Name
Abstract | ||
|---|---|---|
In this paper we evaluate how seminal biophysical Hodgkin Huxley model and hardware-efficient TrueNorth model of spiking neurons can be used to perform computations on spike rates in frequency domain. This side-by-side evaluation allows us to draw connections how fundamental arithmetic operations can be realized by means of spiking neurons and what assumptions should be made on input to guarantee the correctness of the computed result. We validated our approach in simulation and consider this work as a first step towards FPGA hardware implementation of neuromorphic accelerators based on spiking models. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59153-7_46 | ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT I |
Keywords | Field | DocType |
TrueNorth model,Hodgkin-Huxleymodel,Rate encoding,Arithmetic operations,Simulations | Frequency domain,Computer science,Correctness,Neuromorphic engineering,Field-programmable gate array,Computer hardware,TrueNorth,Computation,Hodgkin–Huxley model | Conference |
Volume | ISSN | Citations |
10305 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konstantin Selyunin | 1 | 26 | 2.84 |
| Ramin M. Hasani | 2 | 14 | 10.88 |
| Denise Ratasich | 3 | 12 | 3.36 |
| Ezio Bartocci | 4 | 733 | 57.55 |
| Radu Grosu | 5 | 1011 | 97.48 |