HUMAN BRAIN & AI : Statistical interconnection between Brain neurons and artificial neurons and its simulation

 Brain neuron network and its working :

A neural network in the brain is a group of interconnected units called neurons that send signals to one another. These neurons can be either biological cells or mathematical models. While individual neurons are simple, many of them together in a network can perform complex tasks.

A biological neural network is a population of biological neurons chemically connected to each other by synapses. Each neuron sends and receives electrochemical signals called action potentials to its connected neighbors. A neuron can serve an excitatory role, amplifying and propagating signals it receives, or an inhibitory role, suppressing signals instead.

It has been estimated that there are around 86 billion neurons in the brain. Each neuron is connected to another 1,000 neurons, creating an incredibly complex network of communication

. During development, the 100 trillion synapses in the human cortex form at a rate of an estimated 10,000 every 15 minutes.

Neural networks route signals through the brain along a linear pathway, analyzing and organizing different types of information within fractions of a second. These networks play a crucial role in how we process and understand the world around us.

Neurons, also known as nerve cells, are the basic working units of the brain and nervous system. They function as information messengers, using electrical impulses and chemical signals to transmit information between different regions of the brain and between the brain and the rest of the nervous system.

Neurons can be split into three parts:

1. Soma (cell body): This portion of the neuron receives information. It contains the cell’s nucleus.
2. Dendrites: These thin filaments carry information from other neurons to the soma. They are the “input” part of the cell.
3. Axon: This long projection carries information from the soma and sends it off to other cells. This is the “output” part of the cell. It normally ends with a number of synapses connecting to the dendrites of other neurons.

Neurons work by sending chemicals, called neurotransmitters, across a small area between the axon of one neuron and the dendrite of another. This tiny space that allows for information exchange is called a synapse. Neurons also allow the central nervous system (CNS) and the peripheral nervous system (PNS) to report information back to each other instantaneously and constantly.

If a neuron receives a large number of inputs from other neurons, these signals add up until they exceed a particular threshold. Once this threshold is exceeded, the neuron is triggered to send an impulse along its axon — this is called an action potential.

you know that 

1.Your brain is hyper-efficient, running on just 20 watts of power.

2.Signals in your neurons can travel at the same speed as Formula 1 cars (~100 m/s or 360 km/h)

3.Your brain has about 100 billion neurons – about as many stars as in the Milky Way galaxy.

Artificial Neural Network :(ANN)

An Artificial Neural Network (ANN) is a computational model inspired by the human brain and nervous system. It’s a sub-field of artificial intelligence that attempts to mimic the network of neurons that make up a human brain.

Here are some key components of an ANN:

1. Units: These are the artificial neurons in the network, also known as nodes.
2. Layers: Units are arranged in a series of layers, which together constitute the whole ANN. Typically, an ANN has an input layer, one or more hidden layers, and an output layer.
3. Weights: Each connection between units has weights that determine the influence of one unit on another.

The input layer receives data from the outside world which the neural network needs to analyze or learn about. This data then passes through the hidden layers that transform the input into something valuable for the output layer. Finally, the output layer provides a response to the input data.

As the data transfers from one unit to another, the neural network learns more about the data, which eventually results in an output from the output layer. The ANN is designed by programming computers to behave simply like interconnected brain cells.

The concept of ANNs comes from biological neurons found in animal brains, so they share a lot of similarities in structure and function. For example, dendrites from Biological Neural Network represent inputs in Artificial Neural Networks, cell nucleus represents Nodes, synapse represents Weights, and Axon represents Output.

example of complex  artificial neuron network

Biological neurons and artificial neurons share several similarities:

1. Processing and Transmitting Information: Both biological and artificial neurons are the basic building blocks that process and transmit information.

2. Parallel Processing: Biological neural networks process information in parallel, which is also true for artificial neural networks.

3. Learning: In biological neural networks, learning comes from past experiences which improve their performance level. This is also true of artificial neural networks.

4. Interconnections: The human brain has a biological neural network which has billions of interconnections. As the brain learns, these connections are either formed, changed, or removed, similar to how an artificial neural network adjusts its weights to account for a new training example.

5. Neurons and Synapses: In both biological and artificial neural networks, neurons are the basic building blocks that process and transmit information. Synapses are the points of connection between neurons, where information is transmitted.

6. Neural Pathways: In both biological and artificial neural networks, neural pathways are the connections between neurons that allow information to be transmitted throughout the network.

   

   Biological Neurons vs Artificial Neurons

   1. Structure: Biological neurons have multiple dendrites that receive input from multiple sources, and the axons transmit signals to other neurons. In contrast, artificial neurons are simplified and usually only have a single output

   2. Synapses: In biological neurons, synapses are the points of connection between neurons, where information is transmitted. The connections between neurons are more flexible, and the strength of the connections can be modified by a variety of factors, including learning and experience. In artificial neurons, the connections between neurons are usually fixed, and the strength of the connections is determined by a set of weights.

   3. Neural Pathways: In biological neurons, neural pathways are highly complex and diverse, and the connections between neurons can be modified by experience and learning. In artificial neurons, neural pathways are usually simpler and predetermined by the architecture of the network.

   4. Processing Speed: Biological neurons are slow in processing information, while artificial neurons process information at a faster pace.

   5. Memory: In an artificial neural network, the system’s unique functional memory is placed independently with the CPU. On the other hand, the distributed memory in the biological neural network is located within the neural interlinks.

   6. Learning: Like the human brain, artificial neural networks learn from experiences, generalize from examples, and may extract important information from noisy input.

   In summary, while artificial neurons are inspired by biological neurons, they are simplified versions designed for computational efficiency and ease of mathematical analysis.