8f43927575
examples/ is based on KasperskyLab/knp at 55470ed6 with local changes to the mnist-learn altai/blifat models, cuda-backend CMake and model_loader. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
223 lines
10 KiB
C++
223 lines
10 KiB
C++
/**
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* @file construct_network.cpp
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* @brief Functions for network construction.
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* @kaspersky_support A. Vartenkov
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* @date 03.12.2024
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* @license Apache 2.0
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* @copyright © 2024-2026 AO Kaspersky Lab
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <string>
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#include <vector>
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#include "hyperparameters.h"
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// cppcheck-suppress missingInclude
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#include "models/network_constructor.h"
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// cppcheck-suppress missingInclude
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#include "models/resource_from_weight.h"
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#include "network_functions.h"
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/// Short name for delta synapse.
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using DeltaSynapse = knp::synapse_traits::DeltaSynapse;
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/// Short name for delta synapse parameters.
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using DeltaSynapseParams = knp::synapse_traits::synapse_parameters<DeltaSynapse>;
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/// Short name for delta synapse projection.
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using DeltaProjection = knp::core::Projection<DeltaSynapse>;
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/// Short name for STDP delta synapse.
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using ResourceSynapse = knp::synapse_traits::SynapticResourceSTDPDeltaSynapse;
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/// Short name for STDP delta synapse parameters.
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using ResourceSynapseParams = knp::synapse_traits::synapse_parameters<ResourceSynapse>;
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/// Short name for STDP BLIFAT neuron parameters.
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using ResourceNeuronData = knp::neuron_traits::neuron_parameters<knp::neuron_traits::SynapticResourceSTDPBLIFATNeuron>;
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/// Structure to store all network populations for organized access.
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struct NetworkPopulations
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{
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/// Input population for processing rasterized image data.
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const PopulationInfo &input_pop_;
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/// Output population for classification results.
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const PopulationInfo &output_pop_;
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/// Gate population for WTA competition mechanisms.
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const PopulationInfo &gate_pop_;
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/// Population for rasterized image channeling.
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const PopulationInfo &raster_pop_;
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/// Population for image label processing.
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const PopulationInfo &target_pop_;
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};
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// Create all network populations with appropriate neuron parameters and roles for BLIFAT model.
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static NetworkPopulations create_populations(NetworkConstructor &constructor)
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{
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// Create neurons with BLIFAT-specific parameters.
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// Online Help link: https://click.kaspersky.com/?hl=en-US&version=2.0&pid=KNP&link=online_help&helpid=235859
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ResourceNeuronData default_neuron{{}};
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default_neuron.activation_threshold_ = default_threshold;
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ResourceNeuronData input_neuron = default_neuron;
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input_neuron.potential_decay_ = input_neuron_potential_decay;
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input_neuron.d_h_ = hebbian_plasticity;
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input_neuron.dopamine_plasticity_time_ = neuron_dopamine_period;
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input_neuron.synapse_sum_threshold_coefficient_ = threshold_weight_coeff;
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input_neuron.isi_max_ = isi_max;
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input_neuron.min_potential_ = min_potential;
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input_neuron.stability_change_parameter_ = stability_change_parameter;
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input_neuron.resource_drain_coefficient_ = resource_drain_coefficient;
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input_neuron.stochastic_stimulation_ = stochastic_stimulation;
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// Create input population with specialized BLIFAT neuron parameters.
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const auto &input_pop =
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constructor.add_population(input_neuron, num_input_neurons, PopulationRole::INPUT, true, "INPUT");
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// Create output population for digit classification (one neuron per digit class).
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const auto &output_pop =
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constructor.add_population(default_neuron, classes_amount, PopulationRole::OUTPUT, true, "OUTPUT");
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// Create gate population for WTA competition mechanisms.
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const auto &gate_pop =
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constructor.add_population(default_neuron, classes_amount, PopulationRole::NORMAL, false, "GATE");
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// Create raster population for channelized image processing (28x28 = 784 neurons).
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const auto &raster_pop = constructor.add_channeled_population(input_size, true);
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// Create target population for label processing (10 neurons for 10 digit classes).
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const auto &target_pop = constructor.add_channeled_population(classes_amount, false);
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// Return organized population references for projection creation.
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return {input_pop, output_pop, gate_pop, raster_pop, target_pop};
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}
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// Create all synaptic projections between network populations with appropriate parameters for BLIFAT.
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static void create_projections(
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AnnotatedNetwork &network, NetworkConstructor &constructor, const NetworkPopulations &pops)
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{
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// Configure synapse parameters for raster to input projection with STDP plasticity.
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ResourceSynapseParams raster_to_input_synapse;
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raster_to_input_synapse.rule_.synaptic_resource_ =
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resource_from_weight(base_weight_value, min_synaptic_weight, max_synaptic_weight);
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raster_to_input_synapse.rule_.dopamine_plasticity_period_ = synapse_dopamine_period;
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raster_to_input_synapse.rule_.w_min_ = min_synaptic_weight;
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raster_to_input_synapse.rule_.w_max_ = max_synaptic_weight;
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// Create projection connecting raster population to input population.
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auto raster_to_input_proj = constructor.add_projection(
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raster_to_input_synapse, knp::framework::projection::creators::all_to_all<ResourceSynapse>, pops.raster_pop_,
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pops.input_pop_, true, false);
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// Record projection for inference and monitoring.
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network.data_.projections_from_raster_.push_back(raster_to_input_proj);
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// Configure dopamine synapse for target to input projection.
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DeltaSynapseParams target_to_input_synapse_dopamine;
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target_to_input_synapse_dopamine.weight_ = 0.18;
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target_to_input_synapse_dopamine.delay_ = 3;
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target_to_input_synapse_dopamine.output_type_ = knp::synapse_traits::OutputType::DOPAMINE;
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// Create dopamine projection connecting target population to input population.
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auto target_to_input_proj_dopamine = constructor.add_projection(
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target_to_input_synapse_dopamine, knp::framework::projection::creators::aligned<DeltaSynapse>, pops.target_pop_,
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pops.input_pop_, false, false);
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// Record dopamine projection for label processing.
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network.data_.projections_from_classes_.push_back(target_to_input_proj_dopamine);
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// Configure synapse for input to output projection.
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DeltaSynapseParams input_to_output_synapse;
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input_to_output_synapse.output_type_ = knp::synapse_traits::OutputType::EXCITATORY;
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input_to_output_synapse.weight_ = 10;
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// Create projection connecting input population to output population.
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auto input_to_output_proj = constructor.add_projection(
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input_to_output_synapse, knp::framework::projection::creators::aligned<DeltaSynapse>, pops.input_pop_,
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pops.output_pop_, false, true);
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// Connect projection as receiver to WTA mechanism.
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network.data_.wta_data_.back().second.push_back(input_to_output_proj);
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// Configure synapse for output to gate projection (blocking signal).
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DeltaSynapseParams output_to_gate_synapse;
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output_to_gate_synapse.weight_ = -10;
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output_to_gate_synapse.output_type_ = knp::synapse_traits::OutputType::BLOCKING;
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// Create blocking projection connecting output population to gate population.
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auto output_to_gate_proj = constructor.add_projection(
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output_to_gate_synapse, knp::framework::projection::creators::aligned<DeltaSynapse>, pops.output_pop_,
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pops.gate_pop_, false, false);
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// Configure synapse for target to gate projection.
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DeltaSynapseParams target_to_gate_synapse;
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target_to_gate_synapse.weight_ = 10.f;
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target_to_gate_synapse.output_type_ = knp::synapse_traits::OutputType::EXCITATORY;
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// Create projection connecting target population to gate population.
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auto target_to_gate_proj = constructor.add_projection(
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target_to_gate_synapse, knp::framework::projection::creators::aligned<DeltaSynapse>, pops.target_pop_,
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pops.gate_pop_, false, false);
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// Record gate projection for label processing.
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network.data_.projections_from_classes_.push_back(target_to_gate_proj);
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// Configure synapse for gate to input projection.
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DeltaSynapseParams gate_to_input_synapse;
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gate_to_input_synapse.weight_ = 10;
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gate_to_input_synapse.output_type_ = knp::synapse_traits::OutputType::EXCITATORY;
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// Create gating projection connecting gate population to input population.
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auto gate_to_input_proj = constructor.add_projection(
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gate_to_input_synapse, knp::framework::projection::creators::aligned<DeltaSynapse>, pops.gate_pop_,
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pops.input_pop_, false, false);
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// Configure excitatory synapse for target to input projection.
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DeltaSynapseParams target_to_input_synapse_excitatory;
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target_to_input_synapse_excitatory.weight_ = -30;
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target_to_input_synapse_excitatory.delay_ = 4;
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target_to_input_synapse_excitatory.output_type_ = knp::synapse_traits::OutputType::EXCITATORY;
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// Create excitatory projection connecting target population to input population.
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auto target_to_input_proj_excitatory = constructor.add_projection(
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target_to_input_synapse_excitatory, knp::framework::projection::creators::aligned<DeltaSynapse>,
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pops.target_pop_, pops.input_pop_, false, false);
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// Record excitatory projection for label processing.
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network.data_.projections_from_classes_.push_back(target_to_input_proj_excitatory);
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}
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// Construct BLIFAT network with multi-subnetwork architecture. Each subnetwork contains the complete population and projection structure.
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template <>
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AnnotatedNetwork construct_network<knp::neuron_traits::BLIFATNeuron>(const ModelDescription &model_desc)
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{
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AnnotatedNetwork result;
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// Create WTA borders for each digit class.
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for (size_t i = 0; i < classes_amount; ++i) result.data_.wta_borders_.push_back(neurons_per_column * (i + 1));
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// Create multiple subnetworks for BLIFAT architecture.
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for (int i = 0; i < num_subnetworks; ++i)
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{
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NetworkConstructor constructor(result);
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NetworkPopulations pops = create_populations(constructor);
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// Add input population as WTA sender for each subnetwork.
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result.data_.wta_data_.emplace_back().first.push_back(pops.input_pop_.uid_);
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create_projections(result, constructor, pops);
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}
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return result;
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}
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