The complexity of the human brain and its relationship with artificial intelligence: ANN, RNN, LSTM and GRU
The perfection of nature has been the source of inspiration for people for centuries. The idea of using this information in technology by studying the design and functions of organisms and systems found in nature actually dates back thousands of years. People made simple tools and structures by taking inspiration from nature in the past. With the development of science and technology in the last century, the study of natural systems and the transfer of this knowledge to technology have also accelerated. Therefore, it is not surprising that many of the greatest technologies we use today are inspired by systems in nature. The human body system has also inspired advances in the field of robotics.
One of the most interesting natural systems that scientists and engineers are trying to imitate is the human brain. The brain is a mysterious and complex organ that has been the subject of research and discovery for centuries. The human brain is formed through a network of synapses connected to each other by billions of neurons. It is due to this sophisticated neural network that the brain receives a large amount of information, stores it, learns from experience and makes difficult decisions.
One of the most interesting discoveries in the field of artificial intelligence in recent years is the discovery of neural networks. By creating a neural network, the function of the human brain can be imitated. This learning technique allows data to be processed through interconnected nodes or neurons, as in the human brain. Deep learning is a method that can completely change the artificial intelligence used in everyday life.
Artificial intelligence is a field of computer science that aims to create machines that can perform tasks that normally require human intelligence. One of the areas where artificial intelligence has achieved this is to mimic the structure and function of the human brain.
The human brain is one of the most complex systems ever discovered, with an estimated 86 billion neurons. Neurons are the basic building blocks of the nervous system and are responsible for controlling and coordinating all the functions of the body. These neurons communicate with each other through special connections and are what are called synapses, which allow them to spread and transmit information throughout the body. The number of neurons in the human brain is truly amazing, and this makes our brain an organ that can perform extraordinary cognitive and creative achievements. Despite our extensive knowledge of the human brain, there is still a lot to be discovered about this remarkable organ and its many secrets.
The average power of the human brain is estimated at about 20 watts (W). Google’s 2018 artificial neural network project aimed to simulate the one-second activity of the human brain. The project was implemented using Summit, the world’s largest supercomputer with 1.6 million processor cores. The project aimed to mimic the interactions between neurons in the human brain and designed an artificial neural network with 1.73 million nerve cells and 10.4 trillion synaptic connections. Google’s project has successfully simulated the one-second activity of the human brain, but this is only a small fraction of it, and a much larger structure is required for a full simulation of the human brain. This project is seen as an important step for artificial intelligence and brain research, as similar simulations can be used in other research on neural networks and in the treatment of neurological diseases.
Neural networks, which are the building blocks of artificial intelligence, are modeled similarly to the structure of the human brain. It is a type of machine learning process called deep learning and it uses interconnected nodes or neurons with a layered structure. These networks consist of a system in which artificial neurons are interconnected in layers. Like neurons in the brain, these artificial neurons can receive and process information from other neurons and transmit it to the next layer of the network.
In neural networks, data is fed to the input layer, which is processed by the first set of neurons in this layer. The output of this layer is then passed to the next layer of neurons and further processed there. This process continues until the final output layer that produces the desired result is reached. The connections between neurons vary in weight, which allows the network to learn from the data it processes.
Neural networks are also designed for learning from experience. This is accomplished by adjusting the weights of the connections between neurons based on the feedback the network receives. Over time, the network gets better at identifying and predicting patterns, similar to the way the human brain adapts and learns from experience.
The ability of neural networks to learn from experience is one of their most important advantages. In this way, connections between neurons can change in response to feedback from the network. As more data is processed, the network becomes more powerful at detecting patterns and making predictions. Neural networks are often used for tasks that require learning from a lot of data, such as speech and image recognition.
The ability to manage interactions between complex and nonlinear variables is another benefit of neural networks. In this way, even if these relationships are not explicitly defined to the system, the network can learn to simulate them. Because there are so many variables that affect a stock’s price, neural networks are very helpful in things like predicting stock values.
Although neural networks have many advantages, they also have some disadvantages. One of the main problems with neural networks is that computational training can be costly. This happens because the network has to analyze a lot of information and change the weights of the connections between neurons. Another challenge is the complexity of understanding neural networks. This creates the challenge as the network does not explicitly specify how it arrives at its output. Neural networks and the many applications they could potentially provide have a bright future, although there are still many challenges to overcome.
The similarities between neural networks and human brain neurons are many, which makes neural networks powerful in the field of artificial intelligence. Here are some of their key similarities:
Connected nodes: Like the human brain, neural networks are made up of interconnected nodes or neurons. These nodes are connected via a network of weighted connections to process and analyze data.
Neuron layers: In both neural networks and the human brain, neurons are divided into layers. These layers process information sequentially, and each layer processes information based on the output of the previous layer.
Learning from experience: Neural networks are designed to learn from experience, like the human brain. This means that the network can adjust the weights of connections between neurons based on the feedback it receives. Over time, the network gets better at identifying patterns and making predictions.
Nonlinear relationships: Neural networks and the human brain can model complex and nonlinear relationships between variables. This means that the network can learn to model these relationships, even if these relationships are not explicitly programmed into the system.
Activation functions: In both neural networks and the human brain, neurons use an activation function to determine their output. This activation function takes the sum of the signals entering the neuron and produces an output according to this sum.
Error correction: When a neural network makes an error, it uses a process called error correction to adjust the weights of the connections between neurons. This is similar to how the human brain adjusts its behavior by learning from mistakes.
In general, the similarities between neural networks and human brain neurons are what make neural networks powerful in the field of artificial intelligence. By mimicking the way the human brain processes information, neural networks learn from experience, model complex relationships, and make predictions with a high degree of accuracy. As technology evolves, we will likely see more applications of neural networks in fields such as healthcare, finance, and robotics.
Now it’s time to mention about the Human Brain Project, which tries to simulate the human brain.
Human Brain Project:
In recent years, scientists and researchers have been working on a project that aims to simulate the human brain on a computer. Many studies are being carried out to solve the mysterious structure of the brain, one of the most remarkable is the Human Brain Project. This project is a large-scale collaborative effort involving scientists, engineers and computer experts from around the world. This project is seen as an important step in our journey to understand one of the most complex and interesting organs in the human body.
To achieve this goal, researchers are working to map the structure and functioning of the human brain using advanced computer simulations and cutting-edge imaging techniques. They are also working to develop new computer algorithms and software tools that can help simulate the complex behavior of neurons and neural networks.
The Human Brain Project aims to create a detailed digital model that can be used to better understand how the human brain works, develop new treatments for brain-related diseases, and even create more advanced artificial intelligence systems. The Human Brain Project also focuses on developing new neurotechnologies such as brain-machine interfaces. These technologies have the potential to be used to improve the quality of life for millions of people around the world suffering from conditions such as Parkinson’s, epilepsy and depression.
Now I would like to tell you about RNN, LSTM and GRU, which you will hear frequently in the field of deep learning.
RNN, LSTM and GRU:
Artificial Neural Networks (ANNs) are widely used for a variety of tasks, but the problem of forgetting previous inputs is a major problem when dealing with sequential data. Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) have been developed to tackle this problem. While RNNs process sequential data using a loop structure to build a “memory” of previous inputs, LSTMs and GRUs have an additional mechanism called a “memory cell” that has the function of forgetting or remembering information selectively.
RNNs, LSTMs, and GRUs are useful in a variety of fields, such as natural language processing, speech recognition, and more, when used correctly, as they can effectively process sequential data and remember important information from previous inputs.
RNNs, LSTM’s and GRU’s networks are three popular types of neural networks designed to process sequential data. What distinguishes these networks from other types of neural networks is their ability to remember past information and use it to make predictions or classifications about future inputs. This is similar to the way neurons in the human brain work, causing RNNs and LSTMs to be more similar to neurons compared to other types of neural networks.
One of the key features of RNNs is their ability to handle arrays of variable length. Important for many applications where inputs are not fixed, such as natural language processing or speech recognition. The basic idea of RNNs is to use a hidden state that is always updated in step, which informs the network about past entries. The hidden state works like the network’s memory, allowing it to keep track of past information and make predictions about future inputs.
LSTMs take this idea further, introducing a more complex memory cell that can store information over longer periods of time. LSTMs are particularly effective in applications where long-term dependencies are important, such as speech recognition or machine translation. The memory cell in an LSTM is designed to be selectively read, written, or erased depending on the input and the current state of the network. This allows LSTM to selectively remember or forget information and is a vital feature for many real-world applications.
The ability of RNNs and LSTMs to remember past information and use it to make predictions about future input is similar to the way neurons in the human brain work. Neurons in the brain form complex networks capable of storing and processing information over long periods of time. The ability to recall past information is considered an important feature of human intelligence, and this is one of the reasons why RNNs and LSTMs are more like neurons of the brain than other types.
Like other RNNs, GRUs (Gated Recurrent Units) are designed to handle sequential data input such as sentences or time series. However, unlike traditional RNNs, GRUs use gate mechanisms to selectively update and forget information at each time step, allowing the network to selectively store important information and discard unnecessary information.
As a result, RNNs, LSTMs, and GRUs are more similar to neurons in the human brain due to their ability to remember past information and make predictions or classifications about future inputs. This makes it suitable for many real-world applications such as natural language processing, speech recognition, and machine translation. As research in the field of neural networks progresses, RNNs, LSTMs and GRUs will continue to play an important role in the development of intelligent systems that can process complex sequential data.
In this article, we talked about the mysterious relationship between artificial neural networks that try to imitate the human brain, and then we touched on ANN, RNN, LSTM and GRU, which emerged with the forgetting problem. I will be here soon with my new articles on Deep Learning, NLP and Computer Vision.
References:
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Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306.
