A mixed-signal implementation of a polychronous spiking neural network with delay adaptation
| Autor: | Jonathan Tapson, Tara Julia Hamilton, André van Schaik, Runchun Mark Wang |
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| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
polychronous spiking neural network
Computer science mixed-signal implementation analogue implementation lcsh:RC321-571 medicine Original Research Article Axon Field-programmable gate array lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry analog implementation Spiking neural network Digital electronics Artificial neural network Quantitative Biology::Neurons and Cognition business.industry General Neuroscience Mixed-signal integrated circuit medicine.anatomical_structure Neuromorphic engineering nervous system Neuron Artificial intelligence multiplexed neuron array business neuromorphic engineering Computer hardware Neuroscience |
| Zdroj: | Frontiers in Neuroscience, Vol 8 (2014) Frontiers in Neuroscience |
| Popis: | We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analogue and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analogue IC containing the analogue neuron array and the analogue axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analogue and digital axon arrays and the analogue and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analogue and digital circuits. |
| Databáze: | OpenAIRE |
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