//! Neuromorphic Computing for Image Processing
//!
//! This module implements cutting-edge neuromorphic computing algorithms inspired by
//! biological neural networks and brain-like processing. These algorithms provide
//! advanced-low power, event-driven, and adaptive image processing capabilities.
//!
//! # Key Features
//!
//! - **Spiking Neural Networks (SNNs)** for temporal image processing
//! - **Event-driven Processing** for efficient sparse data handling
//! - **Synaptic Plasticity** for adaptive learning and filtering
//! - **Temporal Coding** for dynamic feature extraction
//! - **Homeostatic Adaptation** for robust processing under varying conditions
//! - **Liquid State Machines** for reservoir computing on images
//! - **STDP Learning** (Spike-Timing Dependent Plasticity) for unsupervised feature learning
//! - **Address-Event Representation** for efficient sparse image encoding

use scirs2_core::ndarray::{s, Array1, Array2, Array3, ArrayView2};
use scirs2_core::numeric::{Float, FromPrimitive};
use scirs2_core::random::Rng;
use std::collections::VecDeque;

use crate::error::{NdimageError, NdimageResult};

/// Neuromorphic processing configuration
#[derive(Debug, Clone)]
pub struct NeuromorphicConfig {
    /// Time constants for leaky integration
    pub tau_membrane: f64,
    pub tau_synaptic: f64,
    /// Threshold for spike generation
    pub spike_threshold: f64,
    /// Refractory period (time steps)
    pub refractory_period: usize,
    /// Learning rate for synaptic plasticity
    pub learning_rate: f64,
    /// Homeostatic time constant
    pub tau_homeostatic: f64,
    /// Maximum synaptic weight
    pub max_weight: f64,
    /// Temporal window for STDP learning
    pub stdp_window: usize,
    /// Event-driven processing threshold
    pub event_threshold: f64,
}

impl Default for NeuromorphicConfig {
    fn default() -> Self {
        Self {
            tau_membrane: 20.0,
            tau_synaptic: 5.0,
            spike_threshold: 1.0,
            refractory_period: 2,
            learning_rate: 0.01,
            tau_homeostatic: 1000.0,
            max_weight: 2.0,
            stdp_window: 20,
            event_threshold: 0.1,
        }
    }
}

/// Spiking neuron state
#[derive(Debug, Clone)]
pub struct SpikingNeuron {
    /// Membrane potential
    pub membrane_potential: f64,
    /// Synaptic current
    pub synaptic_current: f64,
    /// Time since last spike
    pub time_since_spike: usize,
    /// Spike history
    pub spike_times: VecDeque<usize>,
    /// Homeostatic firing rate target
    pub target_rate: f64,
    /// Current firing rate estimate
    pub current_rate: f64,
    /// Adaptation current
    pub adaptation_current: f64,
    /// Pre-synaptic spike trace for STDP
    pub pre_spike_trace: f64,
    /// Post-synaptic spike trace for STDP
    pub post_spike_trace: f64,
}

impl Default for SpikingNeuron {
    fn default() -> Self {
        Self {
            membrane_potential: 0.0,
            synaptic_current: 0.0,
            time_since_spike: 0,
            spike_times: VecDeque::new(),
            target_rate: 0.1, // 10% baseline firing rate
            current_rate: 0.0,
            adaptation_current: 0.0,
            pre_spike_trace: 0.0,
            post_spike_trace: 0.0,
        }
    }
}

/// Event representation for address-event processing
#[derive(Debug, Clone)]
pub struct Event {
    pub x: usize,
    pub y: usize,
    pub timestamp: usize,
    pub polarity: bool, // true for ON events, false for OFF events
    pub value: f64,
}

/// Synaptic connection with plasticity
#[derive(Debug, Clone)]
pub struct PlasticSynapse {
    pub weight: f64,
    pub delay: usize,
    pub pre_spike_trace: f64,
    pub post_spike_trace: f64,
    pub eligibility_trace: f64,
}

impl Default for PlasticSynapse {
    fn default() -> Self {
        Self {
            weight: 0.5,
            delay: 1,
            pre_spike_trace: 0.0,
            post_spike_trace: 0.0,
            eligibility_trace: 0.0,
        }
    }
}

/// Spiking Neural Network for Image Processing
///
/// Implements a multi-layer spiking neural network that processes images
/// through temporal spike patterns, providing biological-like processing.
#[allow(dead_code)]
pub fn spiking_neural_network_filter<T>(
    image: ArrayView2<T>,
    network_layers: &[usize],
    config: &NeuromorphicConfig,
    time_steps: usize,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();

    // Initialize spiking neural network
    let mut network = initialize_snn(network_layers, height, width)?;

    // Convert image to temporal spike patterns
    let spike_trains = image_to_spike_trains(&image, time_steps, config)?;

    // Process through network over time
    let mut output_spikes = Array3::zeros((time_steps, height, width));

    for t in 0..time_steps {
        // Extract input spikes for current time step
        let input_spikes = spike_trains.slice(s![t, .., ..]);

        // Forward propagation through network
        let layer_output = forward_propagate_snn(&mut network, &input_spikes, config, t)?;

        // Store output spikes
        output_spikes.slice_mut(s![t, .., ..]).assign(&layer_output);

        // Apply synaptic plasticity
        apply_stdp_learning(&mut network, config, t)?;
    }

    // Convert spike trains back to image
    let result = spike_trains_toimage(output_spikes.view(), config)?;

    Ok(result)
}

/// Event-Driven Image Processing
///
/// Processes images using event-driven neuromorphic algorithms that only
/// activate when significant changes occur, mimicking retinal processing.
#[allow(dead_code)]
pub fn event_driven_processing<T>(
    current_frame: ArrayView2<T>,
    previous_frame: Option<ArrayView2<T>>,
    config: &NeuromorphicConfig,
) -> NdimageResult<(Array2<T>, Vec<Event>)>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = current_frame.dim();

    // Generate events based on temporal differences
    let events = if let Some(prev) = previous_frame {
        generate_events(&current_frame, &prev, config)?
    } else {
        generate_initial_events(&current_frame, config)?
    };

    // Process events through neuromorphic filters
    let mut processedimage = Array2::zeros((height, width));
    let mut event_accumulator = Array2::zeros((height, width));

    // Event-driven convolution using integrate-and-fire neurons
    for event in &events {
        if event.x < width && event.y < height {
            // Apply spatial-temporal filtering kernel
            apply_event_kernel(&mut event_accumulator, event, config)?;
        }
    }

    // Convert accumulated events to image representation
    for y in 0..height {
        for x in 0..width {
            let accumulated = event_accumulator[(y, x)];
            let normalized = T::from_f64(accumulated.tanh()).ok_or_else(|| {
                NdimageError::ComputationError("Type conversion failed".to_string())
            })?;
            processedimage[(y, x)] = normalized;
        }
    }

    Ok((processedimage, events))
}

/// Liquid State Machine for Temporal Image Processing
///
/// Implements a liquid state machine (reservoir computing) for processing
/// temporal sequences of images with rich dynamics and memory.
#[allow(dead_code)]
pub fn liquidstate_machine<T>(
    image_sequence: &[ArrayView2<T>],
    reservoir_size: usize,
    config: &NeuromorphicConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    if image_sequence.is_empty() {
        return Err(NdimageError::InvalidInput(
            "Empty image _sequence".to_string(),
        ));
    }

    let (height, width) = image_sequence[0].dim();

    // Initialize liquid (reservoir) neurons
    let mut reservoir = initialize_reservoir(reservoir_size, height, width, config)?;

    // Process _sequence through liquid state machine
    let mut liquidstates = Vec::new();

    for (t, image) in image_sequence.iter().enumerate() {
        // Convert image to input currents
        let input_currents = image_to_currents(image)?;

        // Update reservoir dynamics
        update_reservoir_dynamics(&mut reservoir, &input_currents, config)?;

        // Capture liquid state
        let state = capture_reservoirstate(&reservoir)?;
        liquidstates.push(state);
    }

    // Read out final processed image from liquid states
    let processedimage_f64 = readout_from_liquidstates(&liquidstates, (height, width), config)?;

    // Convert from f64 to generic type T
    let processedimage = processedimage_f64.mapv(|v| T::from_f64(v).unwrap_or(T::zero()));

    Ok(processedimage)
}

/// Adaptive Homeostatic Filtering
///
/// Implements homeostatic plasticity mechanisms that maintain optimal
/// neural activity levels for robust image processing under varying conditions.
#[allow(dead_code)]
pub fn homeostatic_adaptive_filter<T>(
    image: ArrayView2<T>,
    config: &NeuromorphicConfig,
    adaptation_steps: usize,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();

    // Initialize adaptive filter network
    let mut neurons = Array2::from_elem((height, width), SpikingNeuron::default());
    let mut synaptic_weights = Array3::from_elem((height, width, 9), 0.5); // 3x3 neighborhood

    let mut processedimage = Array2::zeros((height, width));

    // Adaptive processing with homeostatic regulation
    for step in 0..adaptation_steps {
        // Apply current filter state
        for y in 1..height - 1 {
            for x in 1..width - 1 {
                let mut weighted_sum = 0.0;
                let mut weight_index = 0;

                // Process 3x3 neighborhood
                for dy in -1i32..=1 {
                    for dx in -1i32..=1 {
                        let ny = (y as i32 + dy) as usize;
                        let nx = (x as i32 + dx) as usize;
                        let pixel_val = image[(ny, nx)].to_f64().unwrap_or(0.0);
                        let weight = synaptic_weights[(y, x, weight_index)];

                        weighted_sum += pixel_val * weight;
                        weight_index += 1;
                    }
                }

                // Update neuron with leaky integration
                let neuron = &mut neurons[(y, x)];
                neuron.synaptic_current = weighted_sum;

                // Leaky integration
                let decay = (-1.0 / config.tau_membrane).exp();
                neuron.membrane_potential =
                    neuron.membrane_potential * decay + neuron.synaptic_current;

                // Spike generation
                let output_value = if neuron.membrane_potential > config.spike_threshold {
                    neuron.membrane_potential = 0.0; // Reset
                    neuron.time_since_spike = 0;
                    neuron.spike_times.push_back(step);
                    1.0
                } else {
                    neuron.time_since_spike += 1;
                    0.0
                };

                processedimage[(y, x)] = T::from_f64(output_value).ok_or_else(|| {
                    NdimageError::ComputationError("Type conversion failed".to_string())
                })?;

                // Homeostatic adaptation
                update_homeostatic_weights(&mut synaptic_weights, (y, x), neuron, config, step)?;
            }
        }
    }

    Ok(processedimage)
}

/// Temporal Coding Feature Extraction
///
/// Extracts features using temporal coding principles where information
/// is encoded in the precise timing of neural spikes.
#[allow(dead_code)]
pub fn temporal_coding_feature_extraction<T>(
    image: ArrayView2<T>,
    feature_detectors: &[Array2<f64>],
    config: &NeuromorphicConfig,
    time_window: usize,
) -> NdimageResult<Array3<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();
    let num_features = feature_detectors.len();

    // Initialize feature maps
    let mut feature_maps = Array3::zeros((num_features, height, width));

    // Convert image to temporal patterns
    let temporalimage = create_temporal_patterns(&image, time_window, config)?;

    for (feature_idx, detector) in feature_detectors.iter().enumerate() {
        let (det_h, det_w) = detector.dim();

        // Sliding _window feature detection with temporal coding
        for y in 0..height.saturating_sub(det_h) {
            for x in 0..width.saturating_sub(det_w) {
                let mut temporal_correlation = 0.0;

                // Temporal correlation computation
                for t in 0..time_window {
                    let mut spatial_correlation = 0.0;

                    for dy in 0..det_h {
                        for dx in 0..det_w {
                            let img_val = temporalimage[(t, y + dy, x + dx)];
                            let det_val = detector[(dy, dx)];
                            spatial_correlation += img_val * det_val;
                        }
                    }

                    // Temporal weighting (earlier spikes have higher precedence)
                    let temporal_weight = (-(t as f64) / config.tau_synaptic).exp();
                    temporal_correlation += spatial_correlation * temporal_weight;
                }

                let feature_strength =
                    T::from_f64(temporal_correlation.tanh()).ok_or_else(|| {
                        NdimageError::ComputationError("Type conversion failed".to_string())
                    })?;

                feature_maps[(feature_idx, y, x)] = feature_strength;
            }
        }
    }

    Ok(feature_maps)
}

/// STDP-based Unsupervised Learning Filter
///
/// Implements Spike-Timing Dependent Plasticity for unsupervised learning
/// of image features through biological-like synaptic adaptation.
#[allow(dead_code)]
pub fn stdp_unsupervised_learning<T>(
    trainingimages: &[ArrayView2<T>],
    filter_size: (usize, usize),
    config: &NeuromorphicConfig,
    epochs: usize,
) -> NdimageResult<Array2<f64>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (filter_h, filter_w) = filter_size;

    // Initialize synaptic weights randomly
    let mut rng = scirs2_core::random::rng();
    let mut learned_filter = Array2::from_shape_fn(filter_size, |_| (rng.random_range(-0.1..0.1)));

    let pre_synaptic_traces = Array2::<f64>::zeros(filter_size);
    let post_synaptic_trace = 0.0;

    // STDP learning over multiple epochs
    for epoch in 0..epochs {
        for (img_idx, image) in trainingimages.iter().enumerate() {
            let (height, width) = image.dim();

            // Random location for unsupervised patch learning
            let y_start = rng.random_range(0..height.saturating_sub(filter_h));
            let x_start = rng.random_range(0..width.saturating_sub(filter_w));

            // Extract patch
            let patch = image.slice(s![y_start..y_start + filter_h, x_start..x_start + filter_w]);

            // Convert patch to spike times
            let mut pre_spike_times = Array2::zeros(filter_size);
            let mut post_spike_time = 0;

            for y in 0..filter_h {
                for x in 0..filter_w {
                    let intensity = patch[(y, x)].to_f64().unwrap_or(0.0);
                    // Convert intensity to spike timing (higher intensity = earlier spike)
                    let spike_time =
                        (config.stdp_window as f64 * (1.0 - intensity)).max(1.0) as usize;
                    pre_spike_times[(y, x)] = spike_time as f64;
                }
            }

            // Compute post-synaptic response
            let response: f64 = patch
                .iter()
                .zip(learned_filter.iter())
                .map(|(&p, &w)| p.to_f64().unwrap_or(0.0) * w)
                .sum();

            if response > config.spike_threshold {
                post_spike_time = config.stdp_window / 2; // Spike in middle of window
            }

            // Apply STDP learning rule
            for y in 0..filter_h {
                for x in 0..filter_w {
                    let pre_time = pre_spike_times[(y, x)] as usize;
                    let dt = post_spike_time as i32 - pre_time as i32;

                    let stdp_update = if dt > 0 {
                        // Post-before-pre: potentiation
                        config.learning_rate * (-dt.abs() as f64 / config.stdp_window as f64).exp()
                    } else if dt < 0 {
                        // Pre-before-post: depression
                        -config.learning_rate * (-dt.abs() as f64 / config.stdp_window as f64).exp()
                    } else {
                        0.0
                    };

                    learned_filter[(y, x)] += stdp_update;

                    // Weight bounds
                    learned_filter[(y, x)] =
                        learned_filter[(y, x)].clamp(-config.max_weight, config.max_weight);
                }
            }

            // Homeostatic weight normalization
            let weight_sum: f64 = learned_filter.iter().map(|&w| w.abs()).sum();
            if weight_sum > 0.0 {
                learned_filter
                    .mapv_inplace(|w| w / weight_sum * filter_size.0 as f64 * filter_size.1 as f64);
            }
        }

        // Decay learning rate
        // config.learning_rate *= 0.99; // Can't modify config, handled externally
    }

    Ok(learned_filter)
}

// Helper functions

#[allow(dead_code)]
fn initialize_snn(
    layers: &[usize],
    height: usize,
    width: usize,
) -> NdimageResult<Vec<Array2<SpikingNeuron>>> {
    let mut network = Vec::new();

    for &_layer_size in layers {
        let neurons = Array2::from_elem((height, width), SpikingNeuron::default());
        network.push(neurons);
    }

    Ok(network)
}

#[allow(dead_code)]
fn image_to_spike_trains<T>(
    image: &ArrayView2<T>,
    time_steps: usize,
    config: &NeuromorphicConfig,
) -> NdimageResult<Array3<f64>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = image.dim();
    let mut spike_trains = Array3::zeros((time_steps, height, width));
    let mut rng = scirs2_core::random::rng();

    // Convert pixel intensities to Poisson spike trains
    for y in 0..height {
        for x in 0..width {
            let intensity = image[(y, x)].to_f64().unwrap_or(0.0);
            let spike_rate = intensity.max(0.0).min(1.0); // Normalized rate

            for t in 0..time_steps {
                if rng.random_range(0.0..1.0) < spike_rate * config.learning_rate {
                    spike_trains[(t, y, x)] = 1.0;
                }
            }
        }
    }

    Ok(spike_trains)
}

#[allow(dead_code)]
fn forward_propagate_snn(
    network: &mut [Array2<SpikingNeuron>],
    input_spikes: &scirs2_core::ndarray::ArrayView2<f64>,
    config: &NeuromorphicConfig,
    current_time: usize,
) -> NdimageResult<Array2<f64>> {
    let (height, width) = input_spikes.dim();
    let mut output_spikes = Array2::zeros((height, width));

    // Simple feedforward propagation (first layer only for demonstration)
    if !network.is_empty() {
        let layer = &mut network[0];

        for y in 0..height {
            for x in 0..width {
                let neuron = &mut layer[(y, x)];
                let input_current = input_spikes[(y, x)];

                // Update synaptic current
                let decay = (-1.0 / config.tau_synaptic).exp();
                neuron.synaptic_current = neuron.synaptic_current * decay + input_current;

                // Update membrane potential
                let membrane_decay = (-1.0 / config.tau_membrane).exp();
                neuron.membrane_potential =
                    neuron.membrane_potential * membrane_decay + neuron.synaptic_current;

                // Check for spike
                if neuron.membrane_potential > config.spike_threshold
                    && neuron.time_since_spike > config.refractory_period
                {
                    neuron.membrane_potential = 0.0; // Reset
                    neuron.time_since_spike = 0;
                    neuron.spike_times.push_back(current_time);

                    // Limit spike history
                    if neuron.spike_times.len() > config.stdp_window {
                        neuron.spike_times.pop_front();
                    }

                    output_spikes[(y, x)] = 1.0;
                } else {
                    neuron.time_since_spike += 1;
                    output_spikes[(y, x)] = 0.0;
                }
            }
        }
    }

    Ok(output_spikes)
}

#[allow(dead_code)]
fn apply_stdp_learning(
    network: &mut [Array2<SpikingNeuron>],
    config: &NeuromorphicConfig,
    current_time: usize,
) -> NdimageResult<()> {
    // Simplified STDP implementation for demonstration
    // In practice, this would involve complex connectivity patterns

    for layer in network {
        for neuron in layer.iter_mut() {
            // Update spike traces
            let trace_decay = (-1.0 / config.tau_synaptic).exp();
            neuron.pre_spike_trace *= trace_decay;
            neuron.post_spike_trace *= trace_decay;

            // Check if neuron spiked recently
            if let Some(&last_spike_time) = neuron.spike_times.back() {
                if current_time == last_spike_time {
                    neuron.post_spike_trace += 1.0;
                }
            }
        }
    }

    Ok(())
}

#[allow(dead_code)]
fn spike_trains_toimage<T>(
    spike_trains: scirs2_core::ndarray::ArrayView3<f64>,
    _config: &NeuromorphicConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy,
{
    let (time_steps, height, width) = spike_trains.dim();
    let mut image = Array2::zeros((height, width));

    // Convert spike _trains back to continuous values
    for y in 0..height {
        for x in 0..width {
            let mut _spike_count = 0.0;
            let mut weighted_sum = 0.0;

            for t in 0..time_steps {
                let spike = spike_trains[(t, y, x)];
                if spike > 0.0 {
                    _spike_count += 1.0;
                    // Weight by temporal position
                    let temporal_weight = 1.0 - (t as f64 / time_steps as f64);
                    weighted_sum += spike * temporal_weight;
                }
            }

            let intensity = weighted_sum / time_steps as f64;
            image[(y, x)] = T::from_f64(intensity).ok_or_else(|| {
                NdimageError::ComputationError("Type conversion failed".to_string())
            })?;
        }
    }

    Ok(image)
}

#[allow(dead_code)]
fn generate_events<T>(
    current: &ArrayView2<T>,
    previous: &ArrayView2<T>,
    config: &NeuromorphicConfig,
) -> NdimageResult<Vec<Event>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = current.dim();
    let mut events = Vec::new();

    for y in 0..height {
        for x in 0..width {
            let curr_val = current[(y, x)].to_f64().unwrap_or(0.0);
            let prev_val = previous[(y, x)].to_f64().unwrap_or(0.0);
            let diff = curr_val - prev_val;

            if diff.abs() > config.event_threshold {
                events.push(Event {
                    x,
                    y,
                    timestamp: 0, // Would be set by external timing
                    polarity: diff > 0.0,
                    value: diff.abs(),
                });
            }
        }
    }

    Ok(events)
}

#[allow(dead_code)]
fn generate_initial_events<T>(
    image: &ArrayView2<T>,
    config: &NeuromorphicConfig,
) -> NdimageResult<Vec<Event>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = image.dim();
    let mut events = Vec::new();

    for y in 0..height {
        for x in 0..width {
            let intensity = image[(y, x)].to_f64().unwrap_or(0.0);

            if intensity > config.event_threshold {
                events.push(Event {
                    x,
                    y,
                    timestamp: 0,
                    polarity: true,
                    value: intensity,
                });
            }
        }
    }

    Ok(events)
}

#[allow(dead_code)]
fn apply_event_kernel(
    accumulator: &mut Array2<f64>,
    event: &Event,
    config: &NeuromorphicConfig,
) -> NdimageResult<()> {
    let (height, width) = accumulator.dim();
    let kernel_size = 3;
    let half_kernel = kernel_size / 2;

    // Apply spatial-temporal kernel around event location
    for dy in -(half_kernel as i32)..=(half_kernel as i32) {
        for dx in -(half_kernel as i32)..=(half_kernel as i32) {
            let ny = event.y as i32 + dy;
            let nx = event.x as i32 + dx;

            if ny >= 0 && ny < height as i32 && nx >= 0 && nx < width as i32 {
                let uy = ny as usize;
                let ux = nx as usize;

                // Gaussian-like spatial kernel
                let spatial_weight =
                    (-((dy * dy + dx * dx) as f64) / (2.0 * config.tau_synaptic)).exp();

                // Temporal decay based on event properties
                let temporal_weight = (-(event.timestamp as f64) / config.tau_membrane).exp();

                let contribution = event.value * spatial_weight * temporal_weight;
                accumulator[(uy, ux)] += if event.polarity {
                    contribution
                } else {
                    -contribution
                };
            }
        }
    }

    Ok(())
}

#[allow(dead_code)]
fn initialize_reservoir(
    reservoir_size: usize,
    _height: usize,
    width: usize,
    config: &NeuromorphicConfig,
) -> NdimageResult<Array1<SpikingNeuron>> {
    let mut reservoir = Array1::from_elem(reservoir_size, SpikingNeuron::default());
    let mut rng = scirs2_core::random::rng();

    // Initialize reservoir with diverse properties
    for (i, neuron) in reservoir.iter_mut().enumerate() {
        neuron.target_rate = 0.05 + 0.1 * (i as f64 / reservoir_size as f64);
        neuron.membrane_potential = rng.random_range(-0.05..0.05);
    }

    Ok(reservoir)
}

#[allow(dead_code)]
fn image_to_currents<T>(image: &ArrayView2<T>) -> NdimageResult<Array2<f64>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = image.dim();
    let mut currents = Array2::zeros((height, width));

    for y in 0..height {
        for x in 0..width {
            currents[(y, x)] = image[(y, x)].to_f64().unwrap_or(0.0);
        }
    }

    Ok(currents)
}

#[allow(dead_code)]
fn update_reservoir_dynamics(
    reservoir: &mut Array1<SpikingNeuron>,
    input_currents: &Array2<f64>,
    config: &NeuromorphicConfig,
) -> NdimageResult<()> {
    let (height, width) = input_currents.dim();

    // Update each reservoir neuron
    for (i, neuron) in reservoir.iter_mut().enumerate() {
        // Connect to random input locations (simplified connectivity)
        let input_y = i % height;
        let input_x = (i / height) % width;
        let input_current = input_currents.get((input_y, input_x)).unwrap_or(&0.0);

        // Update neuron dynamics
        let decay = (-1.0 / config.tau_membrane).exp();
        neuron.membrane_potential = neuron.membrane_potential * decay + input_current;

        // Spike generation
        if neuron.membrane_potential > config.spike_threshold {
            neuron.membrane_potential = 0.0;
            neuron.time_since_spike = 0;
        } else {
            neuron.time_since_spike += 1;
        }
    }

    Ok(())
}

#[allow(dead_code)]
fn capture_reservoirstate(reservoir: &Array1<SpikingNeuron>) -> NdimageResult<Array1<f64>> {
    let mut state = Array1::zeros(reservoir.len());

    for (i, neuron) in reservoir.iter().enumerate() {
        state[i] = neuron.membrane_potential;
    }

    Ok(state)
}

#[allow(dead_code)]
fn readout_from_liquidstates(
    liquidstates: &[Array1<f64>],
    outputshape: (usize, usize),
    _config: &NeuromorphicConfig,
) -> NdimageResult<Array2<f64>> {
    let (height, width) = outputshape;
    let mut output = Array2::zeros((height, width));

    if liquidstates.is_empty() {
        return Ok(output);
    }

    let reservoir_size = liquidstates[0].len();

    // Simple linear readout (in practice would use trained weights)
    for y in 0..height {
        for x in 0..width {
            let mut sum = 0.0;

            for state in liquidstates {
                for i in 0..reservoir_size {
                    let weight = ((y * width + x + i) as f64
                        / (height * width * reservoir_size) as f64)
                        .sin();
                    sum += state[i] * weight;
                }
            }

            output[(y, x)] = sum.tanh(); // Nonlinear readout
        }
    }

    Ok(output)
}

#[allow(dead_code)]
fn update_homeostatic_weights(
    weights: &mut Array3<f64>,
    pos: (usize, usize),
    neuron: &SpikingNeuron,
    config: &NeuromorphicConfig,
    step: usize,
) -> NdimageResult<()> {
    let (y, x) = pos;

    // Estimate current firing rate
    let recent_spikes = neuron
        .spike_times
        .iter()
        .filter(|&&spike_time| step.saturating_sub(spike_time) < 100)
        .count() as f64;
    let current_rate = recent_spikes / 100.0;

    // Homeostatic scaling
    let rate_error = neuron.target_rate - current_rate;
    let scaling_factor = 1.0 + config.learning_rate * rate_error / config.tau_homeostatic;

    // Apply scaling to all synaptic weights
    for i in 0..9 {
        weights[(y, x, i)] *= scaling_factor;
        weights[(y, x, i)] = weights[(y, x, i)].clamp(0.0, config.max_weight);
    }

    Ok(())
}

#[allow(dead_code)]
fn create_temporal_patterns<T>(
    image: &ArrayView2<T>,
    time_window: usize,
    config: &NeuromorphicConfig,
) -> NdimageResult<Array3<f64>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = image.dim();
    let mut temporal_patterns = Array3::zeros((time_window, height, width));

    // Create temporal patterns with different time constants
    for t in 0..time_window {
        let temporal_weight = (-(t as f64) / config.tau_synaptic).exp();

        for y in 0..height {
            for x in 0..width {
                let intensity = image[(y, x)].to_f64().unwrap_or(0.0);
                temporal_patterns[(t, y, x)] = intensity * temporal_weight;
            }
        }
    }

    Ok(temporal_patterns)
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::Array2;

    #[test]
    fn test_neuromorphic_config_default() {
        let config = NeuromorphicConfig::default();

        assert_eq!(config.tau_membrane, 20.0);
        assert_eq!(config.tau_synaptic, 5.0);
        assert_eq!(config.spike_threshold, 1.0);
        assert_eq!(config.refractory_period, 2);
        assert_eq!(config.learning_rate, 0.01);
    }

    #[test]
    fn test_spiking_neuron_default() {
        let neuron = SpikingNeuron::default();

        assert_eq!(neuron.membrane_potential, 0.0);
        assert_eq!(neuron.synaptic_current, 0.0);
        assert_eq!(neuron.time_since_spike, 0);
        assert_eq!(neuron.target_rate, 0.1);
    }

    #[test]
    fn test_event_driven_processing() {
        let current =
            Array2::from_shape_vec((3, 3), vec![0.0, 1.0, 0.0, 1.0, 0.5, 1.0, 0.0, 1.0, 0.0])
                .expect("Test: operation failed");

        let previous =
            Array2::from_shape_vec((3, 3), vec![0.0, 0.5, 0.0, 0.5, 0.5, 0.5, 0.0, 0.5, 0.0])
                .expect("Test: operation failed");

        let config = NeuromorphicConfig::default();
        let result = event_driven_processing(current.view(), Some(previous.view()), &config)
            .expect("Operation failed");

        assert_eq!(result.0.dim(), (3, 3));
        assert!(!result.1.is_empty()); // Should generate events
    }

    #[test]
    fn test_homeostatic_adaptive_filter() {
        let image = Array2::from_shape_vec((5, 5), (0..25).map(|x| x as f64 / 25.0).collect())
            .expect("Operation failed");

        let config = NeuromorphicConfig::default();
        let result =
            homeostatic_adaptive_filter(image.view(), &config, 5).expect("Operation failed");

        assert_eq!(result.dim(), (5, 5));
        assert!(result.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_temporal_coding_feature_extraction() {
        let image = Array2::from_shape_vec((4, 4), (0..16).map(|x| x as f64 / 16.0).collect())
            .expect("Operation failed");

        let edge_detector = Array2::from_shape_vec(
            (3, 3),
            vec![-1.0, -1.0, -1.0, -1.0, 8.0, -1.0, -1.0, -1.0, -1.0],
        )
        .expect("Test: operation failed");

        let detectors = vec![edge_detector];
        let config = NeuromorphicConfig::default();

        let result = temporal_coding_feature_extraction(image.view(), &detectors, &config, 10)
            .expect("Operation failed");

        assert_eq!(result.dim(), (1, 4, 4));
        assert!(result.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_stdp_unsupervised_learning() {
        let trainingimage =
            Array2::from_shape_vec((6, 6), (0..36).map(|x| x as f64 / 36.0).collect())
                .expect("Operation failed");
        let trainingimages = vec![trainingimage.view()];

        let config = NeuromorphicConfig::default();
        let learned_filter = stdp_unsupervised_learning(&trainingimages, (3, 3), &config, 2)
            .expect("Operation failed");

        assert_eq!(learned_filter.dim(), (3, 3));
        assert!(learned_filter.iter().all(|&x| x.is_finite()));
    }
}
