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This is A PREVIEW for NEST 3.0 and NOT an OFFICIAL RELEASE! Some functionality may not be available and information may be incomplete!

stdp_connection_facetshw_hom – Synapse type for spike-timing dependent plasticity using homogeneous parameters

Description

stdp_facetshw_synapse is a connector to create synapses with spike-timing dependent plasticity (as defined in 1). This connector is a modified version of stdp_synapse. It includes constraints of the hardware developed in the FACETS (BrainScaleS) project [2,3], as e.g. 4-bit weight resolution, sequential updates of groups of synapses and reduced symmetric nearest-neighbor spike pairing scheme. For details see 3. The modified spike pairing scheme requires the calculation of tau_minus_ within this synapse and not at the neuron site via Kplus_ like in stdp_connection_hom.

Parameters

Common properties

tau_plus

ms

Time constant of STDP window, causal branch

tau_minus_stdp

ms

Time constant of STDP window, anti-causal branch

Wmax

real

Maximum allowed weight

no_synapses

integer

Total number of synapses

synapses_per_driver

integer

Number of synapses updated at once

driver_readout_time

real

Time for processing of one synapse row (synapse line driver)

readout_cycle_duration

real

Duration between two subsequent updates of same synapse (synapse line driver)

lookuptable_0

list of integers

Three look-up tables (LUT)

lookuptable_1

list of integers

lookuptable_2

list of integers

configbit_0

list of integers

Configuration bits for evaluation function. For details see code in function eval_function_ and 4 (configbit[0]=e_cc, ..[1]_=e_ca, ..[2]_=e_ac, ..[3]=e_aa). Depending on these two sets of configuration bits weights are updated according LUTs (out of three: (1,0), (0,1), (1,1)). For (0,0) continue without reset.

configbit_1

list of integers

reset_pattern

list of integers

Configuration bits for reset behavior. Two bits for each LUT (reset causal and acausal). In hardware only (all false; never reset) or (all true; always reset) is allowed.

Individual properties

a_causal

real

Causal and anti-causal spike pair accumulations

a_acausal

real

a_thresh_th

real

Two thresholds used in evaluation function No common property, because variation of analog synapse circuitry can be applied here

a_thresh_tl

real

synapse_id

integer

Synapse ID, used to assign synapses to groups (synapse drivers)

Remarks:

The synapse IDs are assigned to each synapse in an ascending order (0,1,2, …) according their first presynaptic activity and is used to group synapses that are updated at once. It is possible to avoid activity dependent synapse ID assignments by manually setting the no_synapses and the synapse_id(s) before running the simulation. The weights will be discretized after the first presynaptic activity at a synapse.

Common properties can only be set on the synapse model using SetDefaults.

Transmits

SpikeEvent

References

1

Morrison A, Diesmann M, Gerstner W (2008). Phenomenological models of synaptic plasticity based on spike-timing. Biological Cybernetics, 98:459-478. DOI: https://doi.org/10.1007/s00422-008-0233-1

2

Schemmel J, Gruebl A, Meier K, Mueller E (2006). Implementing synaptic plasticity in a VLSI spiking neural network model. In Proceedings of the 2006 International Joint Conference on Neural Networks, pp.1–6, IEEE Press. DOI: https://doi.org/10.1109/IJCNN.2006.246651

3

Pfeil T, Potjans TC, Schrader S, Potjans W, Schemmel J, Diesmann M, Meier K (2012). Is a 4-bit synaptic weight resolution enough? - constraints on enabling spike-timing dependent plasticity in neuromorphic hardware. Frontiers in Neuroscience 6(90). DOI: https://doi.org/10.3389/fnins.2012.00090

4

Friedmann, S. in preparation