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Found 9 papers, 0 papers with code
Learning Hierarchically-Structured Concepts II: Overlapping Concepts, and Networks With Feedback
We continue our study from Lynch and Mallmann-Trenn (Neural Networks, 2021), of how concepts that have hierarchical structure might be represented in brain-like neural networks, how these representations might be used to recognize the concepts, and how these representations might be learned.
A superconducting nanowire spiking element for neural networks
As the limits of traditional von Neumann computing come into view, the brain's ability to communicate vast quantities of information using low-power spikes has become an increasing source of inspiration for alternative architectures.
Learning Hierarchically Structured Concepts
Our main goal is to introduce a general framework for these tasks and prove formally how both (recognition and learning) can be achieved.
Winner-Take-All Computation in Spiking Neural Networks
We provide efficient constructions of WTA circuits in our stochastic spiking neural network model, as well as lower bounds in terms of the number of auxiliary neurons required to drive convergence to WTA in a given number of steps.
Integrating Temporal Information to Spatial Information in a Neural Circuit
We consider the problem of translating temporal information into spatial information in such networks, an important task that is carried out by actual brains.
A Basic Compositional Model for Spiking Neural Networks
We define two operators on SNNs: a $composition$ $operator$, which supports modeling of SNNs as combinations of smaller SNNs, and a $hiding$ $operator$, which reclassifies some output behavior of an SNN as internal.
Collaboratively Learning the Best Option, Using Bounded Memory
We say an individual learns the best option if eventually (as $t \to \infty$) it pulls only the arm with the highest average reward.
Neuro-RAM Unit with Applications to Similarity Testing and Compression in Spiking Neural Networks
Randomization allows us to solve this task with a very compact network, using $O \left (\frac{\sqrt{n}\log n}{\epsilon}\right)$ auxiliary neurons, which is sublinear in the input size.
Computational Tradeoffs in Biological Neural Networks: Self-Stabilizing Winner-Take-All Networks
In this paper, we focus on the important `winner-take-all' (WTA) problem, which is analogous to a neural leader election unit: a network consisting of $n$ input neurons and $n$ corresponding output neurons must converge to a state in which a single output corresponding to a firing input (the `winner') fires, while all other outputs remain silent.
