Neural Cells as Harmony Detectors
The issues of harmony have notably low profile in the current AI research. Meanwhile, when it comes to brain architecture, harmony plays the central role. As we hope to demonstrate in this essay, neural cells act primarily as local harmony detectors. On a less local scale, the consensus is that perceptions correspond to certain stable periodic patterns of neuron firing. A harmony-based architecture would probably be more successful than current AI paradigms.
It would help here to recall the basics of neuron firing theory. In the rest state neuron membrane typically has electrochemical polarization potential of 70 millivolts. When the firing impulse comes to the neuron from another neuron via the corresponding synapse (the site of their connection), this polarization potential changes, typically by 1-2 millivolts or less. If the polarization potential decreases beyond the threshold of approximately 60 or 55 millivolts, the neuron fires, otherwise the polarization potential tends to rapidly relax to the original rest level of 70 millivolts.
Hence, when the reception of an impulse via a synapse decreases the membrane polarization potential of the receiving neuron, we call this synaptic connection excitatory, because the decrease of the polarization potential makes it easier to fire for our neuron. Otherwise, the synaptic connection is called inhibitory. Because the reception of an impulse changes the polarization potential by at most 2 millivolts and because the polarization potential tends to rapidly relax back to 70 millivolts, the neuron can fire only if it receives several (from 4 to more than a dozen) impulses via excitatory connections simultaneously or in a very quick succession.
Hence the neuron works as a detector of several excitatory impulses coming almost simultaneously. So we can say that the neuron detects the harmony between its incoming impulses.
Now we shall turn to learning mechanisms in the brain, and observe that the local learning (on the level of one neuron) is directed towards detecting this harmony even better. As we have noted, the reception of an impulse changes the polarization potential usually by 2 millivolts or less. The actual value of this change is usually called synaptic strength. This value is not constant, but changes with time. This ability of synaptic strength to change is the key mechanism of neural learning and is called synaptic plasticity.
The most typical rule of synaptic plasticity for excitatory connection works approximately as follows. If a neuron fires shortly after receiving an excitatory impulse (i.
In the beginning phases of muscle contraction, a “cocked” motor neuron in the spinal cord is activated to form a neuromuscular junction with each muscle fiber when it begins branching out to each cell. An action potential is passed down the nerve, releasing calcium, which simultaneously stimulates the release of acetylcholine onto the sarcolemma. As long as calcium and ATP are present, the contraction will continue. Acetylcholine then initiates the resting potential’s change under the motor end plate, stimulates the action potential, and passes along both directions on the surface of the muscle fiber. Sodium ions rush into the cell through the open channels to depolarize the sarcolemma. The depolarization spreads. The potassium channels open while the sodium channels close off, which repolarizes the entire cell. The action potential is dispersed throughout the cell through the transverse tubule, causing the sarcoplasmic reticulum to release
The ultimate goal for a system of visual perception is representing visual scenes. It is generally assumed that this requires an initial ‘break-down’ of complex visual stimuli into some kind of “discrete subunits” (De Valois & De Valois, 1980, p.316) which can then be passed on and further processed by the brain. The task thus arises of identifying these subunits as well as the means by which the visual system interprets and processes sensory input. An approach to visual scene analysis that prevailed for many years was that of individual cortical cells being ‘feature detectors’ with particular response-criteria. Though not self-proclaimed, Hubel and Wiesel’s theory of a hierarchical visual system employs a form of such feature detectors. I will here discuss: the origins of the feature detection theory; Hubel and Wiesel’s hierarchical theory of visual perception; criticism of the hierarchical nature of the theory; an alternative theory of receptive-field cells as spatial frequency detectors; and the possibility of reconciling these two theories with reference to parallel processing.
Briefly explain the process of neurotransmission. Neurotransmission starts with the neuron, the most important part of the central nervous system. A neuron contains a cell body, axon, and dendrites. When a neuron receives an electrical impulse, the impulse travels away from the cell body down the axon. The axon breaks off into axon terminals. At the axon terminals, the electrical impulse creates a neurotransmitter. The neurotransmitter is released into the synapse, a space between two neurons. If the neurotransmitter tries to stimulate a response of another neuron, it is an excitatory neurotransmitter. If the neurotransmitter does not stimulate a response of another neuron it is an inhibitory neurotransmitter. If a response is generated, the second neuron or postsynaptic neuron will receive an action potential at the site of the dendrite and the communication process will continue on. If a response is not generated, neurotransmitters left in the synapse will be absorbed by the first neuron or presynaptic neuron, a process known as reuptake. Neurotransmitters control our body functions, emotions, and
The presynaptic terminal stores high concentrations of neurotransmitter molecules in vesicles, which are tiny nearly spherical packets. These molecules are then released by depolarization. Depolarization opens voltage-dependent calcium gates in the presynaptic terminal. After calcium enters the terminal, it causes exocytosis, which is the burst of release of neurotransmitters from the presynaptic neuron. After its release from the presynaptic cell, the neurotransmitter diffuses across the synaptic cleft to the postsynaptic membrane, where it attaches to the receptor.
Neurons dispatch signals to other cells through thin fibers called axons, that cause chemicals acknowledged as neurotransmitters to be released at junctions identified as synapses. A synapse gives a command to the cell and the entire communication process typically takes only a fraction of a millisecond.
The brain is part of the central nervous system, which consists of neurons and glia. Neurons which are the excitable nerve cells of the nervous system that conduct electrical impulses, or signals, that serve as communication between the brain, sensory receptors, muscles, and spinal cord. In order to achieve rapid communication over a long distance, neurons have developed a special ability for sending electrical signals, called action potentials, along axons. The way in which the cell body of a neuron communicates with its own terminals via the axon is called conduction. In order for conduction to occur, an action potential which is an electrical signal that occurs in a neuron due to ions moving across the neuronal membrane which results in depolarization of a neuron, is to be generated near the cell body area of the axon. Wh...
The occurrence of action potential is a very short process. When action potential occurs in the neuron the sodium channels open along the axon and sodium comes in. Because the sodium is positive it make the inside of the axon positive. When both the inside and outside are comparative in charge the sodium storms rushing in and starts the depolarization of the action potential. After this happens the sodium channels begin to close and the potassium channels begin to ...
Mirror neurons have been one of the most exciting neurological discoveries in recent years. Some researchers have even gone as far as comparing the discovery of mirror neurons to DNA. Mirror neurons may be analogous to other human sensory systems and some believe that mirror neurons represent their own unique sensory system. Mirror neurons fire when a person or animal performs certain activities as well as when they watch another perform the same activity (Winerman, 2005). Basically, they allow animals and humans to imitate and possibly even learn from others. While the original studies were conducted in monkeys, recent research has extended the theory to humans and other abilities outside of basic motor movements. In this paper, research on mirror neurons in humans, language, and autism will be summarized. In addition, the limitations on this work will be discussed.
Ma begins his paper by referring to the argument made by University of Southern California professor, and well established neuroscientist, Antonio Damasio. This argument states “that feeling and emotions expressed in art and music play a central role in high-level cognitive reasoning,” (Ma 258). Ma elaborates on this by mentioning how new advances in neurobiology have made it more clear that the human brain uses dual neural pathways for thinking processes, one for critical thinking and one for empathetic thinking (Ma 258). By doing this, Ma is able to show his audience that his ideas are supported by highly respected intellectuals in the complex field of neuroscience. Ma continues to add logic to his argument through his discussion of equilibrium. Ma piggybacks on the widely-accepted idea that equilibrium is the key factor for the survival of all forms of life. He states that, “Evolution is the balance between stability and the changes necessary to cope with new challenges in the environment,” (Ma 259). Ma implies that this “balance” is necessary in all aspects of life, including cognitive reasoning. This argument is very perspicacious in the appeal that it incorporates such widely-accepted
...ical impulse, repeating the mechanism described above. The neurons received signal, they crumble up the information passed it down until they get to the last one.
The brain contains millions of tiny nerve cells, known as neurons and these neurons are joined and connected to a million more neurons. When these neurons form a series of interconnected neuron...
Therefore, the human organism although made of multiple “swarms,” is different from other organisms or programs because of the capacity to make conclusions and make illogical and “unnatural” decisions not based on the rudimentary interworking of the brain cells. Therefore although multi agent distributed parallel processing programs, can produce emergent behavior that could possibly be equated to our illogical decisions and creativity, human behavior, although somewhat emergent, stems from a deeper consciousness not generated by the interactions of brain
The traditional notion that seeks to compare human minds, with all its intricacies and biochemical functions, to that of artificially programmed digital computers, is self-defeating and it should be discredited in dialogs regarding the theory of artificial intelligence. This traditional notion is akin to comparing, in crude terms, cars and aeroplanes or ice cream and cream cheese. Human mental states are caused by various behaviours of elements in the brain, and these behaviours in are adjudged by the biochemical composition of our brains, which are responsible for our thoughts and functions. When we discuss mental states of systems it is important to distinguish between human brains and that of any natural or artificial organisms which is said to have central processing systems (i.e. brains of chimpanzees, microchips etc.). Although various similarities may exist between those systems in terms of functions and behaviourism, the intrinsic intentionality within those systems differ extensively. Although it may not be possible to prove that whether or not mental states exist at all in systems other than our own, in this paper I will strive to present arguments that a machine that computes and responds to inputs does indeed have a state of mind, but one that does not necessarily result in a form of mentality. This paper will discuss how the states and intentionality of digital computers are different from the states of human brains and yet they are indeed states of a mind resulting from various functions in their central processing systems.
With each of our senses (sight, smell, touch, taste, and hear), information is transmitted to the brain. Psychologists find it problematic to explain the processes in which the physical energy that is received by the sense organs can form the foundation of perceptual experience. Perception is not a direct mirroring of stimulus, but a compound messy pattern dependent on the simultaneous activity of neurons. Sensory inputs are somehow converted into perceptions of laptops, music, flowers, food, and cars; into sights, sounds, smells, taste ...
Learning is defined as a “process of change that occurs as a result of an individual’s experience” (Mazure, 2006). Researchers assume that the process of learning follows certain general principles, which were developed, into the general process learning theories. These include operant conditioning and classical conditioning which has been put forward by leading psychologists like Pavlov, B.F.Skinner and Thorndike. However, in learning, operant and classical conditoning are opposed by biological constraints that state that there are limitations to the theories. Some of these biological constraints on learning will be discussed below.