Quantifying periodic activity in central pattern generators: the crayfish swimmeret

Olsen, O.H. and Murray-Smith, D. (1993) Quantifying periodic activity in central pattern generators: the crayfish swimmeret. Journal of Neuroscience Methods, 50(1), pp. 25-35. (doi: 10.1016/0165-0270(93)90053-T)

Abstract

Experimental recordings from neurones are generally dificult to quantify. Spike patterns and membrane potential cycles are often compared qualitatively and are hence limited by human resolution and judgement. This paper describes a statistical method for quantifying individual spike patterns and membrane potential fluctuations in oscillating systems, allowing quantification of successive 'individual' bursts. The neuronal activities of the network can therefore be subject to more extensive analyses and comparisons. Principal-component analysis was used to extract the principal components from a sequence of bursts recorded in vitro from the crayfish (Pacifasticus leniusculus) swimmeret. The principal component coefficients were correlated with intuitive biological concepts such as burst displacement, number of spikes in a burst, and burst width. The bursting of both interneurones and motor neurones were found to be modulated by some unidentified oscillator that displaced the bursts in time. The amplitude of the time displacements was significant i.e. the order of the bursting period (1.5 s), and the period of the oscillations was approximately 200 bursts. The strength of the method is that this feature was detected without any prior suspicion of its existence.

• Item Type: Articles
• Keywords: Central pattern generator, crayfish swimmeret, principal component analysis, spike pattern analysis, descriptive statistics
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Murray-Smith, Professor David
• Authors: Olsen, O.H., and Murray-Smith, D.
• Subjects: Q Science > QP Physiology; T Technology > TK Electrical engineering. Electronics Nuclear engineering
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Journal of Neuroscience Methods
• ISSN: 0165-0270
• Published Online: 14 March 2003