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

  • Transpose
    • Parameters
    • Methods
      • connectivity_matrix
      • load
      • receptive_fields
      • save
      • save_connectivity

Transpose

extensions.convolution.Transpose.Transpose(self, proj, target)

Transposed projection reusing the weights of an already-defined rate-coded projection.

Even though the original projection can be learnable, this one can not. The computed post-synaptic potential is the default case for rate-coded projections: “w * pre.r”

The proposed target can differ from the target of the forward projection.

Example:

proj_ff = ann.Projection( input, output, target="exc" )
proj_ff.connect_all_to_all(weights=Uniform(0,1)

proj_fb = Transpose(proj_ff, target="inh")
proj_fb.connect()

Parameters

Name Type Description Default
proj original projection. required
target type of the connection (can differ from the original one). required

Methods

Name Description
connectivity_matrix Not available.
load Not available.
receptive_fields Not available.
save Not available.
save_connectivity Not available.

connectivity_matrix

extensions.convolution.Transpose.Transpose.connectivity_matrix(fill=0.0)

Not available.

load

extensions.convolution.Transpose.Transpose.load(filename)

Not available.

receptive_fields

extensions.convolution.Transpose.Transpose.receptive_fields(
    variable='w',
    in_post_geometry=True,
)

Not available.

save

extensions.convolution.Transpose.Transpose.save(filename)

Not available.

save_connectivity

extensions.convolution.Transpose.Transpose.save_connectivity(filename)

Not available.

Pooling
Copy
 

Copyright Julien Vitay, Helge Ülo Dinkelbach, Fred Hamker