Abstract
Available tools for recording neuronal activity are limited and reductive due to massive data arising from high-frequency measurements. We have developed a method that utilizes variance within the physiological activity and includes all data points per measurement. Data is expressed geometrically in a physiologically meaningful manner, to represent a precise and detailed view of the recorded neural activity. The recorded raw data from any pair of electrodes was plotted and following a covariance calculation, an eigenvalues and chi-square distribution were used to define the ellipse which bounds 95% of the raw data. We validated our method by correlating specific behavioral observation and physiological activity with behavioral tasks that require similar body movement but potentially involve significantly different neuronal activity. Graphical representation of telemetrically recorded data generates a scatter plot with distinct elliptic geometrical properties that clearly and significantly correlated with behavior. Our reproducible approach improves on existing methods by allowing a dynamic, accurate and comprehensive correlate using an intuitive output. Long-term, it may serve as the basis for advanced machine learning applications and animal-based artificial intelligence models aimed at predicting or characterizing behavior.
Highlights
Available tools for recording neuronal activity are limited and reductive due to massive data arising from high-frequency measurements
We examined the possible association between the neural activity recorded from any pair of electrodes within the Putamen and the Motor cortex, pustulating they reflect both reward processing and the motor actuation, respectively[11]
Our newly developed method uses geometrical analysis of neural activity to successfully discriminate between the contingent different behaviors and examine them in a meaningful, reproducible manner
Summary
Available tools for recording neuronal activity are limited and reductive due to massive data arising from high-frequency measurements. Data is expressed geometrically in a physiologically meaningful manner, to represent a precise and detailed view of the recorded neural activity. The spectrogram approach is representing each neuron separately and missing the inter-neuron-correlation Another limitation of current approaches is the lack of live synchronization - the ability to synchronize the entirely recorded neural activity with behavior as it occurs. We have developed a method that overcomes computational challenges encountered by current approaches This method avoids data filtration or spike sorting, comprises all data points for each measurement and relies on the variance observed within the physiological activity. The main aim of our study is to seek the correlation between these two regions while performing ‘low’ vs. ‘high’ selective attention tasks
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