A high-throughput system for automated training combined with continuous long-term neural recordings in rodents

Addressing the neural mechanisms underlying complex learned behaviors requires training animals in well-controlled tasks and concurrently measuring neural activity in their brains, an often time-consuming and labor-intensive process that can severely limit the feasibility of such studies. To overcome this constraint, we developed a fully computer-controlled general purpose system for high-throughput training of rodents. By standardizing and automating the implementation of predefined training protocols within the animal’s home-cage our system dramatically reduces the efforts involved in animal training while also removing human errors and biases from the process. We deployed this system to train rats in a variety of sensorimotor tasks, achieving learning rates comparable to existing, but more laborious, methods. By incrementally and systematically increasing the difficulty of the task over weeks of training, rats were able to master motor tasks that, in complexity and structure, resemble ones used in primate studies of motor sequence learning. We also developed a low-cost system that can be attached to the home-cages for recording neural activity continuously in an unsupervised fashion for the entire months-long training process. Our system allows long-term tethering of animals and is designed for recording and processing tens of terabytes of raw data at very high speeds. We developed a novel spike-sorting algorithm that allows us to track the activity of many simultaneously recorded single neurons for weeks despite large gradual changes in their spike waveforms. This is done with minimal human input enabling, for the first time, the identification of almost every single spike from a single neuron over many weeks of training. We used these systems to record from the motor cortex of rats as they learned to perform a sequence of highly stereotyped movements. We found that neural activity in the motor cortex was exquisitely correlated with the behavior. Surprisingly, the pattern of neural activity in the motor cortex was similar before and after learning despite the fact that motor cortex is required to learn the task, but not to perform it once it has been acquired.Medical Science

A Fully Automated High-Throughput Training System for Rodents

Addressing the neural mechanisms underlying complex learned behaviors requires training animals in well-controlled tasks, an often time-consuming and labor-intensive process that can severely limit the feasibility of such studies. To overcome this constraint, we developed a fully computer-controlled general purpose system for high-throughput training of rodents. By standardizing and automating the implementation of predefined training protocols within the animal’s home-cage our system dramatically reduces the efforts involved in animal training while also removing human errors and biases from the process. We deployed this system to train rats in a variety of sensorimotor tasks, achieving learning rates comparable to existing, but more laborious, methods. By incrementally and systematically increasing the difficulty of the task over weeks of training, rats were able to master motor tasks that, in complexity and structure, resemble ones used in primate studies of motor sequence learning. By enabling fully automated training of rodents in a home-cage setting this low-cost and modular system increases the utility of rodents for studying the neural underpinnings of a variety of complex behaviors

Complete automation of a complex multi-step training protocol for a version of the center-out movement task (see Methods).

<p><b>A</b>. Training session structure. Animals were trained during six nightly 30-minute training sessions. The density plots show the distribution of joystick presses for four representative animals in their third week of training. ‘Free’ water is only available to rats earning less than a minimal amount of water during the nightly training session. <b>B</b>. Thirty rats were trained to perform the center-out movement task in three successive stages, each with multiple sub-stages (see Methods S1 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083171#pone.0083171.s003" target="_blank">File S1</a> for details). Stage 1: touching the joystick for a reward tone and subsequently licking at the water spout to initiate water reward delivery. Stage 2: moving the joystick down on cue. Stage 3: moving the joystick left and right. <b>C</b>. Stage and sub-stage completion times for each rat. Six rats (indicated with asterisks) were dropped from the study due to poor learning. Inset shows the mean (and standard error) of the number of completed sub-stages as a function of time. <b>D</b>. Time needed to complete one stage vs. another for the 24 successful rats. </p

Fully automated live-in home-cage training is comparable to existing methods in terms of learning and performance.

<p><b>A</b>. Rats were trained to spontaneously press a lever twice with a 700 ms delay between presses in an individually housed live-in training paradigm (red, n = 24 rats) or in a socially housed setting in which they were transferred to the behavior apparatus for daily training sessions (blue, n = 13 rats). <b>B</b>. Motivation as measured by the number of trials per day over time. <b>C</b>. Learning performance as measured by the fraction of correct trials, defined as trials within 30% of the 700 ms target inter-press interval, over time. Shaded regions in B and C represent standard error across animals.</p

Automated training of memory guided action sequences.

<p><b>A</b>. Structure of the behavioral task. An experimental block starts with a visually guided trial (left), in which the center LED indicates trial initiation. Upon moving the joystick down, the left (right) LED comes on. After a successful left (right) movement the LED turns off and the joystick is moved back to the center. A second movement is then cued by the right (left) LED. Upon moving the joystick right (left) a water reward is delivered. Any erroneous movement results in a timeout. After two consecutive correct trials, directional cues are not given and the movement sequence has to be performed from memory (right). After two consecutive correct memory guided trials (or ten total trials – an incomplete block), the next block commences with a new sequence. <b>B</b>. A sample run of 7 consecutive blocks from one animal. Each row represents one trial, with the sequence of movements color coded as in ‘A’. Left column denotes the target sequence (L-left movement; R-right movement). Shaded trials denote memory guided trials. Blocks denoted with asterisk correspond to perfect performance. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083171#pone.0083171.s002" target="_blank">Video S2</a> shows experimental blocks 5-7. <b>C</b>-<b>D</b>. Aggregate performance of 4 rats trained in the task as measured by the fraction of completed blocks (<b>C</b>) and the number of memory guided trials until completion (<b>D</b>). Performance is compared to simulated chance (error-bars denote 95% confidence level). Data from 679 blocks for Rat 1, 647 blocks for Rat 2, 472 blocks for Rat 3 and 392 blocks for Rat 4.</p

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