Charles Rudolf Legéndy (Hungarian: Legéndy Károly Rudolf, born November 2, 1936), is a Hungarian-born American engineer, theoretical physicist, and neuroscientist.
Charles R. Legéndy | |
|---|---|
 C. R. Legéndy – photograph taken in 2008 | |
| Born | November 2, 1936 |
| Nationality | American |
| Occupation | Scientist |
| Known for | Physics: helicons – Neuroscience: Poisson surprise, brain capacity |
In physics, Legéndy is known for his theoretical work on the helicon phenomenon,[1] In neuroscience, he is known for introducing the "Poisson surprise" test used for burst detection in neuronal spike trains,[2][3] and for his calculations of brain capacity.[4]
Biography
editLegéndy began his university studies at the Budapest University of Technology (Műegyetem). After emigrating to the United States in 1956, he obtained a bachelor's degree in engineering physics from Princeton University (1959) and a PhD in theoretical physics from Cornell University (1964).
In his postdoctoral years, Legéndy switched from physics to neuroscience and took a series of research positions in the US and overseas, publishing both theoretical and experimental papers. In 1985 he lost his research funding and returned to engineering, working in aerospace design (ITT Avionics, Singer-Kearfott), and in database management, until his retirement (2001). In retirement, Legéndy joined the Psychology Department at Columbia University in an unpaid position as an adjunct researcher,[5] and (after a 26-year hiatus in publications) resumed work in theoretical neuroscience.
While in Germany, Legéndy met his future wife Annemarie; they married in 1977, and settled down to raise their children in New York City.
Physics: helicon waves
editAs a graduate student at Cornell, Legéndy joined an experimental team which had just discovered an electromagnetic resonance phenomenon at unexpectedly low frequencies (32 Hz and below) inside a metal sample in a magnetic field. Legéndy showed that the resonance was the manifestation of solid-state plasma wave propagation inside the metal,[6] mathematically comparable to radio whistlers [7] in the ionosphere.
Neuroscience: general design of data processing in the brain
editLegéndy's theory of the brain develops certain additions to the Hebbian theory; in particular, to the theory of cell assemblies.[8] He estimated the maximum number of stable cell assemblies that could fit into the network. Under the most optimistic set of assumptions available at the time, the brain model could contain up to 109 "mental entities," the figure later used by Hebb[4] in illustrating the large capacity of the brain. (Recent data shows that the estimate must be greatly reduced; and Legéndy now believes that the large capacity of brains cannot be explained in terms of synaptic weights alone; synapse-based memory must be supplemented by a molecular backup system.[9])
Legéndy's work on brain circuitry and ignitions led to the “Poisson surprise” analysis of neuronal spike trains[10] a method of burst detection.[3][2] (Poisson surprise is defined as –log P, where P is the probability of a spike pattern under the assumed baseline distribution of spiking — in this case the Poisson distribution.) The test can quickly detect and quantify bursts in neuronal spike trains; and it is used, for instance, as a way to detect loss of functionality in certain brain regions. The critique of the Poisson surprise test[3][2] is that actual spike distributions differ from the Poisson distribution to varying degrees (for instance for interspike intervals smaller than the refractory period); the critique generally suggests changing the underlying probability distribution, not the principle of surprise-based burst detection. The concept of surprising events in neuronal spike trains was originally introduced on theoretical grounds,[11] in view of the large number of unplanned contacts present in the brain which necessitate statistical methods for distinguishing the useful signals from random firing. (The connection to the cell assembly theory is through the ignition phenomenon – ignitions create surprising events at distant locations.[12])
References
edit- ^ Boswell, R. W. and Chen F. F. (December 1997) "Helicons – the early years". IEEE Transactions on Plasma Science 25 (6): 1229–1244. DOI: 10.1109/27.650898.
- ^ a b c Gourévitch, B. and Eggermont, J. J. (2007) “A nonparametric approach for detection of bursts in spike trains.” Journal of Neuroscience Methods 160:349-358. DOI:10.1016/j.jneumeth.2006.09.024
- ^ a b c Cotterill, E. and Eglen, S. J. (February 2018) “Burst detection methods”. q-bio.NC (Quantitative Biology - Neurons and Cognition) arXiv:1802.01287v1
- ^ a b Hebb, D. O. (December 1976) "Physiological learning theory". Journal of Abnormal Child Psychology 4 (4): 309-314. DOI: 10.1007/BF00922529, PMID 187637, print ISSN 0091-0627, online ISSN 1573-2835.
- ^ "Charles Legendy | Department of Psychology". psychology.columbia.edu. Archived from the original on 2016-12-20.
- ^ Bowers, R., Legéndy, C. R., and Rose, F. E. (November 1961) "Oscillatory galvanomagnetic effect in metallic Sodium". Physical Review Letters 7 (9): 339-341. DOI: 10.1103/PhysRevLett.7.339.
- ^ Storey, L. R. O. (9 July 1953) "An investigation of whistling atmospherics". Philosophical Transactions of the Royal Society A. 246 (908): 113. DOI: 10.1098/rsta.1953.0011.
- ^ Palm, G. (February 1981) “Towards a theory of cell assemblies” Biological Cybernetics 39 (3): 181-194. DOI: 10.1007/BF00342771
- ^ Legéndy, C. R. (August 2016) "Synaptic and extrasynaptic traces of long-term memory: the ID molecule theory". Reviews in the Neurosciences 27 (6): 575-598. DOI: 10.1515/revneuro-2016-0015.
- ^ Legéndy, C. R. and Salcman M. (April 1985) "Bursts and recurrences of bursts in the spike trains of spontaneously active striate cortex neurons". Journal of Neurophysiology 53 (4): 926-939. DOI: 10.1152/jn.1985.53.4.926. PMID 3998798.
- ^ Palm, G. (November 1981) “Evidence, information, and surprise” Biological Cybernetics 42 (1): 57-68. PMID 7326283, DOI: 10.1007/BF00335160
- ^ Legéndy, C. R. (2009) Circuits in the Brain: A Model of Shape Processing in the Primary Visual Cortex. New York: Springer, 2009. DOI: 10.1007/978-0-387-88849-1, ISBN 978-0-387-88848-4, e-ISBN 978-0-387-88849-1.