Synthetic tetrode recording dataset with spike-waveform drift

<p><strong>Introduction</strong></p> <p>This synthetic ground-truth dataset accurately models long-term, continuous extracellular tetrode recordings from the rodent brain over a time-period of 256 hours. Each "recording" comprises spiking of 8 distinct single-units with firing rates ranging from 0.1 - 6 Hz, superimposed on background multi-unit spiking activity at 20 Hz. The recording sampling rate is 30 kHz. Single-unit spike amplitudes drift over a range of 100 to 400 $\mu V$ based on the drift we observe in our own long-term recordings from the rodent motor cortex and striatum. For more details, please see our paper "Automated long-term recording and analysis of neural activity in behaving animals" ( https://doi.org/10.1101/033266).</p> <p>These recordings can be used to test the accuracy of spike-sorting algorithms when clustering non-stationary spike waveform data, such as our own Fast Automated Spike Tracker (FAST) outlined in our paper and available at https://github.com/Olveczky-Lab/FAST.</p> <p> </p> <p><strong>Dataset</strong></p> <p>Due to size restrictions, we provide here 1 sample tetrode of the full 6 tetrode dataset. Please contact us (https://olveczkylab.oeb.harvard.edu/about) if you require access to the other 5 synthetic tetrode recordings.</p> <p> </p> <p><strong>Instructions</strong></p> <p>The dataset comprises spike times and spike waveform snippets extracted a continuous synthetic tetrode recording. Provided are...</p> <ul> <li>A <strong>SpikeTimes</strong> file with a list of sample numbers for detected events (spikes) at <em>uint64</em> precision.</li> <li>A <strong>Spikes</strong> file with the waveforms of the detected events in <em>int16</em> precision. Each event waveform comprises <em>4 channels X 64 samples</em> 16-bit words arranged in the order [Ch0-Sample0, Ch1-Sample0, Ch2-Sample0, Ch3-Sample0, Ch0-Sample1, etc.]. To convert to units of voltage, change type to double precision and multiply by 1.95e-7.</li> <li>A <strong>SnippeterSettings.xml</strong> file with snippeting parameters (this is auto-generated by the FAST snippeting algorithm).</li> <li>A <strong>dataset_params.mat</strong> MATLAB data file containing the simulation parameters. The most important variables in the mat file are <em>sp</em> which contains a list of true spike-times (in samples @ 30 kHz) for all single-units in the dataset, and <em>sp_u</em> which specifies which unit (1-8) each spike originates from. Spike-times are generated by a homogenous Poisson process with firing rate specified for each unit by the variable <em>uFRs</em> and an absolute refractory period of 2 ms. The variable <em>d_Amps</em> specifies the amplitude of each single-unit spikes. The basic spike-waveform shape of each unit is provided in the variable <em>uWVs</em>. The spike-times and identity of background (multi-unit) spikes are specified in <em>b_sp</em> and <em>b_sp_u</em>. </li> </ul

Automated long-term recording and analysis of neural activity in behaving animals

Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons in experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics across different behavioral states. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving rodents. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals

Non-redundant odor coding by sister mitral cells revealed by light addressable glomeruli in the mouse

Sensory inputs frequently converge on the brain in a spatially organized manner, often with overlapping inputs to multiple target neurons. Whether the responses of target neurons with common inputs become decorrelated depends on the contribution of local circuit interactions. We addressed this issue in the olfactory system using newly generated transgenic mice that express channelrhodopsin-2 in all of the olfactory sensory neurons. By selectively stimulating individual glomeruli with light, we identified mitral/tufted cells that receive common input (sister cells). Sister cells had highly correlated responses to odors, as measured by average spike rates, but their spike timing in relation to respiration was differentially altered. In contrast, non-sister cells correlated poorly on both of these measures. We suggest that sister mitral/tufted cells carry two different channels of information: average activity representing shared glomerular input and phase-specific information that refines odor representations and is substantially independent for sister cells