trustformers-core 0.1.1

//! Spiking Neural Network implementation

#![allow(unused_variables)] // Neuromorphic implementation with reserved parameters

use crate::neuromorphic::SpikeEvent;
use anyhow::Result;
use scirs2_core::random::*; // SciRS2 Policy compliant

/// Spiking neural network
#[derive(Debug, Clone)]
pub struct SpikingNeuralNetwork {
    pub neurons: Vec<SpikingNeuron>,
    pub synapses: Vec<Synapse>,
    pub topology: NetworkTopology,
    pub learning_rules: Vec<PlasticityRule>,
    pub power_gated: bool,
}

/// Individual spiking neuron
#[derive(Debug, Clone)]
pub struct SpikingNeuron {
    pub id: usize,
    pub neuron_type: NeuronType,
    pub membrane_potential: f32,
    pub threshold: f32,
    pub leak_rate: f32,
    pub refractory_period: f32,
    pub last_spike_time: f64,
    pub input_current: f32,
}

/// Types of neurons
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum NeuronType {
    LeakyIntegrateAndFire,
    AdaptiveExponential,
    Izhikevich,
    HodgkinHuxley,
}

/// Synaptic connection between neurons
#[derive(Debug, Clone)]
pub struct Synapse {
    pub pre_neuron: usize,
    pub post_neuron: usize,
    pub weight: f32,
    pub delay: f32,
    pub synapse_type: SynapseType,
    pub plasticity_enabled: bool,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum SynapseType {
    Excitatory,
    Inhibitory,
    Modulatory,
}

/// Network topology patterns
#[derive(Debug, Clone)]
pub enum NetworkTopology {
    FullyConnected,
    Layered { layers: Vec<usize> },
    SmallWorld { rewiring_prob: f32 },
    ScaleFree { gamma: f32 },
    Custom { adjacency_matrix: Vec<Vec<bool>> },
}

/// Plasticity learning rules
#[derive(Debug, Clone)]
pub enum PlasticityRule {
    STDP {
        tau_plus: f32,
        tau_minus: f32,
        a_plus: f32,
        a_minus: f32,
    },
    BCM {
        theta: f32,
        tau: f32,
    },
    Homeostatic {
        target_rate: f32,
        alpha: f32,
    },
}

impl SpikingNeuralNetwork {
    /// Create a new spiking neural network
    pub fn new(num_neurons: usize) -> Self {
        let neurons = (0..num_neurons)
            .map(|id| SpikingNeuron::new(id, NeuronType::LeakyIntegrateAndFire))
            .collect();

        Self {
            neurons,
            synapses: Vec::new(),
            topology: NetworkTopology::FullyConnected,
            learning_rules: Vec::new(),
            power_gated: false,
        }
    }

    /// Add a synapse between neurons
    pub fn add_synapse(&mut self, pre: usize, post: usize, weight: f32, delay: f32) -> Result<()> {
        if pre >= self.neurons.len() || post >= self.neurons.len() {
            return Err(anyhow::anyhow!("Invalid neuron indices"));
        }

        let synapse = Synapse {
            pre_neuron: pre,
            post_neuron: post,
            weight,
            delay,
            synapse_type: if weight >= 0.0 {
                SynapseType::Excitatory
            } else {
                SynapseType::Inhibitory
            },
            plasticity_enabled: true,
        };

        self.synapses.push(synapse);
        Ok(())
    }

    /// Process one time step
    pub fn process_time_step(
        &mut self,
        input_spikes: &[SpikeEvent],
        dt: f64,
    ) -> Result<Vec<SpikeEvent>> {
        let mut output_spikes = Vec::new();

        // Apply input currents from spikes
        for spike in input_spikes {
            if spike.neuron_id < self.neurons.len() {
                self.neurons[spike.neuron_id].input_current += spike.weight;
            }
        }

        // Update each neuron and collect spikes
        let mut spiked_neurons = Vec::new();
        for neuron in &mut self.neurons {
            if neuron.update(dt) {
                // Neuron spiked
                output_spikes.push(SpikeEvent::new(neuron.id, 0.0, 1.0));
                spiked_neurons.push(neuron.id);
            }
        }

        // Propagate spikes through synapses
        for neuron_id in spiked_neurons {
            for synapse in &self.synapses {
                if synapse.pre_neuron == neuron_id {
                    if let Some(post_neuron) = self.neurons.get_mut(synapse.post_neuron) {
                        post_neuron.receive_spike(synapse.weight, synapse.delay);
                    }
                }
            }
        }

        // Apply plasticity rules
        self.apply_plasticity(&output_spikes, dt);

        // Reset input currents
        for neuron in &mut self.neurons {
            neuron.input_current = 0.0;
        }

        Ok(output_spikes)
    }

    /// Apply plasticity learning rules
    fn apply_plasticity(&mut self, spikes: &[SpikeEvent], dt: f64) {
        if self.learning_rules.is_empty() {
            return;
        }

        // Collect rules to avoid borrowing conflicts
        let rules = self.learning_rules.clone();
        for rule in &rules {
            match rule {
                PlasticityRule::STDP {
                    tau_plus,
                    tau_minus,
                    a_plus,
                    a_minus,
                } => {
                    self.apply_stdp(*tau_plus, *tau_minus, *a_plus, *a_minus, spikes, dt);
                },
                PlasticityRule::BCM { theta, tau } => {
                    self.apply_bcm(*theta, *tau, spikes, dt);
                },
                PlasticityRule::Homeostatic { target_rate, alpha } => {
                    self.apply_homeostatic(*target_rate, *alpha, spikes, dt);
                },
            }
        }
    }

    fn apply_stdp(
        &mut self,
        tau_plus: f32,
        tau_minus: f32,
        a_plus: f32,
        a_minus: f32,
        spikes: &[SpikeEvent],
        dt: f64,
    ) {
        // Simplified STDP implementation
        for spike in spikes {
            let pre_neuron_id = spike.neuron_id;

            for synapse in &mut self.synapses {
                if synapse.plasticity_enabled && synapse.pre_neuron == pre_neuron_id {
                    let post_neuron = &self.neurons[synapse.post_neuron];
                    let delta_t = spike.timestamp - post_neuron.last_spike_time;

                    if delta_t > 0.0 {
                        // Pre before post - potentiation
                        synapse.weight += a_plus * (-delta_t as f32 / tau_plus).exp();
                    } else {
                        // Post before pre - depression
                        synapse.weight -= a_minus * (delta_t as f32 / tau_minus).exp();
                    }

                    // Clip weights
                    synapse.weight = synapse.weight.clamp(-1.0, 1.0);
                }
            }
        }
    }

    fn apply_bcm(&mut self, theta: f32, tau: f32, spikes: &[SpikeEvent], dt: f64) {
        // BCM rule implementation (placeholder)
        for spike in spikes {
            // BCM plasticity based on postsynaptic activity
            let neuron = &self.neurons[spike.neuron_id];
            let activity = neuron.membrane_potential;

            for synapse in &mut self.synapses {
                if synapse.plasticity_enabled && synapse.post_neuron == spike.neuron_id {
                    let delta_w = activity * (activity - theta) * synapse.weight * dt as f32 / tau;
                    synapse.weight += delta_w;
                    synapse.weight = synapse.weight.clamp(-1.0, 1.0);
                }
            }
        }
    }

    fn apply_homeostatic(&mut self, target_rate: f32, alpha: f32, spikes: &[SpikeEvent], dt: f64) {
        // Homeostatic plasticity to maintain target firing rate
        let current_rate = spikes.len() as f32 / (self.neurons.len() as f32 * dt as f32);
        let rate_error = target_rate - current_rate;

        for synapse in &mut self.synapses {
            if synapse.plasticity_enabled {
                synapse.weight += alpha * rate_error * dt as f32;
                synapse.weight = synapse.weight.clamp(-1.0, 1.0);
            }
        }
    }

    /// Set network topology
    pub fn set_topology(&mut self, topology: NetworkTopology) {
        // Clone the topology data we need before assigning
        match &topology {
            NetworkTopology::Layered { layers } => {
                let layers_clone = layers.clone();
                self.topology = topology;
                self.create_layered_connections(&layers_clone);
            },
            NetworkTopology::SmallWorld { rewiring_prob } => {
                let rewiring_prob_val = *rewiring_prob;
                self.topology = topology;
                self.create_small_world_connections(rewiring_prob_val);
            },
            _ => {
                self.topology = topology;
            },
        }
    }

    fn create_layered_connections(&mut self, layers: &[usize]) {
        self.synapses.clear();
        let mut neuron_idx = 0;
        let mut rng = thread_rng();

        for i in 0..layers.len() - 1 {
            let current_layer_size = layers[i];
            let next_layer_size = layers[i + 1];

            for current in 0..current_layer_size {
                for next in 0..next_layer_size {
                    let pre = neuron_idx + current;
                    let post = neuron_idx + current_layer_size + next;
                    let weight = (rng.random::<f32>() - 0.5) * 2.0; // Random weight [-1, 1]
                    let _ = self.add_synapse(pre, post, weight, 1.0);
                }
            }
            neuron_idx += current_layer_size;
        }
    }

    fn create_small_world_connections(&mut self, rewiring_prob: f32) {
        // Simplified small-world network creation
        self.synapses.clear();
        let n = self.neurons.len();
        let mut rng = thread_rng();

        // Create ring lattice
        for i in 0..n {
            let next = (i + 1) % n;
            let weight = (rng.random::<f32>() - 0.5) * 2.0;
            let _ = self.add_synapse(i, next, weight, 1.0);
        }

        // Rewire some connections
        let mut synapses_to_rewire = Vec::new();
        for (idx, synapse) in self.synapses.iter().enumerate() {
            if rng.random::<f32>() < rewiring_prob {
                synapses_to_rewire.push(idx);
            }
        }

        for idx in synapses_to_rewire {
            let new_target = rng.random_range(0..n);
            self.synapses[idx].post_neuron = new_target;
        }
    }

    /// Add plasticity rule
    pub fn add_plasticity_rule(&mut self, rule: PlasticityRule) {
        self.learning_rules.push(rule);
    }

    /// Enable power gating
    pub fn enable_power_gating(&mut self) {
        self.power_gated = true;
    }

    /// Adjust firing thresholds
    pub fn adjust_firing_thresholds(&mut self, factor: f32) {
        for neuron in &mut self.neurons {
            neuron.threshold *= factor;
        }
    }

    /// Get network statistics
    pub fn get_statistics(&self) -> NetworkStatistics {
        let total_synapses = self.synapses.len();
        let excitatory_synapses = self
            .synapses
            .iter()
            .filter(|s| matches!(s.synapse_type, SynapseType::Excitatory))
            .count();
        let inhibitory_synapses = total_synapses - excitatory_synapses;

        let average_weight = if total_synapses > 0 {
            self.synapses.iter().map(|s| s.weight).sum::<f32>() / total_synapses as f32
        } else {
            0.0
        };

        NetworkStatistics {
            num_neurons: self.neurons.len(),
            num_synapses: total_synapses,
            excitatory_synapses,
            inhibitory_synapses,
            average_weight,
            plasticity_enabled: !self.learning_rules.is_empty(),
        }
    }
}

impl SpikingNeuron {
    /// Create a new spiking neuron
    pub fn new(id: usize, neuron_type: NeuronType) -> Self {
        Self {
            id,
            neuron_type,
            membrane_potential: 0.0,
            threshold: 1.0,
            leak_rate: 0.1,
            refractory_period: 2.0,
            last_spike_time: -100.0,
            input_current: 0.0,
        }
    }

    /// Update neuron state and return true if spiked
    pub fn update(&mut self, dt: f64) -> bool {
        match self.neuron_type {
            NeuronType::LeakyIntegrateAndFire => self.update_lif(dt),
            NeuronType::AdaptiveExponential => self.update_aeif(dt),
            NeuronType::Izhikevich => self.update_izhikevich(dt),
            NeuronType::HodgkinHuxley => self.update_hh(dt),
        }
    }

    fn update_lif(&mut self, dt: f64) -> bool {
        // Leaky Integrate-and-Fire model
        let dt_f32 = dt as f32;

        // Update membrane potential
        self.membrane_potential +=
            dt_f32 * (-self.leak_rate * self.membrane_potential + self.input_current);

        // Check for spike
        if self.membrane_potential >= self.threshold {
            self.membrane_potential = 0.0; // Reset
            self.last_spike_time = 0.0; // Current time (simplified)
            return true;
        }

        false
    }

    fn update_aeif(&mut self, dt: f64) -> bool {
        // Adaptive Exponential Integrate-and-Fire (simplified)
        self.update_lif(dt) // Placeholder - use LIF for now
    }

    fn update_izhikevich(&mut self, dt: f64) -> bool {
        // Izhikevich model (simplified)
        self.update_lif(dt) // Placeholder - use LIF for now
    }

    fn update_hh(&mut self, dt: f64) -> bool {
        // Hodgkin-Huxley model (simplified)
        self.update_lif(dt) // Placeholder - use LIF for now
    }

    /// Receive spike from presynaptic neuron
    pub fn receive_spike(&mut self, weight: f32, delay: f32) {
        // Simplified - add to input current immediately (ignore delay for now)
        self.input_current += weight;
    }
}

/// Network statistics
#[derive(Debug, Clone)]
pub struct NetworkStatistics {
    pub num_neurons: usize,
    pub num_synapses: usize,
    pub excitatory_synapses: usize,
    pub inhibitory_synapses: usize,
    pub average_weight: f32,
    pub plasticity_enabled: bool,
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_spiking_neuron_creation() {
        let neuron = SpikingNeuron::new(0, NeuronType::LeakyIntegrateAndFire);
        assert_eq!(neuron.id, 0);
        assert_eq!(neuron.membrane_potential, 0.0);
        assert_eq!(neuron.threshold, 1.0);
    }

    #[test]
    fn test_neuron_update() {
        let mut neuron = SpikingNeuron::new(0, NeuronType::LeakyIntegrateAndFire);
        neuron.input_current = 2.0; // Strong input

        let spiked = neuron.update(1.0);
        assert!(spiked); // Should spike with strong input
        assert_eq!(neuron.membrane_potential, 0.0); // Should reset
    }

    #[test]
    fn test_spiking_network_creation() {
        let network = SpikingNeuralNetwork::new(5);
        assert_eq!(network.neurons.len(), 5);
        assert_eq!(network.synapses.len(), 0);
    }

    #[test]
    fn test_add_synapse() {
        let mut network = SpikingNeuralNetwork::new(3);
        let result = network.add_synapse(0, 1, 0.5, 1.0);
        assert!(result.is_ok());
        assert_eq!(network.synapses.len(), 1);

        let synapse = &network.synapses[0];
        assert_eq!(synapse.pre_neuron, 0);
        assert_eq!(synapse.post_neuron, 1);
        assert_eq!(synapse.weight, 0.5);
    }

    #[test]
    fn test_invalid_synapse() {
        let mut network = SpikingNeuralNetwork::new(2);
        let result = network.add_synapse(0, 5, 0.5, 1.0); // Invalid post neuron
        assert!(result.is_err());
    }

    #[test]
    fn test_plasticity_rules() {
        let mut network = SpikingNeuralNetwork::new(3);
        let stdp_rule = PlasticityRule::STDP {
            tau_plus: 20.0,
            tau_minus: 20.0,
            a_plus: 0.01,
            a_minus: 0.012,
        };

        network.add_plasticity_rule(stdp_rule);
        assert_eq!(network.learning_rules.len(), 1);
    }

    #[test]
    fn test_network_statistics() {
        let mut network = SpikingNeuralNetwork::new(4);
        let _ = network.add_synapse(0, 1, 0.5, 1.0);
        let _ = network.add_synapse(1, 2, -0.3, 1.0);
        let _ = network.add_synapse(2, 3, 0.8, 1.0);

        let stats = network.get_statistics();
        assert_eq!(stats.num_neurons, 4);
        assert_eq!(stats.num_synapses, 3);
        assert_eq!(stats.excitatory_synapses, 2);
        assert_eq!(stats.inhibitory_synapses, 1);
    }

    #[test]
    fn test_layered_topology() {
        let mut network = SpikingNeuralNetwork::new(6);
        let topology = NetworkTopology::Layered {
            layers: vec![2, 2, 2],
        };
        network.set_topology(topology);

        // Should create connections between layers
        assert!(!network.synapses.is_empty());
    }

    #[test]
    fn test_synapse_types() {
        let excitatory = Synapse {
            pre_neuron: 0,
            post_neuron: 1,
            weight: 0.5,
            delay: 1.0,
            synapse_type: SynapseType::Excitatory,
            plasticity_enabled: true,
        };

        let inhibitory = Synapse {
            pre_neuron: 1,
            post_neuron: 2,
            weight: -0.3,
            delay: 1.0,
            synapse_type: SynapseType::Inhibitory,
            plasticity_enabled: true,
        };

        assert_eq!(excitatory.synapse_type, SynapseType::Excitatory);
        assert_eq!(inhibitory.synapse_type, SynapseType::Inhibitory);
    }

    #[test]
    fn test_power_gating() {
        let mut network = SpikingNeuralNetwork::new(3);
        assert!(!network.power_gated);

        network.enable_power_gating();
        assert!(network.power_gated);
    }

    #[test]
    fn test_threshold_adjustment() {
        let mut network = SpikingNeuralNetwork::new(3);
        let original_threshold = network.neurons[0].threshold;

        network.adjust_firing_thresholds(1.5);
        assert_eq!(network.neurons[0].threshold, original_threshold * 1.5);
    }
}