Modularity Facilitates Classification Performance of Spiking Neural Networks for Decoding Cortical Spike Trains.

After the introduction of recurrence, an important property of the biological brain, spiking neural networks (SNNs) have achieved unprecedented classification performance. But they still cannot outperform many artificial neural networks. Modularity is another crucial feature of the biological brain. It remains unclear if modularity can also improve the performance of SNNs. To investigate this idea, we proposed the modular SNN, and compared its performance with a uniform SNN without modularity by employing them to classify cortical spike trains. For the first time, a significant improvement was found in our modular SNN. Further, we probed into the factors influencing the performance of the modular SNN and found: (a). The modular SNN outperformed the uniform SNN more significantly when the number of neurons in the networks increased; (b). The performance of the modular SNNs increased as the number of modules dropped. These preliminary but novel findings suggest that modularity may help develop better artificial intelligence and brain-machine interfaces. Also, the modular SNN may serve as a model for the study of neuronal spike synchrony.

Volitional Generation of Reproducible, Efficient Temporal Patterns.

One of the extraordinary characteristics of the biological brain is the low energy expense it requires to implement a variety of biological functions and intelligence as compared to the modern artificial intelligence (AI). Spike-based energy-efficient temporal codes have long been suggested as a contributor for the brain to run on low energy expense. Despite this code having been largely reported in the sensory cortex, whether this code can be implemented in other brain areas to serve broader functions and how it evolves throughout learning have remained unaddressed. In this study, we designed a novel brain-machine interface (BMI) paradigm. Two macaques could volitionally generate reproducible energy-efficient temporal patterns in the primary motor cortex (M1) by learning the BMI paradigm. Moreover, most neurons that were not directly assigned to control the BMI did not boost their excitability, and they demonstrated an overall energy-efficient manner in performing the task. Over the course of learning, we found that the firing rates and temporal precision of selected neurons co-evolved to generate the energy-efficient temporal patterns, suggesting that a cohesive rather than dissociable processing underlies the refinement of energy-efficient temporal patterns.