Multi-Neuron Representations of Hierarchical Concepts in Spiking Neural Networks
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 the concepts is presented, and also when some of the neurons in the network might fail. Our failure model involves initial random failures. The three types of networks are: feed-forward networks with high connectivity, feed-forward networks with low connectivity, and layered networks with low connectivity and with both forward edges and "lateral" edges within layers. In order to achieve fault-tolerance, the representations all use multiple representative neurons for each concept. We show how recognition can work in all three of these settings, and quantify how the probability of correct recognition depends on several parameters, including the number of representatives and the neuron failure probability. We also discuss how these representations might be learned, in all three types of networks. For the feed-forward networks, the learning algorithms are similar to ones used in [4], whereas for networks with lateral edges, the algorithms are generally inspired by work on the assembly calculus [3, 6, 7].
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Reinforcement learning with a network of spiking agents
Neuroscientific theory suggests that dopaminergic neurons broadcast global reward prediction errors to large areas of the brain influencing the synaptic plasticity of the neurons in those regions. We build on this theory…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Spiking Recurrent Networks as a Model to Probe Neuronal Timescales Specific to Working Memory
Cortical neurons process and integrate information on multiple timescales. In addition, these timescales or temporal receptive fields display functional and hierarchical organization. For instance, areas important for wo…
Rare Events, Real Signals: Functional Ensembles as Units of Computation in Deep Spiking Networks
We investigate how internal representations emerge across hierarchical processing systems by introducing a neuroscience-inspired framework for analyzing deep spiking neural networks (SNN) through the lens of functional c…
HINT: Hierarchical Neuron Concept Explainer
To interpret deep networks, one main approach is to associate neurons with human-understandable concepts. However, existing methods often ignore the inherent relationships of different concepts (e.g., dog and cat both be…
Object LocalizationWeakly-Supervised Object LocalizationExploring the Potentials of Spiking Neural Networks for Image Deraining
Biologically plausible and energy-efficient frameworks such as Spiking Neural Networks (SNNs) have not been sufficiently explored in low-level vision tasks. Taking image deraining as an example, this study addresses the …
Representation Learning