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Artificial Neural Network vs. Biological Neural Network: What's the Difference?

Edited by Aimie Carlson || By Janet White || Published on February 4, 2024
Artificial neural networks (ANNs) are computing systems inspired by biological neural networks, which are complex networks of neurons in living organisms.

Key Differences

Artificial neural networks (ANNs) are computational models inspired by the human brain's structure, designed to simulate the way biological neural networks process information. ANNs consist of layers of interconnected nodes (or artificial neurons) which process data through weights and activation functions. In contrast, biological neural networks are the actual networks of neurons in living organisms, comprising cells (neurons) that process and transmit information through electrochemical signals.
ANNs are used in machine learning and artificial intelligence to perform tasks like pattern recognition, decision making, and prediction, based on their ability to learn from data. They are capable of adjusting their processing by changing the weights of connections, which is analogous to learning. On the other hand, biological neural networks naturally perform these functions as part of an organism's nervous system, enabling actions, thoughts, and responses to stimuli.
The structure of an ANN typically involves layers of nodes, including input, hidden, and output layers, and is designed by programmers. Its function and efficiency depend on algorithms and computing power. In contrast, a biological neural network is an intricate, naturally occurring structure, evolved over millions of years, and is far more complex than its artificial counterpart. It processes information using a vast network of interconnected neurons, each capable of performing a variety of functions.
ANNs are limited by the current state of technology and understanding of neural processing, and they work within the confines of pre-defined algorithms and computational resources. Whereas, biological neural networks operate on a scale and with a level of complexity that artificial systems have yet to achieve, including the ability to grow, adapt, and repair themselves.
In terms of learning and adaptation, ANNs require training with large datasets to accurately perform tasks and cannot independently acquire new knowledge without retraining. Biological neural networks, however, continuously learn and adapt throughout an organism's life, processing a wide range of sensory inputs and experiences.

Comparison Chart


Interconnected artificial nodes or neurons
Network of biological neurons and synapses

Learning Process

Requires training with data, algorithms
Continuous, natural learning and adaptation

Structure and Design

Designed and programmed by humans
Naturally evolved, highly complex structure

Functionality Limit

Limited by algorithms, computing power
Extremely versatile, capable of growth and repair


Machine learning, data analysis, AI applications
Enables cognitive functions, sensory processing


Needs reprogramming for new tasks or changes
Naturally adapts to new stimuli, experiences

Processing Method

Based on mathematical functions, weights
Electrochemical signals, synaptic plasticity

Artificial Neural Network and Biological Neural Network Definitions

Artificial Neural Network

A network of artificial neurons for machine learning tasks.
The ANN improved its accuracy over time through deep learning.

Biological Neural Network

The basis of cognitive functions and sensory processing.
Memory and learning are functions of biological neural networks.

Artificial Neural Network

A computational model mimicking the brain's neural structure.
We used an artificial neural network for image recognition in our project.

Biological Neural Network

Cells transmitting information through electrochemical signals.
Biological neural networks enable us to process sensory input.

Artificial Neural Network

A tool for pattern recognition and predictive analysis.
ANNs are effective in speech recognition software.

Biological Neural Network

Networks in the brain and nervous system for information processing.
Biological neural networks are responsible for our responses to stimuli.

Artificial Neural Network

A structure in computing for simulating human cognition.
Our artificial neural network mimics cognitive functions for problem-solving.

Biological Neural Network

A complex network of neurons in living organisms.
The human brain is an example of a biological neural network.

Artificial Neural Network

A system in AI that learns from data to make decisions.
The artificial neural network can predict market trends from historical data.

Biological Neural Network

A naturally evolved structure for thinking and reacting.
Biological neural networks have evolved to adapt to complex environments.


Can biological neural networks grow and adapt?

Yes, they continuously evolve, grow, and adapt throughout an organism's life.

How do artificial neural networks learn?

Through algorithms and training with large datasets.

What tasks are artificial neural networks used for?

Pattern recognition, decision making, and prediction in AI applications.

Are biological neural networks involved in learning and memory?

Yes, they are crucial for learning, memory, and cognitive functions.

What is an artificial neural network?

A computer-based model designed to simulate the way biological neural networks process information.

How do biological neural networks process information?

Through electrochemical signals and synaptic interactions.

What limits the capabilities of artificial neural networks?

Current technology, computational resources, and algorithmic design.

Do artificial neural networks require retraining for new tasks?

Yes, they generally need reprogramming or retraining for new tasks.

What is a biological neural network?

The network of neurons and synapses in living organisms that process and transmit information.

Are artificial neural networks as complex as biological ones?

No, biological neural networks are far more complex and capable.

Can biological neural networks repair themselves?

To some extent, they have the ability to repair and adapt.

How do biological neural networks contribute to sensory processing?

They analyze and respond to various sensory inputs.

What makes artificial neural networks useful in AI?

Their ability to learn from data and improve decision-making processes.

Do biological neural networks use electrical signals?

Yes, they use electrochemical signals for communication and processing.

How are artificial neural networks structured?

They are composed of layers of interconnected nodes or neurons.

Can artificial neural networks operate without algorithms?

No, they function based on pre-defined algorithms.

Are biological neural networks responsible for human behavior?

Yes, they play a key role in determining behavior and responses.

What distinguishes biological neural networks in nature?

Their natural evolution, complexity, and adaptability to environments.

Can artificial neural networks mimic human emotions?

They can simulate aspects of emotional processing but not actual emotions.

What is the role of neurons in artificial neural networks?

They are computational units that process and pass on data.
About Author
Written by
Janet White
Janet White has been an esteemed writer and blogger for Difference Wiki. Holding a Master's degree in Science and Medical Journalism from the prestigious Boston University, she has consistently demonstrated her expertise and passion for her field. When she's not immersed in her work, Janet relishes her time exercising, delving into a good book, and cherishing moments with friends and family.
Edited by
Aimie Carlson
Aimie Carlson, holding a master's degree in English literature, is a fervent English language enthusiast. She lends her writing talents to Difference Wiki, a prominent website that specializes in comparisons, offering readers insightful analyses that both captivate and inform.

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