RE: Is there such a thing as choice ?
November 16, 2010 at 11:05 pm
(This post was last modified: November 17, 2010 at 12:33 am by lrh9.)
According to modern neuroscience, the brain is a network of relatively simple neurons acting in parallel. A neuron is a unit consisting of a cell body called a soma, a number of fibers called dendrites, and a single long fiber called an axon. The dendrites branch out in a network around the soma, and the axon stretches out to the dendrites and somas of other neurons. Where one neuron's axon meets another neuron's dendrites is a connection called a synapse.
Signals propagate from one neuron to another by complex electrochemical reactions. Synapses release chemicals that cause a change in a soma's electrical potential. When the soma's potential reaches its threshold, an electrical impulse is sent via its axon. The pulse reaches other synapses, changing their potential. A synapse's potential is a "weight" that determines the magnitude of the changes in a soma's electrical potential.
Another feature of neural networks is their plasticity. In response to an impulse pattern, neurons exhibit long-term changes in the strength of their connections. Neurons can also form new connections with other neurons. Entire collections of neurons can migrate from one place to another.
Our brain is a highly complex, nonlinear, and parallel information-processing system. Information is stored and processed in our neural network simultaneously throughout the whole network. Due to the plasticity of our brain, connections between neurons leading to the "right answer" are strengthened while those leading to the "wrong answer" weaken. Neural networks learn through experience.
Whether a soma activates or not is a branch. Just like whether a bit of data in a computer is less than, equal to, or greater than another bit of data is a branch. The result will be in that range. Some might call that a choice. Does will play a role in the result? Is will deterministic or non-deterministic? Those are the real questions.
Signals propagate from one neuron to another by complex electrochemical reactions. Synapses release chemicals that cause a change in a soma's electrical potential. When the soma's potential reaches its threshold, an electrical impulse is sent via its axon. The pulse reaches other synapses, changing their potential. A synapse's potential is a "weight" that determines the magnitude of the changes in a soma's electrical potential.
Another feature of neural networks is their plasticity. In response to an impulse pattern, neurons exhibit long-term changes in the strength of their connections. Neurons can also form new connections with other neurons. Entire collections of neurons can migrate from one place to another.
Our brain is a highly complex, nonlinear, and parallel information-processing system. Information is stored and processed in our neural network simultaneously throughout the whole network. Due to the plasticity of our brain, connections between neurons leading to the "right answer" are strengthened while those leading to the "wrong answer" weaken. Neural networks learn through experience.
Whether a soma activates or not is a branch. Just like whether a bit of data in a computer is less than, equal to, or greater than another bit of data is a branch. The result will be in that range. Some might call that a choice. Does will play a role in the result? Is will deterministic or non-deterministic? Those are the real questions.
