Biological & Artificial Neural Networks

Exploring the distinctions and similarities between biological neural networks and their artificial counterparts to enhance our understanding of neural networks.

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Artificial Intelligence, The Human Mind
Academic Research
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1 May 2024
Date of Publication
Image courtesy of the Massachusetts Institute of Technology.
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Comparative Analysis of Biological and Artificial Neural Networks


The realm of neural networks, spanning from biological to artificial constructs, represents a fascinating convergence of biology and technology. This article delves into the intricate world of neural networks, exploring the nuanced differences and surprising similarities between biological neural networks (BNNs) and artificial neural networks (ANNs). Furthermore, I aim to bridge the gap between neurobiological insights and computational applications and provide a comprehensive understanding of how these systems function, learn, and evolve.

Biological Neural Networks (BNNs)

Biological neural networks are complex systems composed of 1x1011 (100 Billion) neurons, each capable of forming approximately 6x1013 (60 Trillion) synaptic connections. These networks are the foundation of the nervous system in all vertebrates and many invertebrates. The basic unit of this network, the neuron, operates through electrochemical processes. Neurotransmitters such as glutamate, which generally mediates excitatory signals, and GABA, which mediates inhibitory signals, play crucial roles in these electrochemical processes.

Neuron Structure and Function

  • Soma: The cell body which contains the nucleus and genetic materials.
  • Dendrites: Branch-like structures that receive messages from other neurons.
  • Axon: A long, slender projection that conducts electrical impulses away from the neuron's cell body.
  • Synapses: The junctions between neurons where neurotransmitters are released.

Types of Neurons

  • Pyramidal Cells: The Pyramidal cells within the prefrontal cortex are most likely responsible for processing input from the primary somatosensory cortex, visual cortex, and auditory cortex.
  • Interneurons: The Interneurons are neurons that connect different brain regions and are not directly motor neurons or sensory neurons.

The signal transmission in neurons involves a complex process where electrical signals, or action potentials, are generated at the axon hillock and travel along the axon to the synapse. Together, these neurons and their functions form complex circuits and networks within the brain, and serve to stimulate or inhibit their activity, similar to a chain reaction with back propagation (which we will discuss below). These processes are fundamental to a neuron's ability to communicate with other neurons.

Humans learn in interesting ways. It is important to differentiate that humans learn using purely biological basis, but are influenced by various cognitive, emotional, and environmental factors. The first step in learning within BNNs is the process of perception - where sensory information is received and processed from visual, auditory, and sensory inputs. As a child, humans must learn how to concentrate their attention on relevant stimuli while ignoring others, as concentration is important in storing short-term and long-term memories. Eventually, information from short-term memories is consolidated (or batched) into long-term memories through chemical processes, where it can be retrieved at a later time. These foundational elements in human memory formation are important when receiving input, but it's also worth noting that there are types of learning, states of learning, and then neuroplasticity to assist in forming new connections within the brain.

Neuroplasticity is the brains ability to reorganize itself by forming new connections within neurons to compensate for injury and disease to adjust their activities in response to new situations or changes in the environment. Neuroplasticity allows the brain to learn new, complex ideas, and this is closely related to Long-Term Potentiation (LTP) as synapses are strengthened based on recent patterns of activity. In this way, the brain uses neuroplasticity to "encode" new information.

Artificial Neural Networks (ANNs)

Artificial neural networks are computational models designed to simulate the way biological neural networks process information. ANNs are used extensively in machine learning and artificial intelligence for tasks that involve pattern recognition, speech synthesis, and data classification, among others. Using complex mathematical structures allow convolutional layers (inspired by the organization of the Biological Visual Cortex), and recurrent layers to process, identify, and "memorize" data input in a artificial neural network.

Basic Components of ANNs

  • Input Layer: Receives various forms of external data.
  • Hidden Layers: Intermediate layers where data transformations occur through a process known as feature extraction.
  • Output Layer: Delivers the final decision or output of the ANN.

Specialized Layers of ANNs

  • Gated Recurrent Units (GRUs) assist in the creation of recurrent neural network architectures as they work alongside Long Short-Term Memory (LSTMs) to sequence data in a method that allows information to be selectively remembered or forgotten over time.

Backpropagation is how Neural Networks learn. This process can be thought of a series of "trial and errors" tested by the mathematical function to optimize the output. To train the neural network, data is passed through the network layer by layer, as shown in the "Diagram of Artificial Neural Network" below. The output uses a loss function such as W=Wη⋅(∂W/E), where η is the learning rate, W represents the weights, and (∂W/E) is the gradient error with respect to the weights. Through the process of backpropagation, an error flows backwards through the network, starting from the output layer and goes towards the input layer. Along the way, the gradient is calculated with respect to each weight in the network. This gradient indicates the direction and magnitude by which the weights need to be adjusted to minimize the error.

Core Formula in ANNs


Where 𝑥1,𝑥2,𝑥3 are inputs, 𝑤1,𝑤2,𝑤3 are weights, and 𝑏 is the bias. This formula is fundamental in the operation of neurons within an ANN, determining how inputs are converted into outputs.

Diagram of Artificial Neuron

Diagram of Artificial Neural Network

A Comparative Analysis

Speed of Operation

  • BNNs: Operate at a slower speed due to the biological limitations of neurotransmitter diffusion and synaptic delay.
  • ANNs: Benefit from the speed of electronic computations, processing information many orders of magnitude faster than BNNs.

Processing Capabilities

  • BNNs: Capable of handling complex patterns and learning from minimal data due to the adaptability of synaptic connections.
  • ANNs: Require large datasets to learn but can process data at a much faster rate once trained.

Size and Complexity

  • BNNs: Comprise approximately 100 billion neurons in the human brain, each with the potential to connect to thousands of others.
  • ANNs: While large networks can consist of millions of artificial neurons, the complexity still falls short of biological standards.

Fault Tolerance

  • BNNs: Exhibit robustness to damage; loss of neurons can often be compensated by other parts of the network.
  • ANNs: Susceptible to catastrophic forgetting and may suffer performance drops if key neurons (nodes) are damaged or removed.

Learning Mechanisms

  • BNNs: Adapt synaptic strengths based on experience, a process supported by neuroplasticity.
  • ANNs: Adjust weights based on error feedback methods such as backpropagation, lacking the biological nuances of synaptic plasticity.

Future Directions

The integration of neurobiology within artificial intelligence holds the potential for the development of more advanced and adaptable neural network models. By incorporating neurobiological principles into the design of ANNs, future systems may be able to mimic the efficiency and flexibility of BNNs more closely. Although there are a myriad of moral considerations with this progress, it will happen, and we will need to choose as humans how to treat a system that is smarter than us.


The study of neural networks, both biological and artificial, not only enriches our understanding of cognitive functions but also enhances the capabilities of computational models. It is crucial to recognize that the intelligence of these systems hinge on their ability to store and process this information. Humans leverage neuroplasticity to learn and encode new knowledge, whereas artificial neural networks use backpropagation to refine their learning processes. As these fields continue to evolve, the integration of biological infighting within neural networks is poised to reinforce innovation within neural networks. This fusion continues to blur the distinctions between biological and artificial intelligence.

3-D Representation of a series of biological neurons.

Explore a fascinating series of YouTube videos by Grant Sanderson, a Math and Computer Science graduate from Stanford, on YouTube where you can learn more about Neural Networks, Backpropagation, Transformers, and more.

Explore 3Blue1Brown on YouTube
Animated gif, showing a confocal microscopy Z-stack of medium spiny neurons in the striatum of a Gpr101-Cre:dtTomato mouse.
Simplified neural network example: The network is trained to associate a ringed pattern and star outline with a sea star, and a striped pattern and oval shape with a sea urchin. In this run, it correctly detects the sea star in the picture at left.
NN is attempting to make a prediction based on the image (data) that it has been provided. It forecasts that the answer to this question will be 2. Source: 3Blue1Brown
English: Forest of synthetic pyramidal dendrites grown using Cajal's laws of neuronal branching. PLoS Computational Biology Issue Image, Vol. 6(8) August 2010. PLoS Comput Biol 6(8): ev06.i08. doi:10.1371/image.pcbi.v06.i08
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