Synaptic Plasticity in Spiking Neural Networks (SP2INN) : A System Approach  

Oleh:

Mehrtash, N. ; Jung, D. ; Hellmich, H. H. ; Schoenauer, T. ; Lu, V. T. ; Klar, H.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 5 (2003), page 980-992.
Topik: neural network; synaptic plasticity; digital accelerator

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.9 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In this paper, we present a digital system called (SP2INN) for simulating very large - scale spiking neural networks (VLSNNs) comprising, e. g., 1000000 neurons with several million connections in total. SP2INN makes it possible to simulate VLSNN with features such as synaptic short term plasticity, long term plasticity as well as configurable connections. For such VLSNN the computation of the connectivity including the synapses is the main challenging task besides computing the neuron model. We describe the configurable neuron model of SP2INN, before we focus on the computation of the connectivity. Within SP2INN, connectivity parameters are stored in an external memory, while the actual connections are computed online based on defined connectivity rules. The communication between the SP2INN processor and the external memory represents a bottle - neck for the system performance. We show this problem is handled efficiently by introducing a tag scheme and a target - oriented addressing method. The SP2INN processor is described in a high - level hardware description language. We present its implementation in a 0.35 µm CMOS technology, but also discuss advantages and drawbacks of implementing it on a field programmable gate array.

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