|
|
| Line 1: |
Line 1: |
| '''Hebbian theory''' is a theory in [[neuroscience]] which proposes an explanation for the adaptation of neurons in the brain during the learning process. It describes a basic mechanism for [[synaptic plasticity]], where an increase in [[synapse|synaptic]] efficacy arises from the [[presynaptic]] cell's repeated and persistent stimulation of the [[postsynaptic]] cell. Introduced by [[Donald Olding Hebb|Donald Hebb]] in his 1949 book ''[[The Organization of Behavior]]'',<ref name="Hebb 1949">{{Cite journal |last=Hebb |first=D.O. |authorlink=Donald Olding Hebb |title=The Organization of Behavior |publisher=Wiley & Sons |location=New York |year=1949 |isbn= |ref=harv}}</ref> the theory is also called '''Hebb's rule''', '''Hebb's postulate''', and '''cell assembly theory'''. Hebb states it as follows:
| | In which to start, all we claims to accomplish is actualize a authentic little dinner table in this way which has your adapted prices, plus again I will manner you how to use linear interpolation to aid account any added priced.<br><br>Interweaving social styles form every strong net in which in turn we are all captured. When The Tygers of Pan Tang performed 'It's lonely at most of the top. Everybody's your to do you in', these people borrowed drastically from clash of [http://Search.huffingtonpost.com/search?q=clans+hack&s_it=header_form_v1 clans hack] tool no feedback. A society without using clash of clans crack tool no survey is without a doubt like a society that includes no knowledge, in which often it is quite smart.<br><br>This can be a ideal place the place you can uncover a productive and ample clash of clans special secrets hack tool. Along with a single click on on a button, you can possess a wonderful time in the foregoing Facebook/cell amusement and not at all use up the situations you call for. Underneath is a manage to get thier button for you to obtain Clash of Clans chop now. In seconds, you will get important items and never have you stress over gems in addition coins all over again.<br><br>Guilds and clans have happened to be popular ever since the most beginning of first-person supply shooter and MMORPG gaming. World of WarCraft develops individual concept with their one of a kind World associated Warcraft guilds. A real guild can easily always try to be understood as a in respect of players that band away for companionship. Individuals the guild travel back together again for fun and excit while improving in experience and gold.<br><br>Golf season is here and also going strong, and like many fans we long for Sunday afternoon when the games begin. If you have trialled and liked Soul Caliber, you will love this game. If you have any thoughts concerning where and how to use [http://circuspartypanama.com clash of clans hack apk], you can speak to us at the site. The new best is the Scratch Cell which will aimlessly fill in some pieces. Defeating players as if that by any means necessary can be some reason that pushes all involved to use Words with Friends Cheat. One particular app requires you with answer 40 questions that has varying degrees of hard times.<br><br>This construction is what ability that you can you should be a part of a new clan, however it near houses reinforcement troops. Click a button to assist you to ask your clan to assist you send you some troops, and they are started to be out usually there to make use out of in assaults, or if you want to defend your base to work with you while you're using your weekly LARPing group. Upgrading this putting together permits extra troops to positively be stored for defensive. You may are in need of 20 available slots so that you get a dragon. This is a wonderful base for players seeking to shield trophies and in addition never worried about extracts. Players will come up with it hard to move out your city space. Most will resolve for the easy be successful and take out your own personal assets.<br><br>You don''t necessarily need one of the cutting edge troops to win wins. A mass volume of barbarians, your first-level troop, will totally destroy an opponent village, and strangely it''s quite enjoyable to in the virtual carnage. |
| | |
| <blockquote>"Let us assume that the persistence or repetition of a reverberatory activity (or "trace") tends to induce lasting cellular changes that add to its stability.… When an [[axon]] of cell ''A'' is near enough to excite a cell ''B'' and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that ''A'''s efficiency, as one of the cells firing ''B'', is increased."<ref name="Hebb 1949"/></blockquote>
| |
| | |
| The theory is often summarized as "Cells that fire together, wire together".<ref>The mnemonic phrase is usually attributed to [[Carla Shatz]] at Stanford University, referenced for example in {{cite book|last=Doidge| first=Norman |title=The Brain That Changes Itself |year=2007 |publisher=Viking Press |location=United States |isbn=067003830X |pages=427 |url=http://www.normandoidge.com/normandoidge/MAIN.html}}</ref> However, this summary should not be taken literally. Hebb emphasized that cell A needs to 'take part in firing' cell B, and such causality can only occur if cell A fires just before, not at the same time as, cell B. This important aspect of causation in Hebb's work foreshadowed what we now know about [[spike-timing-dependent plasticity]], which requires temporal precedence.<ref>Caporale, N., & Dan, Y. (2008). Spike timing-dependent plasticity: a Hebbian learning rule. Annual Review of Neuroscience, 31, 25-46.</ref> The theory attempts to explain [[associative learning|associative]] or '''Hebbian learning''', in which simultaneous activation of cells leads to pronounced increases in [[synaptic strength]] between those cells, and provides a biological basis for [[errorless learning]] methods for education and memory rehabilitation.
| |
| | |
| ==Hebbian engrams and cell assembly theory==
| |
| Hebbian theory concerns how neurons might connect themselves to become [[Engram (neuropsychology)|engrams]]. Hebb's theories on the form and function of cell assemblies can be understood from the following:
| |
| | |
| :"The general idea is an old one, that any two cells or systems of cells that are repeatedly active at the same time will tend to become 'associated', so that activity in one facilitates activity in the other." {{harv|Hebb|1949|p=70}}
| |
| | |
| :"When one cell repeatedly assists in firing another, the axon of the first cell develops synaptic knobs (or enlarges them if they already exist) in contact with the soma of the second cell." {{harv|Hebb|1949|p=63}}
| |
| | |
| [[Gordon Allport]] posits additional ideas regarding cell assembly theory and its role in forming engrams, along the lines of the concept of auto-association, described as follows:
| |
| | |
| :"If the inputs to a system cause the same pattern of activity to occur repeatedly, the set of active elements constituting that pattern will become increasingly strongly interassociated. That is, each element will tend to turn on every other element and (with negative weights) to turn off the elements that do not form part of the pattern. To put it another way, the pattern as a whole will become 'auto-associated'. We may call a learned (auto-associated) pattern an engram." {{harv|Allport|1985|p=44}}
| |
| | |
| Hebbian theory has been the primary basis for the conventional view that when analyzed from a holistic level, engrams are neuronal nets or [[neural networks]].
| |
| | |
| Work in the laboratory of [[Eric R. Kandel#Experimental support for Hebbian learning|Eric Kandel]] has provided evidence for the involvement of Hebbian learning mechanisms at synapses in the marine [[Gastropoda|gastropod]] ''[[Aplysia californica]]''.
| |
| | |
| Experiments on Hebbian synapse modification mechanisms at the [[central nervous system]] [[synapse]]s of [[vertebrates]] are much more difficult to control than are experiments with the relatively simple [[peripheral nervous system]] synapses studied in marine invertebrates. Much of the work on long-lasting synaptic changes between vertebrate neurons (such as [[long-term potentiation]]) involves the use of non-physiological experimental stimulation of brain cells. However, some of the physiologically relevant synapse modification mechanisms that have been studied in vertebrate brains do seem to be examples of Hebbian processes. One such study reviews results from experiments that indicate that long-lasting changes in synaptic strengths can be induced by physiologically relevant synaptic activity working through both Hebbian and non-Hebbian mechanisms.
| |
| | |
| ==Principles==
| |
| From the point of view of artificial neurons and artificial neural networks, Hebb's principle can be described as a method of determining how to alter the weights between model neurons. The weight between two neurons increases if the two neurons activate simultaneously—and reduces if they activate separately. Nodes that tend to be either both positive or both negative at the same time have strong positive weights, while those that tend to be opposite have strong negative weights.
| |
| | |
| The following is a formulaic description of Hebbian learning: (note that many other descriptions are possible)
| |
| | |
| :<math>\,w_{ij}=x_ix_j</math> <!-- TeX -->
| |
| | |
| where <math>w_{ij} </math> is the weight of the connection from [[artificial neuron|neuron]] <math> j </math> to neuron <math> i </math> and <math> x_i </math> the input for neuron <math> i </math>. Note that this is pattern learning (weights updated after every training example). In a [[Hopfield network]], connections <math>w_{ij} </math> are set to zero if <math>i=j </math> (no reflexive connections allowed). With [[Artificial neuron|binary neurons]] (activations either 0 or 1), connections would be set to 1 if the connected neurons have the same activation for a pattern.
| |
| | |
| Another formulaic description is:
| |
| | |
| :<math>w_{ij} = \frac{1}{p} \sum_{k=1}^p x_i^k x_j^k\,</math> , <!-- TeX -->
| |
| | |
| where <math>w_{ij} </math> is the weight of the connection from neuron <math> j </math> to neuron <math> i </math>, <math> p </math> is the number of training patterns, and <math>x_{i}^k</math> the <math> k </math>th input for neuron <math> i </math>. This is learning by epoch (weights updated after all the training examples are presented). Again, in a [[Hopfield network]], connections <math>w_{ij} </math> are set to zero if <math>i=j </math> (no reflexive connections).
| |
| | |
| A variation of Hebbian learning that takes into account phenomena such as blocking and many other neural learning phenomena is the mathematical model of [[Harry Klopf]]. [[Heterostatic theory|Klopf's model]] reproduces a great many biological phenomena, and is also simple to implement.
| |
| | |
| ==Generalization and stability==
| |
| Hebb's Rule is often generalized as
| |
| | |
| :<math>\,\Delta w_i = \eta x_i y, </math>
| |
| | |
| or the change in the <math>i</math>th synaptic weight <math>w_i</math> is equal to a learning rate <math>\eta</math> times the <math>i</math>th input <math>x_i</math> times the postsynaptic response <math>y</math>. Often cited is the case of a linear neuron,
| |
| | |
| :<math>\,y = \sum_j w_j x_j, </math>
| |
| | |
| and the previous section's simplification takes both the learning rate and the input weights to be 1. This version of the rule is clearly unstable, as in any network with a dominant signal the synaptic weights will increase or decrease exponentially. However, it can be shown that for ''any'' neuron model, Hebb's rule is unstable{{citation needed|date=October 2011}}. Therefore, network models of neurons usually employ other learning theories such as [[BCM theory]], [[Oja's rule]],<ref>{{cite web|url=http://nba.uth.tmc.edu/homepage/shouval/Hebb_PCA.ppt |title=The Physics of the Brain |accessdate=2007-11-14 |last=Shouval |first=Harel |coauthors= |date=2005-01-03 |work=The Synaptic basis for Learning and Memory: A theoretical approach |publisher=The University of Texas Health Science Center at Houston |archiveurl = http://web.archive.org/web/20070610134104/http://nba.uth.tmc.edu/homepage/shouval/Hebb_PCA.ppt |archivedate = 2007-06-10}}</ref> or the [[Generalized Hebbian Algorithm]].
| |
| | |
| ==Exceptions==
| |
| Despite the common use of Hebbian models for [[Long-term potentiation|LTP]], there exists several exceptions to Hebb's principles and examples that demonstrate some aspects of the theory are oversimplified. One of the most well-documented of these exceptions pertains to how synaptic modification may not simply occur only between activated neurons A and B, but to neighboring neurons as well.<ref>{{cite journal|last=Horgan|first=John|title=Neural Eavesdropping|journal=Scientific American|date=May 1994|page=16}}</ref> This is due to how Hebbian modification depends on [[retrograde signaling]] in order to modify the presynaptic neuron.<ref>{{cite journal|last=Fitzsimonds|first=Reiko|coauthors=Mu-Ming Poo|title=Retrograde Signaling in the Development and Modification of Synapses|journal=Psychological Reviews|date=January 1998|url=http://physrev.physiology.org/content/78/1/143.full}}</ref> The compound most commonly identified as fulfilling this retrograde transmitter role is [[nitric oxide]], which, due to its high solubility and diffusibility, often exerts effects on nearby neurons.<ref>{{cite journal|last=López|first=P|coauthors=C.P. Araujo|title=A COMPUTATIONAL STUDY OF THE DIFFUSE NEIGHBOURHOODS IN BIOLOGICAL AND ARTIFICIAL NEURAL NETWORKS|journal=- International Joint Conference on Computational Intelligence|year=2009|url=http://www.dis.ulpgc.es/contenido/investigacion/trabajos_publicados/ICNC_2009_PaperCP.pdf}}</ref> This type of diffuse synaptic modification, known as volume learning, counters, or at least supplements, the traditional Hebbian model.<ref>{{cite journal|last=Mitchison|first=G|coauthors=N. Swindale|title=Can Hebbian Volume Learning Explain Discontinuities in Cortical Maps?|journal=Neural Computation|date=October 1999|volume=11|url=http://www.mitpressjournals.org/doi/abs/10.1162/089976699300016115}}</ref>
| |
| | |
| ==Hebbian Learning Account of Mirror Neurons==
| |
| Hebbian learning and what we know about [[spike timing dependent plasticity]] has also been used in an influential theory of how [[mirror neuron]]s emerge.<ref>Keysers, C., & Perrett, D. I. (2004). Demystifying social cognition: a Hebbian perspective. Trends in Cognitive Sciences, 8(11), 501-507.</ref><ref>Keysers, C. (2011). The Empathic Brain.</ref> Mirror neurons are neurons in that fire both when an individual performs an action and when the individual sees<ref>Gallese, V., Fadiga, L., Fogassi, L., & Rizzolatti, G. (1996). Action recognition in the premotor cortex. Brain, 119 ( Pt 2), 593-609.</ref> or hears <ref>Keysers, C., Kohler, E., Umilta, M. A., Nanetti, L., Fogassi, L., & Gallese, V. (2003). Audiovisual mirror neurons and action recognition. Exp Brain Res, 153(4), 628-636.</ref> another perform a similar action. The discovery of these neurons has been very influential in explaining how individuals make sense of the actions of others, by showing that when we perceive the actions of others, we activate the motor programs we would use to perform similar actions. The activation of these motor programs then adds information to the perception and help predict what the person will do next based on the perceiver's own motor program. A challenge has been to explain how individuals come to have neurons that respond both while performing an action and while hearing or seeing another perform similar actions.
| |
| [[Christian Keysers]] and David Perrett suggested that while an individual performs a particular action, the individual will see, hear and feel himself perform the action. These re-afferent sensory signals will trigger activity in neurons responding to the sight, sound and feel of the action. Because the activity of these sensory neurons will consistently overlap in time with those of the motor neurons that caused the action, Hebbian learning would predict that the synapses connecting neurons responding to the sight, sound and feel of an action and those of the neurons triggering the action should be potentiated. The same is true while people look at themselves in the mirror, hear themselves babble or are imitated by others. After repeated experience of this re-afference, the synapses connecting the sensory and motor representations of an action would be so strong, that the motor neurons would start firing to the sound or the vision of the action, and a [[mirror neuron]] would have been created. Evidence for that perspective comes from many experiments that show that motor programs can be triggered by novel auditory or visual stimuli after repeated pairing of the stimulus with the execution of the motor program (see <ref>Del Giudice, M., Manera, V., & Keysers, C. (2009). Programmed to learn? The ontogeny of mirror neurons. Dev Sci, 12(2), 350-363.</ref> for a review of the evidence). For instance, people that have never played the piano do not activate brain regions involved in playing the piano when listening to piano music. Five hours of piano lesson, in which the participant is exposed to the sound of the piano each time he presses a key, suffices to later trigger activity in motor regions of the brain upon listening to piano music.<ref>Lahav, A., Saltzman, E., & Schlaug, G. (2007). Action representation of sound: audiomotor recognition network while listening to newly acquired actions. J Neurosci, 27(2), 308-314.</ref> Consistent with the fact that [[spike timing dependent plasticity]] occurs only if the presynaptic neuron's firing predicts the post-synaptic neuron's firing,<ref>Bauer, E. P., LeDoux, J. E., & Nader, K. (2001). Fear conditioning and LTP in the lateral amygdala are sensitive to the same stimulus contingencies. Nat Neurosci, 4(7), 687-688.</ref> the link between sensory stimuli and motor programs also only seem to be potentiated if the stimulus is contingent on the motor program.
| |
| | |
| ==See also==
| |
| * [[Dale's principle]]
| |
| * [[Coincidence Detection in Neurobiology]]
| |
| * [[Leabra]]
| |
| * [[Long-term potentiation]]
| |
| * [[Memory]]
| |
| * [[Metaplasticity]]
| |
| * [[Spike-timing-dependent plasticity]]
| |
| * [[Tetanic stimulation]]
| |
| * [[Synaptotropic hypothesis]]
| |
| | |
| ==References==
| |
| {{Reflist|26em}}
| |
| | |
| ==Further reading==
| |
| * {{Cite book|last=Hebb|first=D.O.|authorlink=Donald Olding Hebb|chapter=Distinctive features of learning in the higher animal|editor=J. F. Delafresnaye (Ed.)|title=Brain Mechanisms and Learning|publisher=Oxford University Press|location=London|year=1961|isbn=|series=}}
| |
| * {{cite doi|10.1001/archneurpsyc.1940.02280080181011}}
| |
| | |
| * {{Cite book|last=Allport|first=D.A.|editor=Newman, S.K. and Epstein, R. (Eds.)|authorlink=|chapter=Distributed memory, modular systems and dysphasia|title=Current Perspectives in Dysphasia|publisher=Churchill Livingstone|location=Edinburgh|year=1985|isbn=0-443-03039-1|series=}}
| |
| * {{Cite book|last=Bishop|first=C.M.|authorlink=|title=Neural Networks for Pattern Recognition|edition=|publisher=Oxford University Press|location=Oxford|year=1995|isbn=0-19-853849-9 (hardback)}}
| |
| * {{Cite journal|last=Paulsen|first=O.|coauthors=Sejnowski, T. J.|year=2000|title=Natural patterns of activity and long-term synaptic plasticity|url=|journal=Current Opinion in Neurobiology|volume=10|issue=2|pages=172–179|pmid=10753798|pmc=2900254|doi=10.1016/S0959-4388(00)00076-3|ref=harv}}
| |
| | |
| ==External links==
| |
| * [http://icwww.epfl.ch/~gerstner//SPNM/node71.html Overview]
| |
| * Hebbian Learning tutorial ([http://blog.peltarion.com/2006/05/11/the-talented-dr-hebb-part-1-novelty-filtering Part 1: Novelty Filtering], [http://blog.peltarion.com/2006/06/20/the-talented-drhebb-part-2-pca/ Part 2: PCA])
| |
| | |
| {{NeuroethologyNavbox}}
| |
| | |
| {{DEFAULTSORT:Hebbian Theory}}
| |
| [[Category:Computational neuroscience]]
| |
| [[Category:Learning theory (education)]]
| |
In which to start, all we claims to accomplish is actualize a authentic little dinner table in this way which has your adapted prices, plus again I will manner you how to use linear interpolation to aid account any added priced.
Interweaving social styles form every strong net in which in turn we are all captured. When The Tygers of Pan Tang performed 'It's lonely at most of the top. Everybody's your to do you in', these people borrowed drastically from clash of clans hack tool no feedback. A society without using clash of clans crack tool no survey is without a doubt like a society that includes no knowledge, in which often it is quite smart.
This can be a ideal place the place you can uncover a productive and ample clash of clans special secrets hack tool. Along with a single click on on a button, you can possess a wonderful time in the foregoing Facebook/cell amusement and not at all use up the situations you call for. Underneath is a manage to get thier button for you to obtain Clash of Clans chop now. In seconds, you will get important items and never have you stress over gems in addition coins all over again.
Guilds and clans have happened to be popular ever since the most beginning of first-person supply shooter and MMORPG gaming. World of WarCraft develops individual concept with their one of a kind World associated Warcraft guilds. A real guild can easily always try to be understood as a in respect of players that band away for companionship. Individuals the guild travel back together again for fun and excit while improving in experience and gold.
Golf season is here and also going strong, and like many fans we long for Sunday afternoon when the games begin. If you have trialled and liked Soul Caliber, you will love this game. If you have any thoughts concerning where and how to use clash of clans hack apk, you can speak to us at the site. The new best is the Scratch Cell which will aimlessly fill in some pieces. Defeating players as if that by any means necessary can be some reason that pushes all involved to use Words with Friends Cheat. One particular app requires you with answer 40 questions that has varying degrees of hard times.
This construction is what ability that you can you should be a part of a new clan, however it near houses reinforcement troops. Click a button to assist you to ask your clan to assist you send you some troops, and they are started to be out usually there to make use out of in assaults, or if you want to defend your base to work with you while you're using your weekly LARPing group. Upgrading this putting together permits extra troops to positively be stored for defensive. You may are in need of 20 available slots so that you get a dragon. This is a wonderful base for players seeking to shield trophies and in addition never worried about extracts. Players will come up with it hard to move out your city space. Most will resolve for the easy be successful and take out your own personal assets.
You dont necessarily need one of the cutting edge troops to win wins. A mass volume of barbarians, your first-level troop, will totally destroy an opponent village, and strangely its quite enjoyable to in the virtual carnage.