Real-Time Neuron Detection and Neural Signal Extraction Platform for Miniature Calcium Imaging

Jump-GRS: a multi-phase approach to structured pruning of neural networks for neural decoding

Objective.Neural decoding, an important area of neural engineering, helps to link neural activity to behavior. Deep neural networks (DNNs), which are becoming increasingly popular in many application fields of machine learning, show promising performance in neural decoding compared to traditional neural decoding methods. Various neural decoding applications, such as brain computer interface applications, require both high decoding accuracy and real-time decoding speed. Pruning methods are used to produce compact DNN models for faster computational speed. Greedy inter-layer order with Random Selection (GRS) is a recently-designed structured pruning method that derives compact DNN models for calcium-imaging-based neural decoding. Although GRS has advantages in terms of detailed structure analysis and consideration of both learned information and model structure during the pruning process, the method is very computationally intensive, and is not feasible when large-scale DNN models need to be pruned within typical constraints on time and computational resources. Large-scale DNN models arise in neural decoding when large numbers of neurons are involved. In this paper, we build on GRS to develop a new structured pruning algorithm called jump GRS (JGRS) that is designed to efficiently compress large-scale DNN models.Approach.On top of GRS, JGRS implements a 'jump mechanism', which bypasses retraining intermediate models when model accuracy is relatively less sensitive to pruning operations. Design of the jump mechanism is motivated by identifying different phases of the structured pruning process, where retraining can be done infrequently in earlier phases without sacrificing accuracy. The jump mechanism helps to significantly speed up execution of the pruning process and greatly enhance its scalability. We compare the pruning performance and speed of JGRS and GRS with extensive experiments in the context of neural decoding.Main results.Our results demonstrate that JGRS provides significantly faster pruning speed compared to GRS, and at the same time, JGRS provides pruned models that are similarly compact as those generated by GRS.Significance.In our experiments, we demonstrate that JGRS achieves on average 9%-20% more compressed models compared to GRS with 2-8 times faster speed (less time required for pruning) across four different initial models on a relevant dataset for neural data analysis.

A parameter-optimization framework for neural decoding systems

Real-time neuron detection and neural activity extraction are critical components of real-time neural decoding. They are modeled effectively in dataflow graphs. However, these graphs and the components within them in general have many parameters, including hyper-parameters associated with machine learning sub-systems. The dataflow graph parameters induce a complex design space, where alternative configurations (design points) provide different trade-offs involving key operational metrics including accuracy and time-efficiency. In this paper, we propose a novel optimization framework that automatically configures the parameters in different neural decoders. The proposed optimization framework is evaluated in depth through two case studies. Significant performance improvement in terms of accuracy and efficiency is observed in both case studies compared to the manual parameter optimization that was associated with the published results of those case studies. Additionally, we investigate the application of efficient multi-threading strategies to speed-up the running time of our parameter optimization framework. Our proposed optimization framework enables efficient and effective estimation of parameters, which leads to more powerful neural decoding capabilities and allows researchers to experiment more easily with alternative decoding models.

Open-Source Software for Real-time Calcium Imaging and Synchronized Neuron Firing Detection

We developed Carignan, a real-time calcium imaging software that can automatically detect activity patterns of neurons. Carignan can activate an external device when synchronized neural activity is detected in calcium imaging obtained by a one-photon (1p) miniscope. Combined with optogenetics, our software enables closed-loop experiments for investigating functions of specific types of neurons in the brain. In addition to making existing pattern detection algorithms run in real-time seamlessly, we developed a new classification module that distinguishes neurons from false-positives using deep learning. We used a combination of convolutional and recurrent neural networks to incorporate both spatial and temporal features in activity patterns. Our method performed better than existing neuron detection methods for false-positive neuron detection in terms of the F1 score. Using Carignan, experimenters can activate or suppress a group of neurons when specific neural activity is observed. Because the system uses a 1p miniscope, it can be used on the brain of a freely-moving animal, making it applicable to a wide range of experimental paradigms.