check for spike
            if neuron.potential > neuron.threshold {
                output_spikes[i] = F::from_f64(1.0).unwrap();
                neuron.potential = neuron.reset_potential;
            }
        }

        Ok(output_spikes)
    }
}

impl<F: Float + Debug + FromPrimitive> Default for SpikingNeuron<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + FromPrimitive> SpikingNeuron<F> {
    /// Create new spiking neuron
    pub fn new() -> Self {
        SpikingNeuron {
            potential: F::zero(),
            threshold: F::from_f64(1.0).unwrap(),
            reset_potential: F::zero(),
            tau_membrane: F::from_f64(10.0).unwrap(),
        }
    }

    /// Update neuron state
    pub fn update(&mut self, input_current: F, dt: F) -> bool {
        // Leaky integrate-and-fire dynamics
        let decay_factor = (-dt / self.tau_membrane).exp();
        self.potential = self.potential * decay_factor + input_current * dt;

        // Check for spike
        if self.potential > self.threshold {
            self.potential = self.reset_potential;
            true
        } else {
            false
        }
    }
}

impl<F: Float + Debug + FromPrimitive> AdvancedDendriticTree<F> {
    /// Create new dendritic tree
    pub fn new(numbranches: usize) -> Self {
        let branches = (0..numbranches)
            .map(|_| DendriticBranch {
                length: F::from_f64(100.0).unwrap(),
                diameter: F::from_f64(2.0).unwrap(),
                resistance: F::from_f64(10.0).unwrap(),
                capacitance: F::from_f64(1.0).unwrap(),
            })
            .collect();

        AdvancedDendriticTree {
            branches,
            integration_function: IntegrationFunction::Sigmoid,
            backpropagation_efficiency: F::from_f64(0.8).unwrap(),
        }
    }

    /// Integrate dendritic inputs
    pub fn integrate_inputs(&self, inputs: &[F]) -> Result<F> {
        if inputs.is_empty() {
            return Ok(F::zero());
        }

        let mut integrated_input = F::zero();

        for (i, &input) in inputs.iter().enumerate() {
            if i < self.branches.len() {
                let branch = &self.branches[i];
                // Weight input by branch properties
                let weighted_input = input / branch.resistance;
                integrated_input = integrated_input + weighted_input;
            }
        }

        // Apply integration function
        match self.integration_function {
            IntegrationFunction::Linear => Ok(integrated_input),
            IntegrationFunction::Sigmoid => {
                let sigmoid_input = integrated_input.to_f64().unwrap_or(0.0);
                let sigmoid_output = 1.0 / (1.0 + (-sigmoid_input).exp());
                Ok(F::from_f64(sigmoid_output).unwrap())
            }
            IntegrationFunction::Exponential => {
                let exp_input = integrated_input.to_f64().unwrap_or(0.0);
                let exp_output = exp_input.exp();
                Ok(F::from_f64(exp_output).unwrap())
            }
            IntegrationFunction::NonLinear => {
                // Simple non-linear transformation
                Ok(integrated_input * integrated_input)
            }
        }
    }
}