flowchart TD A[Thought Initiation\nPrefrontal Cortex] --> B[Sensory Input\nThalamus] B --> C[Memory Retrieval\nHippocampus] C --> D[Action Selection\nBasal Ganglia] D --> E[Error Checking\nAnterior Cingulate Cortex] E --> F[Solution Integration\nParietal Cortex] F --> G[Decision Finalization\nPrefrontal Cortex] G --> H[Motor Execution\nMotor Cortex] H --> I[Verbal Reply\nBroca's Area] I --> J[Action Output\nBody Movement] E -->|Recalibrate| D F -->|Refine| C G -->|Reevaluate| E classDef highlight fill:#e6f7ff,stroke:#1890ff,stroke-width:2px; class A,G highlight;

pie title Action Selection Probability "Verbal Reply" : 45 "Physical Action" : 25 "Memory Search" : 20 "Emotional Reaction" : 10

mindmap root((Neurons)) Artificial Neurons History McCulloch-Pitts Model (1943) ::icon(fa fa-history) Perceptron (1958) Frank Rosenblatt Backpropagation (1986) Rumelhart & Hinton Deep Learning Revolution 2006 onwards Geoffrey Hinton Yann LeCun Yoshua Bengio Structure Input Layer Weighted connections Multiple inputs Bias terms Processing Unit Summation function Activation function Sigmoid ReLU Tanh Softmax Leaky ReLU Output Single output value Passes to next layer Mathematical Model Linear Combination Weighted sum formula Plus bias term Activation Functions Sigmoid function Exponential form Non-linearity Weight Updates Gradient descent Backpropagation Learning rate Types Perceptron Single layer Linear classification Multi-layer Perceptron Hidden layers Non-linear problems Convolutional Neurons Feature detection Spatial processing Recurrent Neurons Memory capability Sequential data LSTM/GRU Long-term dependencies Gating mechanisms Applications Computer Vision Image recognition Object detection Facial recognition Medical imaging Natural Language Processing Machine translation Sentiment analysis Text generation Chatbots Autonomous Systems Self-driving cars Robotics Navigation Game Playing Chess engines Go (AlphaGo) Video games Healthcare Drug discovery Diagnosis Treatment planning Finance Trading algorithms Risk assessment Fraud detection Advantages Parallel Processing Massive parallelization GPU acceleration Scalability Handle big data Distributed computing Consistency Deterministic behavior Reproducible results Speed Fast computation Real-time processing Precision Exact calculations High accuracy Limitations Energy Consumption High power requirements Cooling needs Training Requirements Large datasets needed Computational resources Interpretability Black box problem Difficult to explain Overfitting Memorization vs learning Generalization issues Hardware Dependence Specialized chips Cost barriers Natural Neurons Evolution Cambrian Explosion 540 million years ago ::icon(fa fa-leaf) Nervous System Development Cnidarians (jellyfish) Bilateral symmetry Centralization Brain Evolution Fish brains Mammalian cortex Human neocortex Anatomy Cell Body (Soma) Nucleus DNA storage Gene expression Mitochondria Energy production ATP synthesis Endoplasmic Reticulum Protein synthesis Golgi Apparatus Protein processing Dendrites Branched extensions Synaptic receptors Signal reception Dendritic spines Plasticity Axon Signal transmission Myelin sheath Insulation Faster conduction Axon terminals Synaptic vesicles Synapse Synaptic cleft Neurotransmitters Dopamine Serotonin Acetylcholine GABA Glutamate Receptors Synaptic plasticity Physiology Resting Potential Negative 70 millivolts Sodium potassium pump Ion gradients Action Potential Depolarization Threshold negative 55mV All or nothing principle Propagation Refractory period Synaptic Transmission Chemical signaling Neurotransmitter release Receptor binding Signal integration Plasticity Hebbian learning rule LTP and LTD mechanisms Structural changes Functional changes Types Sensory Neurons Photoreceptors Mechanoreceptors Chemoreceptors Thermoreceptors Nociceptors Motor Neurons Upper motor neurons Lower motor neurons Muscle control Interneurons Local circuits Information processing Integration Pyramidal Neurons Cortical processing Long-range connections Purkinje Cells Cerebellum Motor learning Dopaminergic Neurons Reward processing Motivation Functions Information Processing Pattern recognition Feature detection Integration Memory Formation Encoding Storage Retrieval Consolidation Motor Control Movement planning Execution Coordination Sensory Processing Perception Filtering Enhancement Consciousness Awareness Attention Self-recognition Emotions Limbic system Amygdala Emotional memory Networks Local Circuits Cortical columns Microcircuits Lateral inhibition Long-range Connections Corpus callosum Thalamic connections Brainstem pathways Brain Regions Cerebral cortex Hippocampus Cerebellum Brainstem Limbic system Oscillations Gamma waves Theta rhythm Alpha waves Sleep spindles Advantages Energy Efficiency 20 watts total Efficient computation Adaptability Continuous learning Plasticity Robustness Fault tolerance Graceful degradation Parallel Processing Massive parallelism Distributed processing Context Awareness Embodied cognition Environmental adaptation Creativity Novel combinations Insight generation Limitations Processing Speed Slow compared to computers Millisecond timescales Precision Noisy signals Approximate computation Memory Capacity Limited working memory Forgetting Training Time Years of development Slow learning Vulnerability Damage sensitivity Aging effects Disease susceptibility Comparison Similarities Information Processing Input-output systems Signal integration Pattern recognition Network Architecture Interconnected units Layered organization Hierarchical processing Learning Capability Adaptation Experience-based changes Memory formation Non-linearity Activation functions Threshold behavior Complex dynamics Key Differences Speed vs Efficiency Artificial: Fast processing Natural: Energy efficient Precision vs Robustness Artificial: Exact computation Natural: Approximate but robust Learning Style Artificial batch learning Natural continuous adaptation Architecture Artificial: Designed structure Natural: Evolved complexity Processing Artificial: Digital/discrete Natural: Analog/continuous Memory Artificial: Perfect recall Natural: Reconstructive memory Creativity Artificial: Pattern matching Natural: True innovation Hybrid Approaches Neuromorphic Computing Brain-inspired hardware Spiking neural networks Event-driven processing Memristors Synaptic behavior Analog memory Low power consumption Quantum Neural Networks Quantum superposition Entanglement Parallel processing Biological-Artificial Interface Brain-computer interfaces Neural implants Cyborg systems Future Directions Convergence Bio-inspired AI Artificial consciousness Hybrid intelligence Applications Personalized medicine Augmented cognition Sustainable computing Challenges Ethical considerations Safety concerns Societal impact Research Areas Computational neuroscience Cognitive architectures Embodied AI Neuroplasticity models