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| 001 | 893117 | ||
| 005 | 20240313103112.0 | ||
| 024 | 7 | _ | |a 10.3389/fninf.2021.609147 |2 doi |
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| 100 | 1 | _ | |a Stapmanns, Jonas |0 P:(DE-Juel1)171475 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Event-Based Update of Synapses in Voltage-Based Learning Rules |
| 260 | _ | _ | |a Lausanne |c 2021 |b Frontiers Research Foundation |
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| 520 | _ | _ | |a Due to the point-like nature of neuronal spiking, efficient neural network simulators often employ event-based simulation schemes for synapses. Yet many types of synaptic plasticity rely on the membrane potential of the postsynaptic cell as a third factor in addition to pre- and postsynaptic spike times. In some learning rules membrane potentials not only influence synaptic weight changes at the time points of spike events but in a continuous manner. In these cases, synapses therefore require information on the full time course of membrane potentials to update their strength which a priori suggests a continuous update in a time-driven manner. The latter hinders scaling of simulations to realistic cortical network sizes and relevant time scales for learning. Here, we derive two efficient algorithms for archiving postsynaptic membrane potentials, both compatible with modern simulation engines based on event-based synapse updates. We theoretically contrast the two algorithms with a time-driven synapse update scheme to analyze advantages in terms of memory and computations. We further present a reference implementation in the spiking neural network simulator NEST for two prototypical voltage-based plasticity rules: the Clopath rule and the Urbanczik-Senn rule. For both rules, the two event-based algorithms significantly outperform the time-driven scheme. Depending on the amount of data to be stored for plasticity, which heavily differs between the rules, a strong performance increase can be achieved by compressing or sampling of information on membrane potentials. Our results on computational efficiency related to archiving of information provide guidelines for the design of learning rules in order to make them practically usable in large-scale networks. |
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| 700 | 1 | _ | |a Hahne, Jan |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Helias, Moritz |0 P:(DE-Juel1)144806 |b 2 |u fzj |
| 700 | 1 | _ | |a Bolten, Matthias |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 4 |u fzj |
| 700 | 1 | _ | |a Dahmen, David |0 P:(DE-Juel1)156459 |b 5 |u fzj |
| 773 | _ | _ | |a 10.3389/fninf.2021.609147 |g Vol. 15, p. 609147 |0 PERI:(DE-600)2452979-5 |p 609147 |t Frontiers in neuroinformatics |v 15 |y 2021 |x 1662-5196 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/893117/files/fninf-15-609147.pdf |y OpenAccess |
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