stdp_facetshw_synapse_hom - Synapse type for spike-timing dependentDescription:
plasticity using homogeneous parameters,
i.e. all synapses have the same parameters.
stdp_facetshw_synapse is a connector to create synapses with spike-timing
dependent plasticity (as defined in ).
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 .
The modified spike pairing scheme requires the calculation of tau_minus_
within this synapse and not at the neuron site via Kplus_ like in
tau_plus double - Time constant of STDP window, causal branch in ms
tau_minus_stdp double - Time constant of STDP window, anti-causal branch
Wmax double - Maximum allowed weight
no_synapses long - total number of synapses
synapses_per_driver long - number of synapses updated at once
driver_readout_time double - time for processing of one synapse row
(synapse line driver)
readout_cycle_duration double - duration between two subsequent
updates of same synapse (synapse line
- three look-up tables (LUT)
- configuration bits for evaluation
function. For details see code in
function eval_function_ and 
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
- 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.
a_causal double - causal and anti-causal spike pair accumulations
a_thresh_th double - two thresholds used in evaluation function.
No common property, because variation of analog
synapse circuitry can be applied here
synapse_id long - synapse ID, used to assign synapses to groups (synapse
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.
 Morrison, A., Diesmann, M., and Gerstner, W. (2008).
Phenomenological models of synaptic plasticity based on
spike-timing, Biol. Cybern., 98,459--478
 Schemmel, J., Gruebl, A., Meier, K., and 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
 Pfeil, T., Potjans, T. C., 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. Front. Neurosci. 6 (90).
 Friedmann, S. in preparation
Thomas Pfeil (TP), Moritz Helias, Abigail MorrisonFirstVersion: