Topic: Computational neuroscience
Title: Spike-based computing and learning in brains, machines, and visual systems in particular
Abstract:
Using simulations, we have first shown that, thanks to the physiological learning mechanism referred to as spike timing-dependent plasticity (STDP), neurons can detect and learn repeating spike patterns, in an unsupervised manner, even when those patterns are embedded in noise[1,2], and the detection can be optimal[3]. Importantly, the spike patterns do not need to repeat exactly: it also works when only a firing probability pattern repeats, providing this profile has narrow (10-20ms) temporal peaks[4]. Brain oscillations may help in getting the required temporal precision[5,6], in particular when dealing with slowly changing stimuli. All together, these studies show that some envisaged problems associated to spike timing codes, in particular noise-resistance, the need for a reference time, or the decoding issue, might not be as severe as once thought. These generic STDP-based mechanisms are probably at work in particular the visual system, where they can explain how selectivity to visual primitives emerges, leading to efficient object recognition systems[7–10]. High spike time precision is required, and microsaccades could help[11].
References:
1. Masquelier T, Guyonneau R, Thorpe SJ (2008) Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains. PLoS One 3: e1377. doi:10.1371/journal.pone.0001377.
2. Masquelier T, Guyonneau R, Thorpe SJ (2009) Competitive STDP-Based Spike Pattern Learning. Neural Comput 21: 1259–1276. doi:10.1162/neco.2008.06-08-804.
3. Masquelier T (2016) STDP allows close-to-optimal spatiotemporal spike pattern detection by single coincidence detector neurons. arXiv.
4. Gilson M, Masquelier T, Hugues E (2011) STDP allows fast rate-modulated coding with Poisson-like spike trains. PLoS Comput Biol 7: e1002231. doi:10.1371/journal.pcbi.1002231.
5. Masquelier T, Hugues E, Deco G, Thorpe SJ (2009) Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme. J Neurosci 29: 13484–13493. doi:10.1523/JNEUROSCI.2207-09.2009.
6. Masquelier T (2014) Oscillations can reconcile slowly changing stimuli with short neuronal integration and STDP timescales. Network 25: 85–96. doi:10.3109/0954898X.2014.881574.
7. Masquelier T, Thorpe SJ (2007) Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Comput Biol 3: e31. doi:10.1371/journal.pcbi.0030031.
8. Masquelier T (2012) Relative spike time coding and STDP-based orientation selectivity in the early visual system in natural continuous and saccadic vision: a computational model. J Comput Neurosci 32: 425–441. doi:10.1007/s10827-011-0361-9.
9. Kheradpisheh SR, Ganjtabesh M, Masquelier T (2016) Bio-inspired unsupervised learning of visual features leads to robust invariant object recognition. Neurocomputing 205: 382–392. doi:10.1016/j.neucom.2016.04.029.
10. Kheradpisheh SR, Ganjtabesh M, Thorpe SJ, Masquelier T (2016) STDP-based spiking deep neural networks for object recognition. arXiv.
11. Masquelier T, Portelli G, Kornprobst P (2016) Microsaccades enable efficient synchrony-based coding in the retina: a simulation study. Sci Rep 6: 24086. doi:10.1038/srep24086.
Title: Spike-based computing and learning in brains, machines, and visual systems in particular
Abstract:
Using simulations, we have first shown that, thanks to the physiological learning mechanism referred to as spike timing-dependent plasticity (STDP), neurons can detect and learn repeating spike patterns, in an unsupervised manner, even when those patterns are embedded in noise[1,2], and the detection can be optimal[3]. Importantly, the spike patterns do not need to repeat exactly: it also works when only a firing probability pattern repeats, providing this profile has narrow (10-20ms) temporal peaks[4]. Brain oscillations may help in getting the required temporal precision[5,6], in particular when dealing with slowly changing stimuli. All together, these studies show that some envisaged problems associated to spike timing codes, in particular noise-resistance, the need for a reference time, or the decoding issue, might not be as severe as once thought. These generic STDP-based mechanisms are probably at work in particular the visual system, where they can explain how selectivity to visual primitives emerges, leading to efficient object recognition systems[7–10]. High spike time precision is required, and microsaccades could help[11].
References:
1. Masquelier T, Guyonneau R, Thorpe SJ (2008) Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains. PLoS One 3: e1377. doi:10.1371/journal.pone.0001377.
2. Masquelier T, Guyonneau R, Thorpe SJ (2009) Competitive STDP-Based Spike Pattern Learning. Neural Comput 21: 1259–1276. doi:10.1162/neco.2008.06-08-804.
3. Masquelier T (2016) STDP allows close-to-optimal spatiotemporal spike pattern detection by single coincidence detector neurons. arXiv.
4. Gilson M, Masquelier T, Hugues E (2011) STDP allows fast rate-modulated coding with Poisson-like spike trains. PLoS Comput Biol 7: e1002231. doi:10.1371/journal.pcbi.1002231.
5. Masquelier T, Hugues E, Deco G, Thorpe SJ (2009) Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme. J Neurosci 29: 13484–13493. doi:10.1523/JNEUROSCI.2207-09.2009.
6. Masquelier T (2014) Oscillations can reconcile slowly changing stimuli with short neuronal integration and STDP timescales. Network 25: 85–96. doi:10.3109/0954898X.2014.881574.
7. Masquelier T, Thorpe SJ (2007) Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Comput Biol 3: e31. doi:10.1371/journal.pcbi.0030031.
8. Masquelier T (2012) Relative spike time coding and STDP-based orientation selectivity in the early visual system in natural continuous and saccadic vision: a computational model. J Comput Neurosci 32: 425–441. doi:10.1007/s10827-011-0361-9.
9. Kheradpisheh SR, Ganjtabesh M, Masquelier T (2016) Bio-inspired unsupervised learning of visual features leads to robust invariant object recognition. Neurocomputing 205: 382–392. doi:10.1016/j.neucom.2016.04.029.
10. Kheradpisheh SR, Ganjtabesh M, Thorpe SJ, Masquelier T (2016) STDP-based spiking deep neural networks for object recognition. arXiv.
11. Masquelier T, Portelli G, Kornprobst P (2016) Microsaccades enable efficient synchrony-based coding in the retina: a simulation study. Sci Rep 6: 24086. doi:10.1038/srep24086.
Complex Systems Studies
https://en.wikipedia.org/wiki/G%C3%B6del,_Escher,_Bach
«گودل، اشر، باخ» (انگلیسی: Gödel, Escher, Bach) یک کتاب از داگلاس هافستادر است. این کتاب درباره کار و زندگی سه نفر است: منطقدان کورت گودل، هنرمند موریس اشر و یوهان سباستیان باخ. این کتاب توضیح میدهد که چگونه یک سیستم زمانی که ظاهراً از اجزای بیمعنی ساخته شدهاست میتواند با استفاده از مفهوم ارجاع به خود و قوانین منطق مفاهیم معنیدار بسازد. این کتاب همچنین در مورد ارتباطات، و معرفی و نگاهداری دانش بحث میکند.
هافستادر در این کتاب بحث میکند که چگونه مفاهیمی بسیار سادهای همچون #یاختههای_عصبی میتوانند برای انسان #هوش به وجود بیاورند و آن را با رفتار یک #کلونی_مورچهها مقایسه میکند که رفتار هر مورچه بسیار ساده و #رفتار کلونی #شعورمند است.
هافستادر در این کتاب بحث میکند که چگونه مفاهیمی بسیار سادهای همچون #یاختههای_عصبی میتوانند برای انسان #هوش به وجود بیاورند و آن را با رفتار یک #کلونی_مورچهها مقایسه میکند که رفتار هر مورچه بسیار ساده و #رفتار کلونی #شعورمند است.