What is the difference between artificial neural networks (ANNs) and biological neural networks?

Artificial neural networks (ANNs) and biological neural networks differ fundamentally in structure, function, and learning mechanisms. ANNs are computational models inspired by the brain’s neural networks but simplified for practical implementation. Biological networks, in contrast, are complex systems of interconnected neurons that process information through electrochemical signals. While ANNs aim to replicate certain aspects of biological learning, they operate under constraints imposed by current hardware and algorithms, leading to significant differences in efficiency, adaptability, and scalability.

Structure and Connectivity Biological neural networks are composed of neurons connected by synapses, which dynamically adjust their strength through processes like synaptic plasticity. These networks are massively parallel, with billions of neurons and trillions of connections operating simultaneously. For example, the human brain processes sensory input, motor control, and cognition in real time using layered structures like the cortex. ANNs, however, are structured as layers of artificial neurons (nodes) with weighted connections, typically organized into input, hidden, and output layers. Unlike biological networks, ANNs lack the brain’s three-dimensional interconnectivity and often rely on simpler, feedforward architectures (e.g., CNNs for images) or recurrent designs (e.g., RNNs for sequences). Additionally, biological neurons communicate via spikes (action potentials), while ANNs use continuous activation functions like ReLU or sigmoid, which approximate but don’t fully replicate biological behavior.

Learning and Adaptation Biological networks learn through mechanisms like Hebbian plasticity (“cells that fire together wire together”) and reinforcement from neurotransmitters like dopamine. This allows humans to learn tasks with minimal data—for instance, recognizing a cat after seeing it once. ANNs, conversely, depend on supervised learning with labeled datasets and optimization techniques like backpropagation. For example, training an image classifier requires thousands of labeled examples to adjust weights via gradient descent. Biological systems also adapt continuously—repurposing neurons after injury or learning new skills without catastrophic forgetting—while ANNs often struggle with retaining prior knowledge when fine-tuned on new tasks. Techniques like transfer learning or spiking neural networks (SNNs) attempt to bridge this gap but remain far less flexible than biological systems.

Efficiency and Energy Use Biological networks are highly energy-efficient, with the human brain consuming roughly 20 watts—equivalent to a laptop—while performing tasks like real-time decision-making. ANNs, especially large models like transformers, require significant computational resources (e.g., GPUs) and energy, often scaling with model size. For instance, training GPT-3 consumed over 1,000 MWh of electricity. Biological systems also excel at processing noisy or incomplete data; humans can recognize a face in poor lighting, while ANNs may fail without preprocessing. Finally, biological networks operate asynchronously, with neurons firing at different rates, whereas ANNs rely on synchronous matrix operations. These differences highlight the trade-offs between biological inspiration and engineering practicality, guiding developers to optimize ANNs within hardware limitations while drawing insights from neuroscience.