Chapter 14
Neural Networks and Brains
. . . of all the fathomless questions that confront and confuse
men, the most baffling is the human mind.”
—Clarence Darrow
Summary All cells in life systems are capable of communication between cells
by the use of signaling molecules such as hormones. The speed of this form of
communication is limited by diffusion rates. Multicellular animals early in evolution
took advantage of the much faster internal electric signaling to coordinate their
activities. Neurons are the specialized cells for this purpose. For quick analysis
of inputs, using short connections, a concentrated network of neurons became
ganglions, and then brains. A brain consists of a set of interconnected neurons,
each acting to process a set of inputs to produce one output. As such, a set of
interconnected electronic gates is capable of acting as a brain (Turing’s lemma),
and is the basis of modern-day computers. We can envision even more powerful
computers using quantum coherent states, but our own brain seems to be acting
classically, with ‘free-will’ coming from the complexity in the brain and the almost
chaotic nature of its inputs.
14.1 Neural Networks as Logic Systems
A “finite classical computer” is a device which takes a given finite input, acts on that
input with Boolean logic using internal memory (which includes programs), and
produces a finite output after running for a number N of clock cycles. That output
is a result based solely on the input and memory. If the device has not concluded its
analysis of the input in N cycles, the result is a failure signal (also called a “halt”
state). The output must have a unique correspondence to the input and memory,
although many inputs may produce the same output. Often, the output is made to be
an “answer” to the input “question” or proposition.
Alan Turing has argued that a finite classical computer can be constructed that
is capable of reproducing, for a given input, the output of any other given finite
© Springer Nature Switzerland AG 2020
W. C. Parke, Biophysics, https://doi.org/10.1007/978-3-030-44146-3_14
577
Neural Networks and Brains
. . . of all the fathomless questions that confront and confuse
men, the most baffling is the human mind.”
—Clarence Darrow
Summary All cells in life systems are capable of communication between cells
by the use of signaling molecules such as hormones. The speed of this form of
communication is limited by diffusion rates. Multicellular animals early in evolution
took advantage of the much faster internal electric signaling to coordinate their
activities. Neurons are the specialized cells for this purpose. For quick analysis
of inputs, using short connections, a concentrated network of neurons became
ganglions, and then brains. A brain consists of a set of interconnected neurons,
each acting to process a set of inputs to produce one output. As such, a set of
interconnected electronic gates is capable of acting as a brain (Turing’s lemma),
and is the basis of modern-day computers. We can envision even more powerful
computers using quantum coherent states, but our own brain seems to be acting
classically, with ‘free-will’ coming from the complexity in the brain and the almost
chaotic nature of its inputs.
14.1 Neural Networks as Logic Systems
A “finite classical computer” is a device which takes a given finite input, acts on that
input with Boolean logic using internal memory (which includes programs), and
produces a finite output after running for a number N of clock cycles. That output
is a result based solely on the input and memory. If the device has not concluded its
analysis of the input in N cycles, the result is a failure signal (also called a “halt”
state). The output must have a unique correspondence to the input and memory,
although many inputs may produce the same output. Often, the output is made to be
an “answer” to the input “question” or proposition.
Alan Turing has argued that a finite classical computer can be constructed that
is capable of reproducing, for a given input, the output of any other given finite
© Springer Nature Switzerland AG 2020
W. C. Parke, Biophysics, https://doi.org/10.1007/978-3-030-44146-3_14
577