Leveraging Spiking Deep Neural Networks to Understand the Neural Mechanisms Underlying Selective Attention
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Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacti ...
Neural networks have been traditionally considered robust in the sense that their precision degrades gracefully with the failure of neurons and can be compensated by additional learning phases. Nevertheless, critical applications for which neural networks ...
— A novel neuron circuit using a Cu/Ti/Al2O3-based conductive-bridge random access memory (CBRAM) device for hardware neural networks that utilize nonvolatile memories as synaptic weights is introduced. The neuronal operations are designed and proved using ...
Institute of Electrical and Electronics Engineers2016
Imitation is the ability to recognize, learn and reproduce the actions of others. In addition to facilitating the transmission of knowledge and skills, it has been suggested that this fundamental cognitive capacity is at the origin of other human faculties ...
Synaptic plasticity, the putative basis of learning and memory formation, manifests in various forms and across different timescales. Here we show that the interaction of Hebbian homosynaptic plasticity with rapid non-Hebbian heterosynaptic plasticity is, ...
Statistical models of neural activity are at the core of the field of modern computational neuroscience. The activity of single neurons has been modeled to successfully explain dependencies of neural dynamics to its own spiking history, to external stimuli ...
Contextual elements can strongly modulate visual performance. For example, performance deteriorates when a vernier is flanked by neighboring lines. On a neural level, such contextual modulation is often explained by local spatial interactions such as later ...
Association for Research in Vision and Ophthalmology2011
Attending to an object can strongly modulate the neural processing of this object. Using average EEG, it was shown that small differences in the focus of attention yield large and long lasting changes in brain dynamics (Plomp et al, 2009). Here, we show th ...
Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the expectation propagation algorithm, we are able to approximate the full poster ...
The complexity of processes occurring in the brain is an intriguing issue not just for scientists and medical doctors, but the humanity in general. The cortex ability to perceive and analyze an enormous amount of information in an instance of time, the par ...