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Analysis of the dynamics of temporal relationships of neural activities using optical imaging data

Lookup NU author(s): Dr Jannetta Steyn, Dr Peter Andras



The dynamics of the temporal relationship of the activities of neurons forming neural circuits is critically important for the flexible and adaptive delivery of the functionality of these circuits (Harris-Warrick et al. 1992; Galán et al. 2004; Hill et al 2012; Bruno et al 2015). For example, switching between synchronised and de-synchronised patterns of activity of neurons forming functional circuits in the hippocampus plays a fundamental role in memory formation, maintenance and recall in vertebrate brains (Axmacher et al. 2006; Robbe et al. 2006). In the case of epilepsy a switch to excessive synchronisation of neural activities breaks down the functionality of many neural circuits and neural systems formed by them (Muldoon et al. 2013; Engel et al. 2013). Recently, it has been shown that the fine timing of inputs to different parts of the dendritic tree of neurons in the visual cortex of mammals determines the spatio-temporal preferences of these parts of the neuron and their combination determines the actual receptive field of the neuron (Chen et al. 2013). In general, both relatively simple and complex changes in the temporal relationship of neural activities can play a critical role in the delivery of the functionality of neural circuits. Until relatively recently the recording of many synaptically connected neurons at individual neuron resolution was not possible in the context of physiologically realistic conditions – e.g. the use of individual micro-electrodes implies significant spatial constraints limiting the number of recordable neurons (Miller 1987). While multi-electrode arrays allow recording of many individual neurons in artificially created cell culture (Potter and DeMarse 2001; Spira and Hai 2013), the activity of neurons in such context is not truly comparable to the activity of neurons in real physiological conditions. In other settings when multi-electrode array or multiple multi-electrodes (e.g. tetrodes) are used to record many neurons form brains or brain slices in physiological conditions the connectivity between the recorded neurons is usually not known (Guitchounts et al. 2013; Scholvin et al. 2015; Santos et al. 2012). The impact of this is that a large part of the work on neuron resolution dynamics of neural circuits remained mostly theoretical (Schneidman et al. 2006; Shlens et al. 2006; Paninski et al. 2010). Currently used techniques of optical recording of neural activity using voltage-sensitive dyes and calcium dyes allow high spatio-temporal resolution recording of the activity of many neurons, making possible the study of the dynamics of temporal relationships of neural activities in biological neural circuits (Canepari and Zecevic 2010). While many applications of these techniques are used to record many neurons that are not necessarily directly coupled synaptically (Mukamel et al. 2009; Rotschild et al. 2010), it has been shown that these methods can also be applied successfully to a range of biological neural systems to record the activity of many synaptically coupled neurons simultaneously. These techniques have been applied to analyse the functionality of neurons in leech ganglia (Briggman et al. 2010), to study the dynamical assignment of functional roles to neurons in snail ganglia (Hill et al 2012; Bruno et al 2015), to record almost simultaneously the activity of all neurons in the brain of the zebra fish embryo (Ahrens et al. 2012), to analyse the activity of neurons in intestinal neural ganglia in guinea pigs (Obaid et al. 1999), and to study the activity of synaptically coupled neurons in the stomatogastric ganglion of crabs (Stein et al 2011; Städele et al. 2012). However, it should be noted that usually the recorded data is quite noisy, potentially making its analysis difficult. Here we address the issue of analysis of such optical imaging data for the purpose of understanding the dynamics of temporal relationship of the activities of individual neurons. Our method relies on the identification of a few key features of the activity patterns of individual neurons, which can be estimated sufficiently robustly from the recorded noisy data. For example, in the case of neurons which spike during depolarisation plateaus the numerically calculated local maximum upward slope and minimum downward slope points of the recorded activity define an approximation of the beginning and the end of the depolarisation plateau during which the neuron is most active. Having the timings of the identified key features of the neural activity patterns we can use these to estimate the changes in the temporal relationships of neural activities and thus the dynamics of the temporal relationships between neural activities. We apply the proposed data analysis method to neurons recorded in the crab stomatogastric ganglion. According to earlier results about the impact of dopamine on individual pyloric constrictor (PY) neurons it can be expected that dopamine exposure causes the de-synchronisation of the activity of these neurons (Johnson et al. 1993; Johnson et al. 1994; Ayali et al. 1998). We analysed and quantified the impact of dopamine on the temporal relationship between the activity patterns of PY neurons. Our results show that as expected there is a statistically significantly measurable de-synchronisation effect in the case of the considered PY neurons in general. The rest of the paper is structured as follows. First we review the relevant background. Then we describe the proposed methodology in detail. Next we describe the application of the methodology to voltage-sensitive dye recording of the activity of PY neurons in the crab stomatogastric ganglion. Finally we discuss the implications of the presented work and draw the conclusions.

Publication metadata

Author(s): Steyn J, Andras P

Publication type: Report

Publication status: Published

Series Title: School of Computing Science Technical Report Series

Year: 2015

Pages: 12

Report Number: 1477

Institution: School of Computing Science, University of Newcastle upon Tyne

Place Published: Newcastle upon Tyne