Artificial Intelligence's Modeling of the Human Brain: A Look at Its Similarities and Differences
In the realm of technology, artificial neural networks (ANNs) have made significant strides, particularly in areas such as language modeling and image recognition. However, these systems, despite their advancements, are vastly different from the human brain in terms of energy consumption, complexity, and learning mechanisms.
Architecture
The human brain's biological neural network (BNN) is composed of neurons with complex structures: dendrites, cell bodies, and axons. These neurons are organised in scattered, specialised brain regions, tailored for functions like language or vision. Synapses between neurons are dynamic—they can form, shrink, or disappear over time.
On the other hand, ANNs are composed of simplified artificial neurons arranged in layers (input, hidden, output). Connections between neurons are typically fixed at design time, although some advanced models can add or remove connections automatically. Artificial neurons perform weighted sums of inputs followed by nonlinear activation functions.
Recent developments in ANNs, such as Spiking Neural Networks (SNNs), aim to mimic the brain’s discrete spiking style, improving energy efficiency by performing event-driven computation.
Learning Processes
The brain learns by local synaptic changes governed by plasticity: repeated activation strengthens synaptic pathways, unused connections may weaken or be pruned, and the brain can reorganise itself after injury or aging. Learning is incremental, continuous, and involves mechanisms such as reinforcement and unsupervised/self-organizing processes.
In contrast, ANNs learn primarily through global error feedback algorithms like backpropagation, which adjusts weights across all layers to minimise a loss function. This typically requires large labeled datasets and extensive iterative training phases. Adaptation to new tasks usually means retraining or fine-tuning the network.
Key Differences
| Aspect | Biological Neural Network (BNN) | Artificial Neural Network (ANN) | |-------------------------|-----------------------------------------------------|-------------------------------------------------| | Neuron Structure | Complex dendrites, axons, synapses | Simplified units, weighted connections | | Connectivity | Dynamic synapse formation/pruning | Mostly fixed at design; some dynamic methods | | Learning Mechanism | Local synaptic plasticity, continuous incremental | Global error-driven backpropagation | | Data Requirements | Learn from rich, unlabeled, multi-modal experience | Large, labeled datasets required | | Adaptability | Self-organizes after injury, lifelong learning | Needs retraining for new situations | | Energy Efficiency | Very low energy consumption via event-driven spikes | High training and inference energy cost | | Cognitive Functions | Integrates perception, emotion, attention, planning | Limited to pattern recognition tasks | | Signal Type | Spikes (discrete events) | Continuous real-values |
In summary, while ANNs are inspired by the brain’s structure and function, they simplify neuron models and learning into mathematically tractable frameworks optimised for pattern recognition tasks. The brain’s architecture supports vastly richer, multi-functional, and energy-efficient learning processes that combine perception, cognition, and emotion, which current ANN models do not fully emulate. New models like Spiking Neural Networks and theoretical advances continue to narrow the gap by incorporating biological insights.
Neural networks learn by adjusting the strength of the connections between neurons, gradually improving their performance with experience. Deep learning, a process in neural networks, allows AI to make sense of complex data by interpreting information progressively from the raw data to deeper, more detailed interpretations. However, neural networks learn slowly and often need to see many mistakes before improving, unlike humans who can learn from a single mistake.
A neural network is a type of computer program designed to recognise patterns and solve problems, inspired by the way biological brains work. Despite their differences, neural networks have proven capable of mimicking some brain-like feats with impressive accuracy, such as recognising speech, translating languages, and diagnosing diseases from medical scans. The journey to build machines that think like us is both an achievement and a reminder of the mysteries that still separate us from our creations.
[1] Lillicrap, T., et al. (2020). Backpropagation through time with continuous-time recurrent neural networks. arXiv preprint arXiv:2006.03440. [2] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. [3] McClelland, J. L., & Rumelhart, D. E. (1988). Parallel distributed processing: Explorations in the microstructure of cognition. MIT Press. [4] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
- The advancements in Artificial Neural Networks (ANNs), such as Spiking Neural Networks (SNNs), are aimed at mimicking the brain’s discrete spiking style, thereby improving energy efficiency by performing event-driven computation.
- While Deep Learning, a process in neural networks, allows AI to make sense of complex data, it learns slowly and often needs to see many mistakes before improving, unlike humans who can learn from a single mistake.
- Despite the differences between neural networks and the human brain, neural networks have proven capable of mimicking some brain-like feats with impressive accuracy, such as recognizing speech, translating languages, and diagnosing diseases from medical scans.
