Homeostatic plasticity improves signal propagation in continuous time recurrent neural networks

Continuous-time recurrent neural networks (CTRNNs) are potentially an excellent substrate for the generation of adaptive behaviour in artificial autonomous agents. However, node saturation effects in these networks can leave them insensitive to input and stop signals from propagating. Node saturation is related to the problems of hyper-excitation and quiescence in biological nervous systems, which are thought to be avoided through the existence of homeostatic plastic mechanisms. Analogous mechanisms are here implemented in a variety of CTRNN architectures and are shown to increase node sensitivity and improve signal propagation, with implications for robotics. These results lend support to the view that homeostatic plasticity may prevent quiescence and hyper-excitation in biological nervous systems

Investigating Machine Learning Techniques for Gesture Recognition with Low-Cost Capacitive Sensing Arrays

Machine learning has proven to be an effective tool for forming models to make predictions based on sample data. Supervised learning, a subset of machine learning, can be used to map input data to output labels based on pre-existing paired data. Datasets for machine learning can be created from many different sources and vary in complexity, with popular datasets including the MNIST handwritten dataset and CIFAR10 image dataset. The focus of this thesis is to test and validate multiple machine learning models for accurately classifying gestures performed on a low-cost capacitive sensing array. Multiple neural networks are trained using gesture datasets obtained from the capacitance board. In this paper, I train and compare different machine learning models on recognizing gesture datasets. Learning hyperparameters are also adjusted for results. Two datasets are used for the training: one containing simple gestures and another containing more complicated gestures. Accuracy and loss for the models are calculated and compared to determine which models excel at recognizing performed gestures

Neuro-RAM Unit with Applications to Similarity Testing and Compression in Spiking Neural Networks

We study distributed algorithms implemented in a simplified biologically inspired model for stochastic spiking neural networks. We focus on tradeoffs between computation time and network complexity, along with the role of noise and randomness in efficient neural computation. It is widely accepted that neural spike responses, and neural computation in general, is inherently stochastic. In recent work, we explored how this stochasticity could be leveraged to solve the \u27winner-take-all\u27 leader election task. Here, we focus on using randomness in neural algorithms for similarity testing and compression. In the most basic setting, given two n-length patterns of firing neurons, we wish to distinguish if the patterns are equal or epsilon-far from equal. Randomization allows us to solve this task with a very compact network, using O((sqrt(n) log n)/epsilon) auxiliary neurons, which is sublinear in the input size. At the heart of our solution is the design of a t-round neural random access memory, or indexing network, which we call a neuro-RAM. This module can be implemented with O(n/t) auxiliary neurons and is useful in many applications beyond similarity testing - e.g., we discuss its application to compression via random projection. Using a VC dimension-based argument, we show that the tradeoff between runtime and network size in our neuro-RAM is nearly optimal. To the best of our knowledge, we are the first to apply these techniques to stochastic spiking networks. Our result has several implications - since our neuro-RAM can be implemented with deterministic threshold gates, it demonstrates that, in contrast to similarity testing, randomness does not provide significant computational advantages for this problem. It also establishes a separation between our networks, which spike with a sigmoidal probability function, and well-studied deterministic sigmoidal networks, whose gates output real number values, and which can implement a neuro-RAM much more efficiently