4 papers
Abstraction in Neural Networks
Nancy Lynch
We show how brain networks, modeled as Spiking Neural Networks, can be viewed at different levels of abstraction. Lower levels include complications such as failures of neurons and…
Using Single-Neuron Representations for Hierarchical Concepts as Abstractions of Multi-Neuron Representations
Nancy Lynch
Brain networks exhibit complications such as noise, neuron failures, and partial synaptic connectivity. These can make it difficult to model and analyze their behavior. This paper…
Parallel Algorithms for Exact Enumeration of Deep Neural Network Activation Regions
Sabrina Drammis, Bowen Zheng, Karthik Srinivasan +3
A feedforward neural network using rectified linear units constructs a mapping from inputs to outputs by partitioning its input space into a set of convex regions where points with…
Multi-Neuron Representations of Hierarchical Concepts in Spiking Neural Networks
Nancy A. Lynch
We describe how hierarchical concepts can be represented in three types of layered neural networks. The aim is to support recognition of the concepts when partial information about…