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🗞Measurement errors in network data
M. E. J. Newman
🔗 https://arxiv.org/pdf/1703.07376
📌 ABSTRACT
The recent growth in interest in the physics and mathematics of networks has been driven in large part by the increasing availability of data describing the structure of networks ranging from the internet and the web to social and biological networks. It is a surprising feature of many empirical studies, however, that the data are reported without any estimate of their expected accuracy, even though it is clear that most do suffer from measurement error of various kinds. In this paper we develop the theory of measurement error for network data and give an expectation-maximization algorithm for estimating both false positive and false negative rates for edges in observed networks. We give an example application of our methods to social networks determined from proximity data. The methods we describe are general, and could be extended straightforwardly to cover different types of networks or errors.
M. E. J. Newman
🔗 https://arxiv.org/pdf/1703.07376
📌 ABSTRACT
The recent growth in interest in the physics and mathematics of networks has been driven in large part by the increasing availability of data describing the structure of networks ranging from the internet and the web to social and biological networks. It is a surprising feature of many empirical studies, however, that the data are reported without any estimate of their expected accuracy, even though it is clear that most do suffer from measurement error of various kinds. In this paper we develop the theory of measurement error for network data and give an expectation-maximization algorithm for estimating both false positive and false negative rates for edges in observed networks. We give an example application of our methods to social networks determined from proximity data. The methods we describe are general, and could be extended straightforwardly to cover different types of networks or errors.
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.