mlx

-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}.