Spiking, Bursting, and Population Dynamics in a Network of Growth Transform Neurons
| Autor: | Ahana Gangopadhyay, Shantanu Chakrabartty |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
education.field_of_study
Quantitative Biology::Neurons and Cognition Computer Networks and Communications Computer science 020208 electrical & electronic engineering Population Theta model Biological neuron model 02 engineering and technology Computer Science Applications Support vector machine Bursting medicine.anatomical_structure Hyperplane Neuromorphic engineering Artificial Intelligence 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Neuron education Algorithm Software |
| Zdroj: | IEEE Transactions on Neural Networks and Learning Systems. 29:2379-2391 |
| ISSN: | 2162-2388 2162-237X |
| DOI: | 10.1109/tnnls.2017.2695171 |
| Popis: | This paper investigates the dynamical properties of a network of neurons, each of which implements an asynchronous mapping based on polynomial growth transforms. In the first part of this paper, we present a geometric approach for visualizing the dynamics of the network where each of the neurons traverses a trajectory in a dual optimization space, whereas the network itself traverses a trajectory in an equivalent primal optimization space. We show that as the network learns to solve basic classification tasks, different choices of primal-dual mapping produce unique but interpretable neural dynamics like noise shaping, spiking, and bursting. While the proposed framework is general enough, in this paper, we demonstrate its use for designing support vector machines (SVMs) that exhibit noise-shaping properties similar to those of $\Sigma \Delta $ modulators, and for designing SVMs that learn to encode information using spikes and bursts. It is demonstrated that the emergent switching, spiking, and burst dynamics produced by each neuron encodes its respective margin of separation from a classification hyperplane whose parameters are encoded by the network population dynamics. We believe that the proposed growth transform neuron model and the underlying geometric framework could serve as an important tool to connect well-established machine learning algorithms like SVMs to neuromorphic principles like spiking, bursting, population encoding, and noise shaping. |
| Databáze: | OpenAIRE |
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