- Biological Neural Networks And Mechanical Neural Networks List
- Biological Neural Networks And Mechanical Neural Networks Diagram
Your brain is a neural net, which is a class of computingarchitectures. Neural nets can be made out of neurons along withsupporting brain cells, or out of electronics, or with chemistry,or with levers and cogs like an ancient adding machine, it doesn'tmatter, it's just a type of computational hardwarearchitecture.For the sake of round numbers, at the tone, your brain containsexactly one trillion living cells. Oops, and declining. You can dothe math. Some of these are neurons, and some are glilal cells andother support cells. You might find it interesting that the supportcells outnumber the neurons by two or three to one, and that theydon't just 'support', but they are essential to the computation,the back-propogation network, for you geeks.These trillion cells do not operate one at a time in sequence.There are cycles and pipelines, but all of the cells are powered upand doing their thing at the same time, all one trillion of them.
Biological Networks. Biological neural systems are heterogeneous, in that there are many different types of cells with different characteristics. Biological systems are also characterized by macroscopic order, but nearly random interconnection on the microscopic layer.
Amillion million. There's a lot of you in there.But I'm just one thing, you cry. I think one thought afteranother, sequentially. Some kind of secondary consciousness,subconscious, hunches, intuition, esp, sure, any thing is OK aslong as I'm only one me.Wrong. Consciousness is a simple brain trick. Don't get hung upon it, I'll explain it later.You've got this network of neurons and other brain cells, I canprove it, I have an electron microscope and a very sharp knife.(Pause) I'm going to ignore the distinction among cell types andjust go with the one trillion number. These trillion cells arearranged in a small number of layers, maybe seven or thirteen,compared to a trillion it's peanuts.
The this flat sheet is foldedand refolded, stretched and bent, and crosswired a bit, but themacroscopic, gross, structure is also a secondary part of thestory. It is still this, call it ten-layer, net that does the heavylifting.Somebody has taken a nice flat neural network, with ten layersof one hundred billion cells each, stomped it, stretched it,crumpled it up, and stuffed in into your head. More or less.Biology is messy, don't think of this as being crisp and neat.Everything is fuzzy. Reward diffusion. Advanced topic.Simplification follows.Think of this ten layer mat as having the outside world come infrom the top. Part of the top layer is your retina, part is thenerve cells attached to those feelers inside your inner ear thatlet you hear, part to the olfactory receptors in your nose, and soon.
The outside world stimulates the top layer through yoursenses.Cells in the second layer each connect to thousands of the cellsin the top layer. A cell in this layer might connect to a row ofcells in the retina in order to detect an edge, a line, an sharpchange in the image. A second layer cell might connect to a set oftop layer cells that let it recognize a C sharp note, orsomething.The hundred billion cells in the next layer down each connect tothousands of cells in the second layer. They might look at multipleedges to find a patch of uniform color, for example.And then another layer might put patches together into objects,and another layer might put images from the two eyes into a threedimensional model of some sort.And then another layer would connect each of its billions ofcells to thousands of cells above, including the three D model, andsome of these cells would make deductions about the model. Whatdoes it mean, what will happen next, what are all the things itconnects to, how does Warren Buffet relate to it, am I going totrip over it. All of these connectons, millions of them, being madeat once.(Which is why I get going and spout nonsense and foam at themouth all the time. I encourage the pesky connections to grow.
Ipay attention to the kids in the back row of the classroom with thecrazy ideas and the ludicrous connections. It makes me laugh, andthat encourages them more. If you want to be crazy too, try it.
Butstart demanding a psychiatrist early, there's a waiting list.)Looking up from the bottom, you might find a cell that controlsone fibre of one muscle. It can say go, stop, emergency power, andlike that to that one fibre.Above it is a layer that can send commands to all the fibres ina muscle, such as a quadricep. Kata hubung menurut nik safiah karim. That is, the bottom layer listensfor the signal, the information here is flowing down. Above that isa layer that does a particular action, such as a snap kick, and alayer above that decides who to kick, and so on.Don't take my breakdown of what's in what layer literally. It'snot designed by humans. You can argue about how it came about, butthat much is clear, it doesn't have a software architecture that isintended for people to think about.
It wasn't built with theintention of having well-defined layers with well-defined functionsused consistently in a logical structure. There is nohuman-comprehensible description of the particular computation anybrain does, except that they've done it for cockroach brains, theythink. I doubt it.That's still how it works, an neural net in layers, a fewhundred billion nerve cells with thousands of connections each.Cascade computation. It's just that some of the concepts thatparticular cells recognize have no names in any human language.Some of the relationships you compute are beyond description, sothey just come to you as a vague feeling that there is someconnection. Some things you know but can't say.Learning. The damn things do not come prewired, outside of thegross structure.
They can't, because you've got the trillion cellsand the thousands of connections, and only twenty-five thousandgenes in your DNA. (plus support genes and 'junk', don't get mestarted). Not enough DNA to specify the structure. The blueprintsfor the Taj Mahal written in crayon on a postit note. Can't bedone.The knowledge is in the connections, the strength of theconnections, and how well the neurons like the connections, and howwell the support cells like the neurons.Synapses, neurotransmitters, dendrite growth, weightingfunctions, pulse modulation, ion wave signal propogation,back-propogation, google it. That's learning. There's instincts,and emotions, and other stuff.
Perception triggers emotion, emotiontriggers action, the sets that are hardwired are called instinct.Food triggers hunger, hunger triggers eating. Two instincts, oneperception, one emotion, one action. In most cases, other emotionsare also considered, and may balance the immediate response.The emotional computer is relatively built in and only a modestupgrade of the reptile version, except that primates devote animmense portion of general purpose neurons to analyzing theirrelationships with other primates of the same species.
Peckingorder, turf, he said, she said, who's doing what with who, and howdoes everyone feel about it, and which of my relationships would bechanged if I did so and so about it.Some people say that those very computations are why theprimates developed such big brains in the first place. Important ina cooperative society.
Worth allocating the brain cells.Now, back to the three D model I'm trying to implant in yourbrain. Your brain isn't a model railroad, it's a coffee filter.(Pause). The grains of coffee are the brain cells, a small numberof layers, the hot water coming in the top is data from the outsideworld, sights and sounds, and the stuff dripping out the bottomgoes to your muscles and glands to affect the outside world, oryour body.Now there are only a few, or a few dozen, but not many layers ofgrains, and you can only do so much computation with it. Sometimesthe coffee comes out too weak. So what do you do? Pour it back intothe top and run it through, over and over, until it's dark enough.Can come out bitter.
If all you care about is is the color, you canrun it through until you reach diminishing returns. You can getsome strange brews that way, though.Some of the gross structure is about that, some of it is just inindividual connections that run backward through the filter.Biology is messy, and the layers and connections just growed undersome general policies and guidance from the DNA. One way oranother, it recirculates.Instead of sending the command to your leg to execute the snapkick, you loop it around to the input as if you saw someone else dothe kick that you intended. Now you can use a dozen layers or twoto figure out what's going to happen next. Laws of physics,anantomy, how he might respond, and what you could do about that,etc.
Then you put that back in the top.Near the top of the filter, there is a double input, 'needskicking', and 'you'll fall on your butt if you try a snap kick'.Before there was just the one input, 'needs kicking'. This time, asit flows through the filter, it might trigger the spin kick, whichmight pass muster and get through to the muscles.Same thing with words. Chop it off short of saying it, considerit, how would you react to hearing it, try again, again, goodenough, spit it out.Same with smells. Lilac, no minty lilac, no minty lilac withbergamon, it's beebalm.
Like at Grandma's house twenty years ago,that summer that the.Same with maps, circle and arrow diagrams, pictures, melodies,quaternions, and things there are no names for in any humanlanguage.Lots of bulletin boards, for every type of media. Not orderly,messy. Each bulletin board partially visible to many parts of thebrain, connected strongly or weakly to many things. Goal directed referees. 'Attention' is the collectiveaction of all of these referees. They know what is likely to yieldgood results. Judgement and forebrain, advanced lesson.So that's it, consciousness and awareness, and all that.
No bigdeal, all mammals and birds do it, possibly reptiles too. Theinternal monolog, and the movie running in your head - just what'srunning on the buiietin boards, to give all the cells in your brainsomething to focus on.Multiplexing, advanced topic. Same neuron participates in manycomputations, accoring to need, probably communicated through thesupport cells. Attention, activitly level on boards.I know this is pretty dry, and not relevant to most people.Thinking is a rare hobby, but I'm almost done, and will go back tothinking with the other head soon.In neural nets, a whole weighty topic in computer science youcould get PhDs for, not that there's anything wrong with that,learning happens through reinforcement. If there is a good outcomefrom what the net computed, then the connections involved in thecomputation get stronger.
Devils in the details, but the principleis that simple. That's how we learn.When you learn a new concepts, discover a new relationship, finda new connection, your brain decides if it's new and potentiallyuseful. If so, it sends back a message that says 'good trick, makemore like it' to the general area of the brain cells that wereinvolved. Biology is messy. And so the brain makes more tricks likeit, recruiting nearby cells, extending connections, and maybe yousay 'still like it, make some more'.
Some of the new tricks you candescribe in words, some you can't, some 'you' aren't even aware of.Advanced reading, Roger Sperry. No matter, you can tell if you likethem and keep running the cycle until diminishing returns sets in.People vary in how long they will run the cycle.(This cycle can be measured on an EEG.
It is called the Zappacycle. You're familiar with the frequency or rhythm: ha, ha, ha,clap, clap, clap.
I don't know why it's called the Zappa cycle, itseems like a funny name for it.).
From 'Texture of the of Man and the '. The figure illustrates the diversity of neuronal morphologies in the.Early treatments of neural can be found in 's Principles of Psychology, 3rd edition (1872), 's (1884), ' Principles of (1890), and 's Project for a Scientific Psychology (composed 1895).
The first rule of neuronal learning was described by in 1949, in the. Thus, Hebbian pairing of pre-synaptic and post-synaptic activity can substantially alter the dynamic characteristics of the synaptic connection and therefore either facilitate or inhibit. In 1959, the, and published the first works on the processing of neural networks. They showed theoretically that networks of artificial neurons could, and functions. Simplified were set up, now usually called. These simple models accounted for (i.e., potentials at the post-synaptic membrane will summate in the ).
Later models also provided for excitatory and inhibitory synaptic transmission.Connections between neurons. Proposed organization of motor-semantic neural circuits for action language comprehension.
Gray dots represent areas of language comprehension, creating a network for comprehending all language. The semantic circuit of the motor system, particularly the motor representation of the legs (yellow dots), is incorporated when leg-related words are comprehended. Adapted from Shebani et al. (2013)The connections between neurons in the brain are much more complex than those of the used in the neural computing models of. The basic kinds of connections between neurons are, and.The establishment of synapses enables the connection of neurons into millions of overlapping, and interlinking neural circuits. Presynaptic proteins called are central to this process.One principle by which neurons work is – at the will sum up in the cell body.
If the of the neuron at the goes above threshold an action potential will occur that travels down the to the terminal endings to transmit a signal to other neurons. Excitatory and inhibitory synaptic transmission is realized mostly by (EPSPs), and (IPSPs).On the level, there are various phenomena which alter the response characteristics of individual synapses (called ) and individual neurons. These are often divided into short-term plasticity and long-term plasticity. Long-term synaptic plasticity is often contended to be the most likely substrate.
Usually the term ' refers to changes in the brain that are caused by activity or experience.Connections display temporal and spatial characteristics. Temporal characteristics refer to the continuously modified activity-dependent efficacy of synaptic transmission, called.
It has been observed in several studies that the synaptic efficacy of this transmission can undergo short-term increase (called ) or decrease according to the activity of the presynaptic neuron. The induction of long-term changes in synaptic efficacy, by (LTP) or (LTD), depends strongly on the relative timing of the onset of the and the postsynaptic action potential. LTP is induced by a series of action potentials which cause a variety of biochemical responses. Eventually, the reactions cause the expression of new receptors on the cellular membranes of the postsynaptic neurons or increase the efficacy of the existing receptors through.Backpropagating action potentials cannot occur because after an action potential travels down a given segment of the axon, the on close, thus blocking any transient opening of the from causing a change in the intracellular sodium ion (Na +) concentration, and preventing the generation of an action potential back towards the cell body.
In some cells, however, does occur through the and may have important effects on synaptic plasticity and computation.A neuron in the brain requires a single signal to a neuromuscular junction to stimulate contraction of the postsynaptic muscle cell. In the spinal cord, however, at least 75 neurons are required to produce firing. This picture is further complicated by variation in time constant between neurons, as some cells can experience their over a wider period of time than others.While in synapses in the synaptic depression has been particularly widely observed it has been speculated that it changes to facilitation in adult brains.Circuitry. Model of a neural circuit in theAn example of a neural circuit is the in the. Another is the linking the to the. There are several neural circuits in the.
These circuits carry information between the cortex, thalamus, and back to the cortex. The largest structure within the basal ganglia, the, is seen as having its own internal microcircuitry.Neural circuits in the called are responsible for controlling motor instructions involved in rhythmic behaviours. Rhythmic behaviours include walking,. The central pattern generators are made up of different groups of.There are four principal types of neural circuits that are responsible for a broad scope of neural functions. These circuits are a diverging circuit, a converging circuit, a reverberating circuit, and a parallel after-discharge circuit.In a diverging circuit, one neuron synapses with a number of postsynaptic cells. Each of thesemay synapse with many more making it possible for one neuron to stimulate up to thousands of cells. This is exemplified in the way that thousands of muscle fibers can be stimulated from the initial input from a single.In a converging circuit, inputs from many sources are converged into one output, affecting just one neuron or a neuron pool.
This type of circuit is exemplified in the of the, which responds to a number of inputs from different sources by giving out an appropriate breathing pattern.A reverberating circuit produces a repetitive output. In a signalling procedure from one neuron to another in a linear sequence, one of the neurons may send a signal back to initiating neuron.Each time that the first neuron fires, the other neuron further down the sequence fires again sending it back to the source. This restimulates the first neuron and also allows the path of transmission to continue to its output.
Biological Neural Networks And Mechanical Neural Networks List
A resulting repetitive pattern is the outcome that only stops if one or more of the synapses fail, or if an inhibitory feed from another source causes it to stop. This type of reverberating circuit is found in the respiratory center that sends signals to the, causing inhalation. When the circuit is interrupted by an inhibitory signal the muscles relax causing exhalation. This type of circuit may play a part in.In a parallel after-discharge circuit, a neuron inputs to several chains of neurons. Each chain is made up of a different number of neurons but their signals converge onto one output neuron. Each synapse in the circuit acts to delay the signal by about 0.5 msec so that the more synapses there are will produce a longer delay to the output neuron. After the input has stopped, the output will go on firing for some time.
Biological Neural Networks And Mechanical Neural Networks Diagram
This type of circuit does not have a feedback loop as does the reverberating circuit. Continued firing after the stimulus has stopped is called after-discharge. This circuit type is found in the of certain. Study methods. See also: andDifferent techniques have been developed to investigate the activity of neural circuits and networks. The use of 'brain scanners' or functional neuroimaging to investigate the structure or function of the brain is common, either as simply a way of better assessing brain injury with high resolution pictures, or by examining the relative activations of different brain areas.
Such technologies may include (fMRI), (brain PET), and (CAT) scans. Uses specific brain imaging technologies to take scans from the brain, usually when a person is doing a particular task, in an attempt to understand how the activation of particular brain areas is related to the task. In functional neuroimaging, especially fMRI, which measures (using ) which is closely linked to neural activity, PET, and (EEG) is used.models serve as a test platform for different hypotheses of representation, information processing, and signal transmission.
Lesioning studies in such models, e.g., where parts of the nodes are deliberately destroyed to see how the network performs, can also yield important insights in the working of several cell assemblies. Similarly, simulations of dysfunctional neurotransmitters in neurological conditions (e.g., dopamine in the basal ganglia of patients) can yield insights into the underlying mechanisms for patterns of cognitive deficits observed in the particular patient group. Predictions from these models can be tested in patients or via pharmacological manipulations, and these studies can in turn be used to inform the models, making the process iterative.Clinical significance Sometimes neural circuitries can become pathological and cause problems such as in when the are involved. Problems in the can also give rise to a number of including Parkinson's.See also.References.