👁️ Sensory Processing Systems

Neural Algorithms for Sensory Information Processing and Feature Detection

4 Processes
4 Categories
100% Complete

🎯 Sensory Processing Overview

This document showcases Sensory Processing Algorithms as part of the Genome Logic Modeling Project (GLMP). These processes represent the sophisticated computational mechanisms by which neural systems extract, filter, and interpret sensory information from the environment.

Each process is modeled using the Programming Framework, demonstrating how sensory processing operates as biological computation with precise algorithms for feature detection, signal filtering, and information integration.

The processes are organized into four main categories: Visual Processing, Auditory Processing, Olfactory Processing, and Multisensory Integration.

🎨 Programming Framework Color Coding

Triggers: Sensory stimuli, light, sound, chemical signals
Enzymes: Transduction proteins, ion channels, signaling molecules
Intermediates: Action potentials, synaptic signals, neural representations
Processing: Feature detection, signal filtering, pattern recognition
Products: Perceptual representations, behavioral responses, cognitive integration

👁️ Visual Processing

Computational algorithms for visual information processing

1. Retinal Processing and Feature Detection

The initial computational stage of visual processing where retinal circuits extract basic visual features through parallel processing pathways, including edge detection, motion sensitivity, and contrast enhancement.

graph TD A[Light Stimulus] --> B[Photoreceptor Activation] B --> C[Phototransduction Cascade] C --> D[Hyperpolarization] D --> E[Bipolar Cell Activation] E --> F[Ganglion Cell Response] F --> G[Action Potential Generation] H[Edge Detection] --> I[Center-Surround Organization] I --> J[Lateral Inhibition] J --> K[Contrast Enhancement] K --> L[Feature Extraction] M[Motion Detection] --> N[Temporal Filtering] N --> O[Motion-Sensitive Circuits] O --> P[Direction Selectivity] P --> Q[Motion Representation] R[Color Processing] --> S[Cone Type Activation] S --> T[Color Opponency] T --> U[Color Channel Separation] U --> V[Color Representation] style A fill:#ff6b6b,color:#fff style B fill:#ffd43b,color:#000 style C fill:#51cf66,color:#fff style D fill:#74c0fc,color:#fff style E fill:#74c0fc,color:#fff style F fill:#74c0fc,color:#fff style G fill:#b197fc,color:#fff style H fill:#ff6b6b,color:#fff style I fill:#51cf66,color:#fff style J fill:#51cf66,color:#fff style K fill:#51cf66,color:#fff style L fill:#b197fc,color:#fff style M fill:#ff6b6b,color:#fff style N fill:#51cf66,color:#fff style O fill:#51cf66,color:#fff style P fill:#51cf66,color:#fff style Q fill:#b197fc,color:#fff style R fill:#ff6b6b,color:#fff style S fill:#ffd43b,color:#000 style T fill:#51cf66,color:#fff style U fill:#51cf66,color:#fff style V fill:#b197fc,color:#fff
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👂 Auditory Processing

Neural algorithms for sound processing and analysis

2. Cochlear Processing and Frequency Analysis

The computational transformation of sound waves into neural representations through mechanical-to-electrical transduction, frequency decomposition, and temporal pattern analysis.

graph TD A[Sound Wave] --> B[Cochlear Fluid Movement] B --> C[Basilar Membrane Vibration] C --> D[Hair Cell Deflection] D --> E[Ion Channel Opening] E --> F[Receptor Potential] F --> G[Neurotransmitter Release] G --> H[Spiral Ganglion Activation] H --> I[Auditory Nerve Signal] J[Frequency Analysis] --> K[Tonotopic Organization] K --> L[Frequency Tuning] L --> M[Frequency Representation] N[Temporal Processing] --> O[Phase Locking] O --> P[Temporal Coding] P --> Q[Timing Information] R[Intensity Coding] --> S[Rate Coding] S --> T[Loudness Representation] T --> U[Intensity Discrimination] style A fill:#ff6b6b,color:#fff style B fill:#51cf66,color:#fff style C fill:#51cf66,color:#fff style D fill:#ffd43b,color:#000 style E fill:#51cf66,color:#fff style F fill:#74c0fc,color:#fff style G fill:#51cf66,color:#fff style H fill:#74c0fc,color:#fff style I fill:#b197fc,color:#fff style J fill:#ff6b6b,color:#fff style K fill:#51cf66,color:#fff style L fill:#51cf66,color:#fff style M fill:#b197fc,color:#fff style N fill:#ff6b6b,color:#fff style O fill:#51cf66,color:#fff style P fill:#51cf66,color:#fff style Q fill:#b197fc,color:#fff style R fill:#ff6b6b,color:#fff style S fill:#51cf66,color:#fff style T fill:#51cf66,color:#fff style U fill:#b197fc,color:#fff
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👃 Olfactory Processing

Chemical signal processing and odor recognition

3. Olfactory Receptor Processing

The computational transformation of chemical signals into neural representations through receptor-ligand interactions, signal amplification, and combinatorial coding of odor identity.

graph TD A[Odorant Molecule] --> B[Olfactory Receptor Binding] B --> C[G-Protein Activation] C --> D[Adenylyl Cyclase Activation] D --> E[cAMP Production] E --> F[Ion Channel Opening] F --> G[Depolarization] G --> H[Action Potential Generation] H --> I[Olfactory Bulb Signal] J[Receptor Specificity] --> K[Ligand-Receptor Matching] K --> L[Combinatorial Coding] L --> M[Odor Identity] N[Concentration Coding] --> O[Receptor Sensitivity] O --> P[Intensity Representation] P --> Q[Concentration Discrimination] R[Temporal Dynamics] --> S[Adaptation Mechanisms] S --> T[Response Modulation] T --> U[Temporal Coding] style A fill:#ff6b6b,color:#fff style B fill:#ffd43b,color:#000 style C fill:#51cf66,color:#fff style D fill:#ffd43b,color:#000 style E fill:#51cf66,color:#fff style F fill:#51cf66,color:#fff style G fill:#74c0fc,color:#fff style H fill:#74c0fc,color:#fff style I fill:#b197fc,color:#fff style J fill:#ff6b6b,color:#fff style K fill:#51cf66,color:#fff style L fill:#51cf66,color:#fff style M fill:#b197fc,color:#fff style N fill:#ff6b6b,color:#fff style O fill:#51cf66,color:#fff style P fill:#51cf66,color:#fff style Q fill:#b197fc,color:#fff style R fill:#ff6b6b,color:#fff style S fill:#51cf66,color:#fff style T fill:#51cf66,color:#fff style U fill:#b197fc,color:#fff
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🔄 Multisensory Integration

Cross-modal information processing and integration

4. Cross-Modal Integration Algorithms

The computational integration of information from multiple sensory modalities to create unified perceptual representations and enhance detection, localization, and recognition of environmental stimuli.

graph TD A[Visual Input] --> B[Visual Processing Stream] C[Auditory Input] --> D[Auditory Processing Stream] E[Somatosensory Input] --> F[Somatosensory Stream] B --> G[Multisensory Convergence] D --> G F --> G G --> H[Cross-Modal Integration] H --> I[Unified Representation] I --> J[Enhanced Detection] K[Temporal Synchrony] --> L[Coincidence Detection] L --> M[Spatial Alignment] M --> N[Integration Enhancement] O[Modality-Specific Processing] --> P[Feature Extraction] P --> Q[Cross-Modal Matching] Q --> R[Integration Decision] R --> S[Behavioral Response] style A fill:#ff6b6b,color:#fff style B fill:#74c0fc,color:#fff style C fill:#ff6b6b,color:#fff style D fill:#74c0fc,color:#fff style E fill:#ff6b6b,color:#fff style F fill:#74c0fc,color:#fff style G fill:#51cf66,color:#fff style H fill:#51cf66,color:#fff style I fill:#b197fc,color:#fff style J fill:#b197fc,color:#fff style K fill:#ff6b6b,color:#fff style L fill:#51cf66,color:#fff style M fill:#51cf66,color:#fff style N fill:#b197fc,color:#fff style O fill:#ff6b6b,color:#fff style P fill:#51cf66,color:#fff style Q fill:#51cf66,color:#fff style R fill:#51cf66,color:#fff style S fill:#b197fc,color:#fff
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