scirs2_ndimage/biological_vision_inspired/

predictive_coding.rs

//! Predictive Coding for Visual Processing
//!
//! This module implements predictive coding mechanisms inspired by hierarchical
//! processing in the brain for efficient visual representation.

use scirs2_core::ndarray::{Array3, Array4, ArrayView2};
use scirs2_core::numeric::{Float, FromPrimitive};

use super::config::{BiologicalVisionConfig, PredictiveCodingSystem};
use crate::error::{NdimageError, NdimageResult};

/// Predictive Coding for Visual Processing
///
/// Implements predictive coding mechanisms inspired by hierarchical
/// processing in the brain for efficient visual representation.
pub fn predictive_coding_visual_processing<T>(
    image_sequence: &[ArrayView2<T>],
    config: &BiologicalVisionConfig,
) -> NdimageResult<PredictiveCodingSystem>
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();
    let mut predictive_system = initialize_predictive_coding_system(height, width, config)?;

    // Process temporal sequence
    for (t, image) in image_sequence.iter().enumerate() {
        // Generate predictions from higher levels
        generate_predictions(&mut predictive_system, t, config)?;

        // Compute prediction errors
        compute_prediction_errors(&mut predictive_system, image, config)?;

        // Update prediction models based on errors
        update_prediction_models(&mut predictive_system, config)?;

        // Estimate confidence
        estimate_prediction_confidence(&mut predictive_system, config)?;

        // Adapt to prediction errors
        adapt_to_prediction_errors(&mut predictive_system, config)?;
    }

    Ok(predictive_system)
}

/// Initialize the predictive coding system
pub fn initialize_predictive_coding_system(
    height: usize,
    width: usize,
    config: &BiologicalVisionConfig,
) -> NdimageResult<PredictiveCodingSystem> {
    let num_levels = config.cortical_layers;
    let mut prediction_models = Vec::new();
    let mut prediction_errors = Vec::new();
    let mut temporal_predictions = Vec::new();
    let mut confidence_estimates = Vec::new();

    for level in 0..num_levels {
        let level_height = height / (level + 1);
        let level_width = width / (level + 1);
        let num_features = 2_usize.pow(level as u32 + 3);

        // Prediction models for each level
        prediction_models.push(Array3::zeros((num_features, level_height, level_width)));

        // Prediction errors
        prediction_errors.push(Array3::zeros((num_features, level_height, level_width)));

        // Temporal predictions (includes time dimension)
        temporal_predictions.push(Array4::zeros((
            config.motion_prediction_window,
            num_features,
            level_height,
            level_width,
        )));

        // Confidence estimates
        confidence_estimates.push(Array3::zeros((num_features, level_height, level_width)));
    }

    Ok(PredictiveCodingSystem {
        prediction_models,
        prediction_errors,
        temporal_predictions,
        confidence_estimates,
    })
}

/// Generate predictions from higher levels to lower levels
pub fn generate_predictions(
    system: &mut PredictiveCodingSystem,
    time: usize,
    config: &BiologicalVisionConfig,
) -> NdimageResult<()> {
    let num_levels = system.prediction_models.len();

    // Generate predictions from higher to lower levels
    for level in (0..num_levels - 1).rev() {
        let higher_level = level + 1;

        // Get dimensions
        let (pred_features, pred_height, pred_width) = system.prediction_models[level].dim();
        let (higher_features, higher_height, higher_width) =
            system.prediction_models[higher_level].dim();

        // Generate predictions by upsampling from higher level
        for pred_f in 0..pred_features {
            for pred_y in 0..pred_height {
                for pred_x in 0..pred_width {
                    // Map to higher level coordinates
                    let higher_y = pred_y * higher_height / pred_height;
                    let higher_x = pred_x * higher_width / pred_width;

                    if higher_y < higher_height && higher_x < higher_width {
                        let mut prediction = 0.0;

                        // Average predictions from higher level features
                        for higher_f in 0..higher_features.min(pred_features) {
                            prediction += system.prediction_models[higher_level]
                                [(higher_f, higher_y, higher_x)];
                        }

                        system.prediction_models[level][(pred_f, pred_y, pred_x)] =
                            prediction / higher_features.min(pred_features) as f64;
                    }
                }
            }
        }
    }

    Ok(())
}

/// Compute prediction errors between predictions and actual input
pub fn compute_prediction_errors<T>(
    system: &mut PredictiveCodingSystem,
    image: &ArrayView2<T>,
    config: &BiologicalVisionConfig,
) -> NdimageResult<()>
where
    T: Float + FromPrimitive + Copy,
{
    let (img_height, img_width) = image.dim();

    // Compute prediction errors for the lowest level (closest to input)
    if let Some(prediction_errors) = system.prediction_errors.get_mut(0) {
        let (num_features, level_height, level_width) = prediction_errors.dim();

        for feature_idx in 0..num_features {
            for y in 0..level_height {
                for x in 0..level_width {
                    // Map to image coordinates
                    let img_y = y * img_height / level_height;
                    let img_x = x * img_width / level_width;

                    if img_y < img_height && img_x < img_width {
                        let actual_value = image[(img_y, img_x)].to_f64().unwrap_or(0.0);
                        let predicted_value = system.prediction_models[0][(feature_idx, y, x)];
                        let error = actual_value - predicted_value;

                        prediction_errors[(feature_idx, y, x)] = error;
                    }
                }
            }
        }
    }

    // Propagate errors up through the hierarchy
    for level in 1..system.prediction_errors.len() {
        let (current_features, current_height, current_width) =
            system.prediction_errors[level].dim();
        let (lower_features, lower_height, lower_width) = system.prediction_errors[level - 1].dim();

        for feature_idx in 0..current_features {
            for y in 0..current_height {
                for x in 0..current_width {
                    // Pool errors from lower level
                    let mut error_sum = 0.0;
                    let mut count = 0;

                    let scale_y = lower_height / current_height;
                    let scale_x = lower_width / current_width;

                    for dy in 0..scale_y {
                        for dx in 0..scale_x {
                            let lower_y = y * scale_y + dy;
                            let lower_x = x * scale_x + dx;

                            if lower_y < lower_height && lower_x < lower_width {
                                for lower_f in 0..lower_features.min(current_features) {
                                    error_sum += system.prediction_errors[level - 1]
                                        [(lower_f, lower_y, lower_x)]
                                        .abs();
                                    count += 1;
                                }
                            }
                        }
                    }

                    system.prediction_errors[level][(feature_idx, y, x)] = if count > 0 {
                        error_sum / count as f64
                    } else {
                        0.0
                    };
                }
            }
        }
    }

    Ok(())
}

/// Update prediction models based on prediction errors
pub fn update_prediction_models(
    system: &mut PredictiveCodingSystem,
    config: &BiologicalVisionConfig,
) -> NdimageResult<()> {
    let learning_rate = 0.01;

    // Update prediction models based on errors
    for level in 0..system.prediction_models.len() {
        let (num_features, height, width) = system.prediction_models[level].dim();

        for feature_idx in 0..num_features {
            for y in 0..height {
                for x in 0..width {
                    let error = system.prediction_errors[level][(feature_idx, y, x)];
                    let current_prediction = system.prediction_models[level][(feature_idx, y, x)];

                    // Update prediction based on error
                    let updated_prediction = current_prediction + learning_rate * error;
                    system.prediction_models[level][(feature_idx, y, x)] = updated_prediction;
                }
            }
        }
    }

    Ok(())
}

/// Estimate confidence in predictions based on error magnitude
pub fn estimate_prediction_confidence(
    system: &mut PredictiveCodingSystem,
    config: &BiologicalVisionConfig,
) -> NdimageResult<()> {
    for level in 0..system.confidence_estimates.len() {
        let (num_features, height, width) = system.confidence_estimates[level].dim();

        for feature_idx in 0..num_features {
            for y in 0..height {
                for x in 0..width {
                    let error = system.prediction_errors[level][(feature_idx, y, x)].abs();

                    // Confidence is inversely related to error
                    let confidence = 1.0 / (1.0 + error);
                    system.confidence_estimates[level][(feature_idx, y, x)] = confidence;
                }
            }
        }
    }

    Ok(())
}

/// Adapt system parameters based on prediction errors
pub fn adapt_to_prediction_errors(
    system: &mut PredictiveCodingSystem,
    config: &BiologicalVisionConfig,
) -> NdimageResult<()> {
    let adaptation_rate = 0.001;

    // Simple adaptation: adjust prediction models based on persistent errors
    for level in 0..system.prediction_models.len() {
        let (num_features, height, width) = system.prediction_models[level].dim();

        for feature_idx in 0..num_features {
            for y in 0..height {
                for x in 0..width {
                    let error = system.prediction_errors[level][(feature_idx, y, x)];

                    // If error is consistently high, adjust the prediction model
                    if error.abs() > config.prediction_error_threshold {
                        let current_model = system.prediction_models[level][(feature_idx, y, x)];
                        let adapted_model =
                            current_model * (1.0 - adaptation_rate) + error * adaptation_rate;
                        system.prediction_models[level][(feature_idx, y, x)] = adapted_model;
                    }
                }
            }
        }
    }

    Ok(())
}