Editing Hebbian theory (section)

==Limitations==
Despite the common use of Hebbian models for long-term potentiation, Hebbian theory does not cover all forms of long-term synaptic plasticity. Hebb did not propose any rules for inhibitory synapses or predictions for anti-causal spike sequences (where the presynaptic neuron fires ''after'' the postsynaptic neuron). Synaptic modification may not simply occur only between activated neurons A and B, but at neighboring synapses as well.<ref>{{cite journal |last=Horgan |first=John |date=May 1994 |title=Neural eavesdropping |journal=Scientific American |volume=270 |issue=5 |page=16 |bibcode=1994SciAm.270e..16H |doi=10.1038/scientificamerican0594-16 |pmid=8197441}}</ref> Therefore, all forms of [[heterosynaptic plasticity]] and [[homeostatic plasticity]] are considered non-Hebbian. One example is [[retrograde signaling]] to presynaptic terminals.<ref>{{cite journal |last=Fitzsimonds |first=Reiko |author2=Mu-Ming Poo |author2-link=Mu-Ming Poo |date=January 1998 |title=Retrograde Signaling in the Development and Modification of Synapses |journal=Physiological Reviews |volume=78 |issue=1 |pages=143–170 |doi=10.1152/physrev.1998.78.1.143 |pmid=9457171 |s2cid=11604896}}</ref> The compound most frequently recognized as a retrograde transmitter is [[nitric oxide]], which, due to its high solubility and diffusivity, often exerts effects on nearby neurons.<ref>{{cite journal |last=López |first=P |author2=C.P. Araujo |year=2009 |title=A computational study of the diffuse neighbourhoods in biological and artificial neural networks |url=http://www.dis.ulpgc.es/contenido/investigacion/trabajos_publicados/ICNC_2009_PaperCP.pdf |journal=International Joint Conference on Computational Intelligence}}</ref> This type of diffuse synaptic modification, known as volume learning, is not included in the traditional Hebbian model.<ref>{{cite journal |last=Mitchison |first=G |author2=N. Swindale |date=October 1999 |title=Can Hebbian Volume Learning Explain Discontinuities in Cortical Maps? |journal=Neural Computation |volume=11 |issue=7 |pages=1519–1526 |doi=10.1162/089976699300016115 |pmid=10490935 |s2cid=2325474}}</ref>