553:
496:
280:
Computationally, this means that if a large signal from one of the input neurons results in a large signal from one of the output neurons, then the synaptic weight between those two neurons will increase. The rule is unstable, however, and is typically modified using such variations as
237:
392:) is still poorly understood. Hebb's original learning rule was originally applied to biological systems, but has had to undergo many modifications as a number of theoretical and experimental problems came to light.
112:
88:
261:
132:
140:
537:
594:
354:
The amount of neurotransmitter released into the synapse and the amount that can be absorbed in the following cell (determined by the number of
613:
633:
530:
628:
587:
114:, or pre- and post-synaptic neurons respectively, are interconnected with synaptic weights represented by the matrix
523:
618:
48:
44:
623:
580:
385:
320:, signal transmission is carried out by interconnected networks of nerve cells, or neurons. For the basic
317:
286:
243:
where the rows of the synaptic matrix represent the vector of synaptic weights for the output indexed by
444:
389:
406:
381:
93:
69:
301:
For biological networks, the effect of synaptic weights is not as simple as for linear neurons or
373:
472:
560:
462:
452:
337:
321:
302:
63:
36:
24:
16:
Strength or amplitude of a connection between two nodes in neuroscience and computer science
35:
of a connection between two nodes, corresponding in biology to the amount of influence the
290:
448:
232:{\displaystyle y_{j}=\sum _{i}w_{ij}x_{i}~~{\textrm {or}}~~{\textbf {y}}=w{\textbf {x}}}
564:
507:
467:
432:
411:
401:
267:
246:
117:
51:
607:
359:
355:
282:
266:
The synaptic weight is changed by using a learning rule, the most basic of which is
503:
20:
457:
348:
433:"The influence of synaptic weight distribution on neuronal population dynamics"
343:
The synaptic weight in this process is determined by several variable factors:
310:
306:
333:
32:
552:
476:
363:
329:
495:
40:
313:
have seen some success in mathematically describing these networks.
340:
which is analogous to the output signal in the computational case.
369:
The number of such connections made by the axon to the dendrites,
325:
347:
How well the input signal propagates through the axon (see
431:
Iyer, R; Menon, V; Buice, M; Koch, C; Mihalas, S (2013).
568:
511:
380:
The changes in synaptic weight that occur is known as
362:
on the cell membrane and the amount of intracellular
328:, which releases neurotransmitter chemicals into the
249:
143:
120:
96:
72:
270:, which is usually stated in biological terms as
255:
231:
126:
106:
82:
336:of the next neuron, which can then generate an
43:has on another. The term is typically used in
588:
531:
8:
384:, and the process behind long-term changes (
595:
581:
538:
524:
275:Neurons that fire together, wire together.
466:
456:
248:
223:
222:
210:
209:
197:
196:
184:
171:
161:
148:
142:
119:
98:
97:
95:
74:
73:
71:
423:
324:, the input signal is carried by the
62:In a computational neural network, a
7:
549:
547:
492:
490:
372:How well the signal propagates and
224:
211:
99:
75:
14:
551:
494:
1:
107:{\displaystyle {\textbf {y}}}
83:{\displaystyle {\textbf {x}}}
567:. You can help Knowledge by
510:. You can help Knowledge by
458:10.1371/journal.pcbi.1003248
134:, where for a linear neuron
650:
614:Artificial neural networks
546:
489:
437:PLOS Computational Biology
332:which is picked up by the
31:refers to the strength or
376:in the postsynaptic cell.
634:Computer science stubs
386:long-term potentiation
318:central nervous system
287:radial basis functions
278:
257:
233:
128:
108:
84:
272:
258:
234:
129:
109:
85:
247:
141:
118:
94:
70:
449:2013PLSCB...9E3248I
407:Synaptic plasticity
382:synaptic plasticity
629:Neuroscience stubs
253:
229:
166:
124:
104:
80:
576:
575:
519:
518:
316:In the mammalian
256:{\displaystyle j}
226:
213:
208:
205:
200:
195:
192:
157:
127:{\displaystyle w}
101:
77:
66:or set of inputs
641:
619:Neural circuitry
597:
590:
583:
561:computer science
555:
548:
540:
533:
526:
498:
491:
481:
480:
470:
460:
443:(10): e1003248.
428:
366:and other ions),
338:action potential
322:pyramidal neuron
303:Hebbian learning
262:
260:
259:
254:
238:
236:
235:
230:
228:
227:
215:
214:
206:
203:
202:
201:
198:
193:
190:
189:
188:
179:
178:
165:
153:
152:
133:
131:
130:
125:
113:
111:
110:
105:
103:
102:
89:
87:
86:
81:
79:
78:
25:computer science
649:
648:
644:
643:
642:
640:
639:
638:
624:Neuroplasticity
604:
603:
602:
601:
545:
544:
487:
485:
484:
430:
429:
425:
420:
398:
309:models such as
299:
291:backpropagation
245:
244:
180:
167:
144:
139:
138:
116:
115:
92:
91:
68:
67:
60:
29:synaptic weight
17:
12:
11:
5:
647:
645:
637:
636:
631:
626:
621:
616:
606:
605:
600:
599:
592:
585:
577:
574:
573:
556:
543:
542:
535:
528:
520:
517:
516:
499:
483:
482:
422:
421:
419:
416:
415:
414:
412:Hebbian theory
409:
404:
402:Neural network
397:
394:
378:
377:
370:
367:
360:NMDA receptors
352:
298:
295:
252:
241:
240:
221:
218:
187:
183:
177:
174:
170:
164:
160:
156:
151:
147:
123:
59:
56:
52:neural network
15:
13:
10:
9:
6:
4:
3:
2:
646:
635:
632:
630:
627:
625:
622:
620:
617:
615:
612:
611:
609:
598:
593:
591:
586:
584:
579:
578:
572:
570:
566:
563:article is a
562:
557:
554:
550:
541:
536:
534:
529:
527:
522:
521:
515:
513:
509:
506:article is a
505:
500:
497:
493:
488:
478:
474:
469:
464:
459:
454:
450:
446:
442:
438:
434:
427:
424:
417:
413:
410:
408:
405:
403:
400:
399:
395:
393:
391:
387:
383:
375:
371:
368:
365:
361:
357:
353:
350:
346:
345:
344:
341:
339:
335:
331:
327:
323:
319:
314:
312:
308:
304:
296:
294:
292:
288:
284:
277:
276:
271:
269:
264:
250:
219:
216:
185:
181:
175:
172:
168:
162:
158:
154:
149:
145:
137:
136:
135:
121:
65:
57:
55:
53:
50:
46:
42:
38:
34:
30:
26:
22:
569:expanding it
558:
512:expanding it
504:neuroscience
501:
486:
440:
436:
426:
379:
342:
315:
300:
279:
274:
273:
265:
242:
90:and outputs
61:
28:
21:neuroscience
18:
349:myelination
307:biophysical
305:. However,
293:algorithm.
268:Hebb's rule
58:Computation
608:Categories
418:References
390:depression
374:integrates
311:BCM theory
283:Oja's rule
54:research.
49:biological
45:artificial
334:dendrites
159:∑
33:amplitude
477:24204219
396:See also
468:3808453
445:Bibcode
364:calcium
330:synapse
297:Biology
289:or the
39:of one
475:
465:
207:
204:
194:
191:
64:vector
41:neuron
37:firing
559:This
502:This
565:stub
508:stub
473:PMID
388:and
358:and
356:AMPA
326:axon
47:and
23:and
463:PMC
453:doi
19:In
610::
471:.
461:.
451:.
439:.
435:.
351:),
285:,
263:.
199:or
27:,
596:e
589:t
582:v
571:.
539:e
532:t
525:v
514:.
479:.
455::
447::
441:9
251:j
239:.
225:x
220:w
217:=
212:y
186:i
182:x
176:j
173:i
169:w
163:i
155:=
150:j
146:y
122:w
100:y
76:x
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.