A Supervised STDP-based Training Algorithm for Living Neural Networks
This work addresses the challenge of integrating biological neural networks into machine learning applications, representing an incremental step in bridging neuroscience and AI.
The paper tackles the problem of using living neural networks in vitro as computational elements for machine learning by proposing a supervised STDP-based training algorithm that accounts for neuron engineering constraints, achieving 74.7% accuracy on the MNIST handwritten digit recognition benchmark.
Neural networks have shown great potential in many applications like speech recognition, drug discovery, image classification, and object detection. Neural network models are inspired by biological neural networks, but they are optimized to perform machine learning tasks on digital computers. The proposed work explores the possibilities of using living neural networks in vitro as basic computational elements for machine learning applications. A new supervised STDP-based learning algorithm is proposed in this work, which considers neuron engineering constrains. A 74.7% accuracy is achieved on the MNIST benchmark for handwritten digit recognition.