-module(mlx_neuromorphic). %% Neuromorphic Computing and Spiking Neural Networks -export([ %% Spiking Neural Networks spiking_neuron/2, spiking_neuron/3, leaky_integrate_fire/3, adaptive_exponential/3, izhikevich_neuron/4, hodgkin_huxley_neuron/5, %% Network Construction spiking_neural_network/2, spiking_neural_network/3, add_spiking_layer/3, connect_neurons/4, synaptic_plasticity/3, spike_timing_dependent_plasticity/4, %% Temporal Dynamics temporal_encoding/2, temporal_encoding/3, spike_train_generation/3, poisson_spike_train/2, rate_encoding/2, temporal_contrast_encoding/2, %% Learning Algorithms stdp_learning/4, triplet_stdp/5, homeostatic_plasticity/3, metaplasticity/4, reward_modulated_stdp/5, neuromodulation/4, %% Neuromorphic Architectures liquid_state_machine/3, echo_state_network/3, neural_turing_machine/4, differentiable_neural_computer/4, reservoir_computing/3, extreme_learning_machine/3, %% Biologically Inspired Components dendritic_computation/3, axonal_delays/3, neural_oscillations/3, population_dynamics/3, cortical_columns/4, brain_inspired_attention/4, %% Event-Driven Processing event_driven_simulation/3, asynchronous_processing/3, spike_based_convolution/4, temporal_pooling/3, %% Neuromorphic Hardware Emulation memristor_crossbar/3, memristor_crossbar/4, analog_computing/3, mixed_signal_processing/4, %% Advanced Neuromorphic Concepts neural_engineering_framework/4, semantic_pointer_architecture/3, hierarchical_temporal_memory/4, predictive_coding/4, %% Multi-Scale Neural Modeling molecular_dynamics/4, synaptic_vesicle_dynamics/3, ion_channel_modeling/4, neural_field_theory/4, %% Cognitive Architectures cognitive_map/3, spatial_navigation/4, episodic_memory/4, working_memory_model/4, attention_gating/4, executive_control/4 ]). -record(spiking_neuron, { id, type, parameters = #{}, state = #{}, connections = [], spike_history = [], last_spike_time = -1 }). -record(synapse, { pre_neuron, post_neuron, weight, delay, plasticity_rule, trace_variables = #{} }). -record(spike, { neuron_id, timestamp, amplitude = 1.0, metadata = #{} }). %% Spiking Neural Networks spiking_neuron(Type, Parameters) -> spiking_neuron(Type, Parameters, #{}). spiking_neuron(Type, Parameters, Options) -> % Create spiking neuron with specified dynamics NeuronId = maps:get(id, Options, make_ref()), InitialState = initialize_neuron_state(Type, Parameters), Neuron = #spiking_neuron{ id = NeuronId, type = Type, parameters = Parameters, state = InitialState }, {ok, Neuron}. leaky_integrate_fire(Neuron, Input, Dt) -> % Leaky Integrate-and-Fire neuron model #{v_membrane := V, v_threshold := VTh, v_reset := VReset, tau_m := TauM, r_membrane := RM} = Neuron#spiking_neuron.parameters, CurrentV = maps:get(v_membrane, Neuron#spiking_neuron.state, VReset), % Membrane voltage dynamics: tau_m * dV/dt = -(V - V_rest) + R_m * I DV = Dt / TauM * (-(CurrentV - VReset) + RM * Input), NewV = CurrentV + DV, % Check for spike case NewV >= VTh of true -> % Generate spike and reset Spike = #spike{ neuron_id = Neuron#spiking_neuron.id, timestamp = erlang:system_time(microsecond) }, NewState = maps:put(v_membrane, VReset, Neuron#spiking_neuron.state), UpdatedNeuron = Neuron#spiking_neuron{ state = NewState, spike_history = [Spike | Neuron#spiking_neuron.spike_history], last_spike_time = Spike#spike.timestamp }, {spike, Spike, UpdatedNeuron}; false -> % Update membrane voltage NewState = maps:put(v_membrane, NewV, Neuron#spiking_neuron.state), UpdatedNeuron = Neuron#spiking_neuron{state = NewState}, {no_spike, UpdatedNeuron} end. adaptive_exponential(Neuron, Input, Dt) -> % Adaptive Exponential Integrate-and-Fire model #{v_membrane := V, w_adaptation := W, v_threshold := VTh, delta_t := DeltaT, tau_m := TauM, tau_w := TauW, a := A, b := B} = Neuron#spiking_neuron.parameters, State = Neuron#spiking_neuron.state, CurrentV = maps:get(v_membrane, State, -70.0), CurrentW = maps:get(w_adaptation, State, 0.0), % Membrane voltage dynamics with exponential term ExpTerm = DeltaT * math:exp((CurrentV - VTh) / DeltaT), DV = Dt / TauM * (-CurrentV + ExpTerm - CurrentW + Input), DW = Dt / TauW * (A * CurrentV - CurrentW), NewV = CurrentV + DV, NewW = CurrentW + DW, % Spike detection case NewV >= VTh of true -> Spike = #spike{ neuron_id = Neuron#spiking_neuron.id, timestamp = erlang:system_time(microsecond) }, % Reset with adaptation ResetV = maps:get(v_reset, Neuron#spiking_neuron.parameters, -70.0), AdaptedW = NewW + B, NewState = maps:merge(State, #{ v_membrane => ResetV, w_adaptation => AdaptedW }), UpdatedNeuron = Neuron#spiking_neuron{ state = NewState, spike_history = [Spike | Neuron#spiking_neuron.spike_history], last_spike_time = Spike#spike.timestamp }, {spike, Spike, UpdatedNeuron}; false -> NewState = maps:merge(State, #{ v_membrane => NewV, w_adaptation => NewW }), UpdatedNeuron = Neuron#spiking_neuron{state = NewState}, {no_spike, UpdatedNeuron} end. izhikevich_neuron(Neuron, Input, Dt, Parameters) -> % Izhikevich neuron model (simple model of spiking neurons) #{a := A, b := B, c := C, d := D} = Parameters, State = Neuron#spiking_neuron.state, V = maps:get(v_membrane, State, -70.0), U = maps:get(u_recovery, State, 0.0), % Izhikevich equations DV = Dt * (0.04 * V * V + 5 * V + 140 - U + Input), DU = Dt * A * (B * V - U), NewV = V + DV, NewU = U + DU, % Spike and reset case NewV >= 30.0 of true -> Spike = #spike{ neuron_id = Neuron#spiking_neuron.id, timestamp = erlang:system_time(microsecond) }, NewState = maps:merge(State, #{ v_membrane => C, u_recovery => NewU + D }), UpdatedNeuron = Neuron#spiking_neuron{ state = NewState, spike_history = [Spike | Neuron#spiking_neuron.spike_history], last_spike_time = Spike#spike.timestamp }, {spike, Spike, UpdatedNeuron}; false -> NewState = maps:merge(State, #{ v_membrane => NewV, u_recovery => NewU }), UpdatedNeuron = Neuron#spiking_neuron{state = NewState}, {no_spike, UpdatedNeuron} end. hodgkin_huxley_neuron(Neuron, Input, Dt, Temperature, IonChannels) -> % Hodgkin-Huxley model with detailed ion channel dynamics State = Neuron#spiking_neuron.state, V = maps:get(v_membrane, State, -65.0), % Get gating variables M = maps:get(m_sodium, State, 0.05), H = maps:get(h_sodium, State, 0.6), N = maps:get(n_potassium, State, 0.32), % Temperature factor TempFactor = math:pow(3.0, (Temperature - 6.3) / 10.0), % Rate constants (voltage-dependent) AlphaM = 0.1 * (V + 40.0) / (1.0 - math:exp(-(V + 40.0) / 10.0)), BetaM = 4.0 * math:exp(-(V + 65.0) / 18.0), AlphaH = 0.07 * math:exp(-(V + 65.0) / 20.0), BetaH = 1.0 / (1.0 + math:exp(-(V + 35.0) / 10.0)), AlphaN = 0.01 * (V + 55.0) / (1.0 - math:exp(-(V + 55.0) / 10.0)), BetaN = 0.125 * math:exp(-(V + 65.0) / 80.0), % Apply temperature scaling AlphaMT = AlphaM * TempFactor, BetaMT = BetaM * TempFactor, AlphaHT = AlphaH * TempFactor, BetaHT = BetaH * TempFactor, AlphaNT = AlphaN * TempFactor, BetaNT = BetaN * TempFactor, % Update gating variables DM = Dt * (AlphaMT * (1 - M) - BetaMT * M), DH = Dt * (AlphaHT * (1 - H) - BetaHT * H), DN = Dt * (AlphaNT * (1 - N) - BetaNT * N), NewM = M + DM, NewH = H + DH, NewN = N + DN, % Ion channel conductances GNa = maps:get(g_sodium, IonChannels, 120.0), GK = maps:get(g_potassium, IonChannels, 36.0), GL = maps:get(g_leak, IonChannels, 0.3), % Reversal potentials ENa = maps:get(e_sodium, IonChannels, 50.0), EK = maps:get(e_potassium, IonChannels, -77.0), EL = maps:get(e_leak, IonChannels, -54.4), % Ion currents INa = GNa * NewM * NewM * NewM * NewH * (V - ENa), IK = GK * NewN * NewN * NewN * NewN * (V - EK), IL = GL * (V - EL), % Membrane capacitance Cm = maps:get(capacitance, IonChannels, 1.0), % Membrane voltage dynamics DV = Dt / Cm * (Input - INa - IK - IL), NewV = V + DV, % Update state NewState = maps:merge(State, #{ v_membrane => NewV, m_sodium => NewM, h_sodium => NewH, n_potassium => NewN }), UpdatedNeuron = Neuron#spiking_neuron{state = NewState}, % Spike detection (simple threshold) case NewV > 0.0 andalso V =< 0.0 of true -> Spike = #spike{ neuron_id = Neuron#spiking_neuron.id, timestamp = erlang:system_time(microsecond) }, {spike, Spike, UpdatedNeuron}; false -> {no_spike, UpdatedNeuron} end. %% Network Construction spiking_neural_network(NetworkTopology, NeuronTypes) -> spiking_neural_network(NetworkTopology, NeuronTypes, #{}). spiking_neural_network(NetworkTopology, NeuronTypes, Options) -> % Create spiking neural network PlasticityRules = maps:get(plasticity_rules, Options, [stdp]), TimeConstant = maps:get(time_constant, Options, 1.0), % Create neurons according to topology Neurons = create_neurons_from_topology(NetworkTopology, NeuronTypes), % Create synaptic connections Synapses = create_synaptic_connections(NetworkTopology, PlasticityRules), Network = #{ neurons => Neurons, synapses => Synapses, topology => NetworkTopology, time_constant => TimeConstant, current_time => 0.0 }, {ok, Network}. add_spiking_layer(Network, LayerConfig, PlasticityConfig) -> % Add new spiking layer to existing network LayerNeurons = create_layer_neurons(LayerConfig), LayerSynapses = create_layer_synapses(LayerConfig, PlasticityConfig), UpdatedNeurons = maps:merge(maps:get(neurons, Network), LayerNeurons), UpdatedSynapses = maps:get(synapses, Network) ++ LayerSynapses, UpdatedNetwork = maps:merge(Network, #{ neurons => UpdatedNeurons, synapses => UpdatedSynapses }), {ok, UpdatedNetwork}. %% Temporal Dynamics temporal_encoding(Data, EncodingScheme) -> temporal_encoding(Data, EncodingScheme, #{}). temporal_encoding(Data, EncodingScheme, Options) -> % Convert data to temporal spike patterns case EncodingScheme of rate_coding -> rate_encoding(Data, Options); temporal_contrast -> temporal_contrast_encoding(Data, Options); phase_coding -> phase_encoding(Data, Options); rank_order -> rank_order_encoding(Data, Options); population_vector -> population_vector_encoding(Data, Options) end. spike_train_generation(Pattern, Duration, Options) -> % Generate spike trains according to specified patterns Pattern_type = maps:get(pattern_type, Options, regular), NoiseLevel = maps:get(noise_level, Options, 0.0), case Pattern_type of regular -> generate_regular_spike_train(Pattern, Duration, NoiseLevel); poisson -> poisson_spike_train(Pattern, Duration); gamma -> generate_gamma_spike_train(Pattern, Duration, Options); burst -> generate_burst_spike_train(Pattern, Duration, Options) end. poisson_spike_train(Rate, Duration) -> % Generate Poisson spike train Dt = 0.001, % 1ms resolution NumSteps = round(Duration / Dt), SpikeProb = Rate * Dt, Spikes = lists:foldl(fun(Step, Acc) -> case rand:uniform() < SpikeProb of true -> Timestamp = Step * Dt, Spike = #spike{ neuron_id = poisson_generator, timestamp = Timestamp }, [Spike | Acc]; false -> Acc end end, [], lists:seq(0, NumSteps - 1)), {ok, lists:reverse(Spikes)}. %% Learning Algorithms stdp_learning(PreSpike, PostSpike, Synapse, LearningRate) -> % Spike-Timing Dependent Plasticity TimeDiff = PostSpike#spike.timestamp - PreSpike#spike.timestamp, % STDP window parameters TauPlus = 20.0, % ms TauMinus = 20.0, % ms APlus = 1.0, AMinus = -0.5, WeightChange = case TimeDiff > 0 of true -> % Post before pre: potentiation APlus * math:exp(-TimeDiff / TauPlus); false -> % Pre before post: depression AMinus * math:exp(TimeDiff / TauMinus) end, CurrentWeight = Synapse#synapse.weight, NewWeight = CurrentWeight + LearningRate * WeightChange, % Apply weight bounds BoundedWeight = erlang:max(0.0, erlang:min(1.0, NewWeight)), UpdatedSynapse = Synapse#synapse{weight = BoundedWeight}, {ok, UpdatedSynapse}. triplet_stdp(PreSpike, PostSpike, Synapse, LearningRate, TripletParams) -> % Triplet STDP rule for more realistic plasticity #{tau_plus := TauPlus, tau_minus := TauMinus, tau_x := TauX, tau_y := TauY, a2_plus := A2Plus, a2_minus := A2Minus, a3_plus := A3Plus, a3_minus := A3Minus} = TripletParams, % Get trace variables from synapse Traces = Synapse#synapse.trace_variables, R1 = maps:get(r1, Traces, 0.0), R2 = maps:get(r2, Traces, 0.0), O1 = maps:get(o1, Traces, 0.0), O2 = maps:get(o2, Traces, 0.0), TimeDiff = PostSpike#spike.timestamp - PreSpike#spike.timestamp, % Compute weight change based on triplet interactions WeightChange = case TimeDiff > 0 of true -> % Potentiation A2Plus * R1 + A3Plus * R2 * O1; false -> % Depression A2Minus * O1 + A3Minus * R1 * O2 end, CurrentWeight = Synapse#synapse.weight, NewWeight = CurrentWeight + LearningRate * WeightChange, BoundedWeight = erlang:max(0.0, erlang:min(1.0, NewWeight)), % Update trace variables NewR1 = R1 * math:exp(-abs(TimeDiff) / TauPlus) + 1.0, NewR2 = R2 * math:exp(-abs(TimeDiff) / TauX) + 1.0, NewO1 = O1 * math:exp(-abs(TimeDiff) / TauMinus) + 1.0, NewO2 = O2 * math:exp(-abs(TimeDiff) / TauY) + 1.0, UpdatedTraces = maps:merge(Traces, #{ r1 => NewR1, r2 => NewR2, o1 => NewO1, o2 => NewO2 }), UpdatedSynapse = Synapse#synapse{ weight = BoundedWeight, trace_variables = UpdatedTraces }, {ok, UpdatedSynapse}. %% Neuromorphic Architectures liquid_state_machine(InputSpikes, ReservoirConfig, ReadoutConfig) -> % Liquid State Machine implementation ReservoirSize = maps:get(size, ReservoirConfig, 100), Connectivity = maps:get(connectivity, ReservoirConfig, 0.1), % Create reservoir {ok, Reservoir} = create_random_reservoir(ReservoirSize, Connectivity), % Inject input spikes {ok, ReservoirStates} = simulate_reservoir_dynamics(InputSpikes, Reservoir), % Train readout {ok, Readout} = train_reservoir_readout(ReservoirStates, ReadoutConfig), {ok, {Reservoir, Readout}}. echo_state_network(InputData, ReservoirConfig, OutputTargets) -> % Echo State Network with spiking neurons ReservoirSize = maps:get(size, ReservoirConfig, 200), SpectralRadius = maps:get(spectral_radius, ReservoirConfig, 0.9), % Create echo state reservoir {ok, Reservoir} = create_echo_state_reservoir(ReservoirSize, SpectralRadius), % Process input through reservoir {ok, ReservoirOutputs} = process_through_reservoir(InputData, Reservoir), % Train linear readout {ok, ReadoutWeights} = train_linear_readout(ReservoirOutputs, OutputTargets), {ok, {Reservoir, ReadoutWeights}}. %% Advanced Neuromorphic Concepts neural_engineering_framework(FunctionToDecode, NeuralPopulation, DecodingWeights, EncodingTransform) -> % Neural Engineering Framework for population-level computation % Encode input using neural population {ok, PopulationActivity} = encode_with_population(FunctionToDecode, NeuralPopulation, EncodingTransform), % Decode function from population activity {ok, DecodedFunction} = decode_from_population(PopulationActivity, DecodingWeights), % Compute representation error RepresentationError = compute_representation_error(FunctionToDecode, DecodedFunction), {ok, {DecodedFunction, RepresentationError}}. semantic_pointer_architecture(ConceptVectors, BindingOperations, UnbindingOperations) -> % Semantic Pointer Architecture for symbolic computation in neural networks % Initialize semantic pointers SemanticPointers = initialize_semantic_pointers(ConceptVectors), % Perform binding operations BoundPointers = apply_binding_operations(SemanticPointers, BindingOperations), % Perform unbinding operations UnboundPointers = apply_unbinding_operations(BoundPointers, UnbindingOperations), {ok, {SemanticPointers, BoundPointers, UnboundPointers}}. hierarchical_temporal_memory(SpatialPooler, TemporalMemory, InputData, LearningConfig) -> % Hierarchical Temporal Memory implementation % Spatial pooling {ok, SparseRepresentation} = spatial_pooling(InputData, SpatialPooler), % Temporal memory {ok, PredictiveState} = temporal_memory_processing(SparseRepresentation, TemporalMemory), % Learning and adaptation {ok, UpdatedHTM} = htm_learning(SpatialPooler, TemporalMemory, LearningConfig), {ok, {PredictiveState, UpdatedHTM}}. predictive_coding(PriorBelief, SensoryInput, PredictionError, HierarchicalLevels) -> % Predictive coding framework for hierarchical neural processing % Generate predictions from prior beliefs {ok, Predictions} = generate_hierarchical_predictions(PriorBelief, HierarchicalLevels), % Compute prediction errors {ok, PredictionErrors} = compute_prediction_errors(Predictions, SensoryInput), % Update beliefs based on prediction errors {ok, UpdatedBeliefs} = update_beliefs_from_errors(PriorBelief, PredictionErrors), % Propagate errors up the hierarchy {ok, HierarchicalErrors} = propagate_errors_hierarchically(PredictionErrors, HierarchicalLevels), {ok, {UpdatedBeliefs, HierarchicalErrors}}. %% Helper functions (simplified implementations) initialize_neuron_state(lif, Parameters) -> VReset = maps:get(v_reset, Parameters, -70.0), #{v_membrane => VReset}; initialize_neuron_state(adaptive_exp, Parameters) -> VReset = maps:get(v_reset, Parameters, -70.0), #{v_membrane => VReset, w_adaptation => 0.0}; initialize_neuron_state(izhikevich, _Parameters) -> #{v_membrane => -70.0, u_recovery => 0.0}; initialize_neuron_state(hodgkin_huxley, _Parameters) -> #{v_membrane => -65.0, m_sodium => 0.05, h_sodium => 0.6, n_potassium => 0.32}. create_neurons_from_topology(_NetworkTopology, _NeuronTypes) -> #{}. create_synaptic_connections(_NetworkTopology, _PlasticityRules) -> []. create_layer_neurons(_LayerConfig) -> #{}. create_layer_synapses(_LayerConfig, _PlasticityConfig) -> []. rate_encoding(Data, _Options) -> % Convert data values to spike rates {ok, Data}. temporal_contrast_encoding(Data, _Options) -> % Encode temporal contrasts as spike patterns {ok, Data}. phase_encoding(Data, _Options) -> % Phase encoding implementation {ok, Data}. rank_order_encoding(Data, _Options) -> % Rank order encoding implementation {ok, Data}. population_vector_encoding(Data, _Options) -> % Population vector encoding implementation {ok, Data}. generate_regular_spike_train(_Pattern, _Duration, _NoiseLevel) -> {ok, []}. generate_gamma_spike_train(_Pattern, _Duration, _Options) -> {ok, []}. generate_burst_spike_train(_Pattern, _Duration, _Options) -> {ok, []}. % Additional helper functions would be implemented here... create_random_reservoir(_ReservoirSize, _Connectivity) -> {ok, #{}}. simulate_reservoir_dynamics(_InputSpikes, _Reservoir) -> {ok, []}. train_reservoir_readout(_ReservoirStates, _ReadoutConfig) -> {ok, #{}}. create_echo_state_reservoir(_ReservoirSize, _SpectralRadius) -> {ok, #{}}. process_through_reservoir(_InputData, _Reservoir) -> {ok, []}. train_linear_readout(_ReservoirOutputs, _OutputTargets) -> {ok, #{}}. encode_with_population(_FunctionToDecode, _NeuralPopulation, _EncodingTransform) -> {ok, []}. decode_from_population(_PopulationActivity, _DecodingWeights) -> {ok, #{}}. compute_representation_error(_FunctionToDecode, _DecodedFunction) -> 0.0. initialize_semantic_pointers(_ConceptVectors) -> #{}. apply_binding_operations(_SemanticPointers, _BindingOperations) -> #{}. apply_unbinding_operations(_BoundPointers, _UnbindingOperations) -> #{}. spatial_pooling(_InputData, _SpatialPooler) -> {ok, []}. temporal_memory_processing(_SparseRepresentation, _TemporalMemory) -> {ok, #{}}. htm_learning(_SpatialPooler, _TemporalMemory, _LearningConfig) -> {ok, #{}}. generate_hierarchical_predictions(_PriorBelief, _HierarchicalLevels) -> {ok, []}. compute_prediction_errors(_Predictions, _SensoryInput) -> {ok, []}. update_beliefs_from_errors(_PriorBelief, _PredictionErrors) -> {ok, #{}}. propagate_errors_hierarchically(_PredictionErrors, _HierarchicalLevels) -> {ok, []}. %% Stub implementations for all other exported functions connect_neurons(_PreNeuron, _PostNeuron, _Weight, _PlasticityRule) -> {error, not_implemented}. synaptic_plasticity(_Synapse, _Activity, _LearningRule) -> {error, not_implemented}. spike_timing_dependent_plasticity(_PreSpike, _PostSpike, _Synapse, _Parameters) -> {error, not_implemented}. homeostatic_plasticity(_Network, _TargetActivity, _TimeWindow) -> {error, not_implemented}. metaplasticity(_Synapse, _ActivityHistory, _MetaplasticityRule, _Parameters) -> {error, not_implemented}. reward_modulated_stdp(_PreSpike, _PostSpike, _Synapse, _RewardSignal, _Parameters) -> {error, not_implemented}. neuromodulation(_Network, _ModulatorType, _ConcentrationLevel, _TargetRegions) -> {error, not_implemented}. neural_turing_machine(_Controller, _Memory, _ReadHeads, _WriteHeads) -> {error, not_implemented}. differentiable_neural_computer(_Controller, _Memory, _ReadHeads, _WriteHeads) -> {error, not_implemented}. reservoir_computing(_InputData, _ReservoirConfig, _ReadoutConfig) -> {error, not_implemented}. extreme_learning_machine(_InputData, _HiddenNodes, _ActivationFunction) -> {error, not_implemented}. dendritic_computation(_DendriticTree, _InputSpikes, _ComputationRule) -> {error, not_implemented}. axonal_delays(_SourceNeuron, _TargetNeurons, _DelayDistribution) -> {error, not_implemented}. neural_oscillations(_NetworkRegion, _OscillationType, _FrequencyBand) -> {error, not_implemented}. population_dynamics(_NeuralPopulation, _ConnectivityMatrix, _DynamicsModel) -> {error, not_implemented}. cortical_columns(_LayerStructure, _MinicolumnConfig, _IntercolumnConnections, _FunctionalModules) -> {error, not_implemented}. brain_inspired_attention(_InputStreams, _AttentionMechanism, _SaliencyMap, _TopDownControl) -> {error, not_implemented}. event_driven_simulation(_Events, _Network, _SimulationParameters) -> {error, not_implemented}. asynchronous_processing(_InputEvents, _ProcessingUnits, _SynchronizationStrategy) -> {error, not_implemented}. spike_based_convolution(_InputSpikes, _ConvolutionKernel, _StrideConfig, _PoolingConfig) -> {error, not_implemented}. temporal_pooling(_SpikeTrains, _PoolingWindow, _PoolingStrategy) -> {error, not_implemented}. memristor_crossbar(_InputVoltages, _MemristorMatrix, _ReadoutStrategy) -> {error, not_implemented}. memristor_crossbar(_InputVoltages, _MemristorMatrix, _ReadoutStrategy, _PlasticityUpdate) -> {error, not_implemented}. analog_computing(_AnalogInputs, _AnalogCircuit, _NoiseModel) -> {error, not_implemented}. mixed_signal_processing(_DigitalInputs, _AnalogInputs, _SignalProcessor, _ConversionStrategy) -> {error, not_implemented}. molecular_dynamics(_MolecularSystem, _ForceField, _IntegrationMethod, _TimeStep) -> {error, not_implemented}. synaptic_vesicle_dynamics(_PresynapticTerminal, _VesiclePool, _ReleaseParameters) -> {error, not_implemented}. ion_channel_modeling(_ChannelType, _VoltageProfile, _ChannelKinetics, _ModulationFactors) -> {error, not_implemented}. neural_field_theory(_SpatialDomain, _FieldEquations, _ConnectivityKernel, _InitialConditions) -> {error, not_implemented}. cognitive_map(_SpatialEnvironment, _PlaceCells, _GridCells) -> {error, not_implemented}. spatial_navigation(_Environment, _NavigationStrategy, _PathPlanning, _LocalizationMethod) -> {error, not_implemented}. episodic_memory(_Events, _MemoryTrace, _RetrievalCues, _ConsolidationProcess) -> {error, not_implemented}. working_memory_model(_StimulusSet, _MaintenanceMechanism, _ManipulationOperations, _DecayFunction) -> {error, not_implemented}. attention_gating(_InputChannels, _AttentionWeights, _GatingThreshold, _CompetitionMechanism) -> {error, not_implemented}. executive_control(_GoalState, _ControlPolicies, _ConflictMonitoring, _CognitiveFlexibility) -> {error, not_implemented}.