ANNarchy 4.8.2
  • ANNarchy
  • Installation
  • Tutorial
  • Manual
  • Notebooks
  • Reference

  • Reference
  • Core components
    • Population
    • Projection
    • Neuron
    • Synapse
    • Monitor
    • PopulationView
    • Dendrite
    • Network
  • Configuration
    • setup
    • compile
    • clear
    • reset
    • set_seed
    • get_population
    • get_projection
    • populations
    • projections
    • monitors
  • Simulation
    • simulate
    • simulate_until
    • step
    • parallel_run
    • enable_learning
    • disable_learning
    • get_time
    • set_time
    • get_current_step
    • set_current_step
    • dt
  • Neuron models
    • LeakyIntegrator
    • Izhikevich
    • IF_curr_exp
    • IF_cond_exp
    • IF_curr_alpha
    • IF_cond_alpha
    • HH_cond_exp
    • EIF_cond_alpha_isfa_ista
    • EIF_cond_exp_isfa_ista
  • Synapse models
    • STP
    • STDP
    • Hebb
    • Oja
    • IBCM
  • Inputs
    • InputArray
    • TimedArray
    • PoissonPopulation
    • TimedPoissonPopulation
    • SpikeSourceArray
    • HomogeneousCorrelatedSpikeTrains
    • CurrentInjection
    • DecodingProjection
    • ImagePopulation
    • VideoPopulation
  • IO
    • save
    • load
    • save_parameters
    • load_parameters
  • Utilities
    • report
  • Random Distributions
    • Uniform
    • DiscreteUniform
    • Normal
    • LogNormal
    • Exponential
    • Gamma
    • Binomial
  • Functions and Constants
    • add_function
    • functions
    • Constant
    • get_constant
  • Plotting
    • raster_plot
    • histogram
    • inter_spike_interval
    • coefficient_of_variation
    • population_rate
    • smoothed_rate
  • Callbacks
    • every
    • callbacks_enabled
    • disable_callbacks
    • enable_callbacks
    • clear_all_callbacks
  • Convolution
    • Convolution
    • Pooling
    • Transpose
    • Copy
  • BOLD monitoring
    • BoldMonitor
    • BoldModel
    • balloon_RN
    • balloon_RL
    • balloon_CN
    • balloon_CL
    • balloon_maith2021
    • balloon_two_inputs
  • Tensorboard logging
    • Logger
  • ANN-to-SNN conversion
    • ANNtoSNNConverter

On this page

  • DecodingProjection
    • Parameters

DecodingProjection

inputs.DecodingProjection.DecodingProjection(
    self,
    pre,
    post,
    target,
    window=0.0,
    name=None,
    copied=False,
)

Decoding projection to transform spike trains into firing rates.

The pre-synaptic population must be a spiking population, while the post-synaptic one must be rate-coded.

Pre-synaptic spikes are accumulated for each post-synaptic neuron. A sliding window can be used to smoothen the results with the window parameter.

The decoded firing rate is accessible in the post-synaptic neurons with sum(target).

The projection can be connected using any method available in Projection (although all-to-all or many-to-one makes mostly sense). Delays are ignored.

The weight value allows to scale the firing rate: if you want a pre-synaptic firing rate of 100 Hz to correspond to a post-synaptic rate of 1.0, use w = 1./100..

Example:

pop1 = ann.PoissonPopulation(1000, rates=100.)
pop2 = ann.Population(1, ann.Neuron(equations="r=sum(exc)"))
proj = DecodingProjection(pop1, pop2, 'exc', window=10.0)
proj.connect_all_to_all(1.0, force_multiple_weights=True)

Parameters

Name Type Description Default
pre Population pre-synaptic population. required
post Population post-synaptic population. required
target str type of the connection. required
window float duration of the time window to collect spikes (default: dt). 0.0
CurrentInjection
ImagePopulation
 

Copyright Julien Vitay, Helge Ülo Dinkelbach, Fred Hamker