A Detailed and Fast Model of Extracellular Recordings

Abstract

We present a novel method to generate realistic simulations of extracellular recordings. The simulations were obtained by superimposing the activity of neurons placed randomly in a cube of brain tissue. Detailed models of individual neurons were used to reproduce the extracellular action potentials of close-by neurons. To reduce the computational load, the contributions of neurons further away were simulated using previously recorded spikes with their amplitude normalized by the distance to the recording electrode. For making the simulations more realistic, we also considered a model of a finite-size electrode by averaging the potential along the electrode surface and modeling the electrode-tissue interface with a capacitive filter. This model allowed studying the effect of the electrode diameter on the quality of the recordings and how it affects the number of identified neurons after spike sorting. Given that not all neurons are active at a time, we also generated simulations with different ratios of active neurons and estimated the ratio that matches the signal-to-noise values observed in real data. Finally, we used the model to simulate tetrode recordings.

Improving data quality in neuronal population recordings

“…For single sites, a smaller ratio of the amplitudes of nearby neurons to those of the distant cells contributing to the background activity leads to a lower signal-to-noise ratio (SNR). The optimal design of an electrode thus depends on the techniques that are used to process the data: with limited spike sorting algorithms, it might be preferable to have small electrodes with large SNR, whereas with more advanced algorithms and multisite probes, larger, low impedance electrodes can increase the yield of identified neurons 15, 16 .…”

Spatial Information in Large-Scale Neural Recordings

“…For example, for optical recordings we assumed scattering through homogenous tissue, and for electrical recordings we ignored the filtering properties of electrodes. There exists a rich literature of modeling optical and electrical systems that could allow better models of recording modalities (e.g., Theer and Denk, 2006;CamuÃ-Mesa and Quiroga, 2013); incorporating these models into the framework may alleviate some of the concerns over oversimplification, and may even provide a framework for validating those models.…”

Wavelet methodology to improve single unit isolation in primary motor cortex cells

“…The Neurocube (Camunas-Mesa and Quiroga, 2013) was used to create a realistic simulation of SUA in the brain. The simulation creates a custom cube of tissue which is randomly populated by neurons, each with its own firing frequency and waveform.…”

“…Identifying the mother wavelet and thresholding schemes that yielded the best results can help develop a more effective approach for the processing of neurophysiological data. Two data sets were used in this research: simulated single unit activity (SUA) recordings using Neurocube (Camunas-Mesa and Quiroga, 2013), and real SUA recorded from an awake behaving primate. Statistical measures were used to evaluate both signal and clustering quality.…”