diff --git a/src/forward.jl b/src/forward.jl
index e13f9b1..31116b8 100644
--- a/src/forward.jl
+++ b/src/forward.jl
@@ -40,7 +40,7 @@ function (kfn::kfn_1)(m::model, input_data::AbstractVector)
             resetLearningParams!(n)
         end
 
-        for n in kfn.output_neuron
+        for n in kfn.outputNeuronsArray
             # epsilonRec need to be reset because it counting how many each synaptic fires and 
             # use this info to calculate how much synaptic weight should be adjust
             resetLearningParams!(n)
diff --git a/src/snn_utils.jl b/src/snn_utils.jl
index 3ff5b95..238f65c 100644
--- a/src/snn_utils.jl
+++ b/src/snn_utils.jl
@@ -19,7 +19,7 @@ function timestep_forward!(x::passthrough_neuron)
     x.z_t = x.z_t1
 end
 
-function timestep_forward!(x::Union{compute_neuron, output_neuron})
+function timestep_forward!(x::Union{computeNeuron, outputNeuron})
     x.z_t = x.z_t1
     x.v_t = x.v_t1
 end
@@ -28,24 +28,24 @@ no_negative!(x) = x < 0.0 ? 0.0 : x
 precision(x::Array{<:Array}) = ( std(mean.(x)) / mean(mean.(x)) ) * 100
 
 # reset functions for LIF/ALIF neuron
-reset_last_firing_time!(n::compute_neuron) = n.lastFiringTime = 0.0
-reset_refractory_state_active!(n::compute_neuron) = n.refractory_state_active = false
+reset_last_firing_time!(n::computeNeuron) = n.lastFiringTime = 0.0
+reset_refractory_state_active!(n::computeNeuron) = n.refractory_state_active = false
 reset_v_t!(n::neuron) = n.v_t = n.vRest
-reset_z_t!(n::compute_neuron) = n.z_t = false
-reset_epsilonRec!(n::compute_neuron) = n.epsilonRec = n.epsilonRec * 0.0
-reset_epsilonRec!(n::output_neuron) = n.epsilonRec = n.epsilonRec * 0.0
+reset_z_t!(n::computeNeuron) = n.z_t = false
+reset_epsilonRec!(n::computeNeuron) = n.epsilonRec = n.epsilonRec * 0.0
+reset_epsilonRec!(n::outputNeuron) = n.epsilonRec = n.epsilonRec * 0.0
 reset_epsilonRecA!(n::alif_neuron) = n.epsilonRecA = n.epsilonRecA * 0.0
-reset_epsilon_in!(n::compute_neuron) = n.epsilon_in = isnothing(n.epsilon_in) ? nothing : n.epsilon_in * 0.0
-reset_error!(n::Union{compute_neuron, linear_neuron}) = n.error = nothing
-reset_w_in_change!(n::compute_neuron) = n.w_in_change = isnothing(n.w_in_change) ? nothing : n.w_in_change * 0.0
-reset_wRecChange!(n::compute_neuron) = n.wRecChange = n.wRecChange * 0.0
+reset_epsilon_in!(n::computeNeuron) = n.epsilon_in = isnothing(n.epsilon_in) ? nothing : n.epsilon_in * 0.0
+reset_error!(n::Union{computeNeuron, linear_neuron}) = n.error = nothing
+reset_w_in_change!(n::computeNeuron) = n.w_in_change = isnothing(n.w_in_change) ? nothing : n.w_in_change * 0.0
+reset_wRecChange!(n::Union{computeNeuron, outputNeuron}) = n.wRecChange = n.wRecChange * 0.0
 reset_a!(n::alif_neuron) = n.a = n.a * 0.0
-reset_reg_voltage_a!(n::compute_neuron) = n.reg_voltage_a = n.reg_voltage_a * 0.0
-reset_reg_voltage_b!(n::compute_neuron) = n.reg_voltage_b = n.reg_voltage_b * 0.0
-reset_reg_voltage_error!(n::compute_neuron) = n.reg_voltage_error = n.reg_voltage_error * 0.0
-reset_firing_counter!(n::compute_neuron) = n.firingCounter = n.firingCounter * 0.0
-reset_firing_diff!(n::Union{compute_neuron, linear_neuron}) = n.firingDiff = n.firingDiff * 0.0
-reset_refractoryCounter!(n::Union{compute_neuron, linear_neuron}) = n.refractoryCounter = n.refractoryCounter * 0.0
+reset_reg_voltage_a!(n::computeNeuron) = n.reg_voltage_a = n.reg_voltage_a * 0.0
+reset_reg_voltage_b!(n::computeNeuron) = n.reg_voltage_b = n.reg_voltage_b * 0.0
+reset_reg_voltage_error!(n::computeNeuron) = n.reg_voltage_error = n.reg_voltage_error * 0.0
+reset_firing_counter!(n::computeNeuron) = n.firingCounter = n.firingCounter * 0.0
+reset_firing_diff!(n::Union{computeNeuron, linear_neuron}) = n.firingDiff = n.firingDiff * 0.0
+reset_refractoryCounter!(n::Union{computeNeuron, linear_neuron}) = n.refractoryCounter = n.refractoryCounter * 0.0
 
 # reset function for output neuron
 reset_epsilon_j!(n::linear_neuron) = n.epsilon_j = n.epsilon_j * 0.0
@@ -218,12 +218,12 @@ function cal_v_reg!(n::alif_neuron)
     n.reg_voltage_b = n.reg_voltage_b + (component_b * (n.epsilonRec - n.epsilonRecA))
 end
 
-function voltage_error!(n::compute_neuron)
+function voltage_error!(n::computeNeuron)
     n.reg_voltage_error = 0.5 * n.reg_voltage_a
     return n.reg_voltage_error
 end
 
-function voltage_regulator!(n::compute_neuron) # running average
+function voltage_regulator!(n::computeNeuron) # running average
     Δw = n.optimiser.eta * n.c_reg_v * n.reg_voltage_b
     return Δw
 end
@@ -233,7 +233,7 @@ function firingRateError(kfn::knowledgeFn)
     return 0.5 * sum([(n.firingDiff)^2 for n in kfn.neuronsArray[start_id:end]])
 end
 
-function firing_rate_regulator!(n::compute_neuron)
+function firing_rate_regulator!(n::computeNeuron)
     # n.firingRate NOT running average (average over learning batch)
     Δw = n.optimiser.eta * n.c_reg *
          (n.firingRate - n.firingRateTarget) * n.eRec
@@ -241,10 +241,10 @@ function firing_rate_regulator!(n::compute_neuron)
     return Δw
 end
 
-firing_rate!(n::compute_neuron) = n.firingRate = (n.firingCounter / n.timeStep) * 1000
-firing_diff!(n::compute_neuron) = n.firingDiff = n.firingRate - n.firingRateTarget
+firing_rate!(n::computeNeuron) = n.firingRate = (n.firingCounter / n.timeStep) * 1000
+firing_diff!(n::computeNeuron) = n.firingDiff = n.firingRate - n.firingRateTarget
 
-function adjust_internal_learning_rate!(n::compute_neuron)
+function adjust_internal_learning_rate!(n::computeNeuron)
     n.internal_learning_rate = n.error_diff[end] < 0.0 ? n.internal_learning_rate * 0.99 :
                                 n.internal_learning_rate * 1.005
 end
@@ -276,7 +276,7 @@ end
 """ Compute all synaptic connection strength of a neuron. Also mark n.wRec to 0 if wRec goes
     below lowerlimit.
 """
-function synapticConnStrength!(n::Union{compute_neuron, output_neuron})
+function synapticConnStrength!(n::Union{computeNeuron, outputNeuron})
     for (i, connStrength) in enumerate(n.synapticStrength)
         # check whether connStrength increase or decrease based on usage from n.epsilonRec
         updown = n.epsilonRec[i] == 0.0 ? "down" : "up"
@@ -290,9 +290,9 @@ function synapticConnStrength!(n::Union{compute_neuron, output_neuron})
     end
 end
 
-function synapticConnStrength!(n::input_neuron) end
+function synapticConnStrength!(n::inputNeuron) end
 
-function neuroplasticity!(n::compute_neuron, firedNeurons::Vector)
+function neuroplasticity!(n::computeNeuron, firedNeurons::Vector)
     # if there is 0-weight then replace it with new connection
     zero_weight_index = findall(iszero.(n.wRec))
     if length(zero_weight_index) != 0
diff --git a/src/types.jl b/src/types.jl
index cb91191..559b531 100644
--- a/src/types.jl
+++ b/src/types.jl
@@ -3,7 +3,7 @@ module types
 export 
     # struct
     IronpenStruct, model, knowledgeFn, lif_neuron, alif_neuron, linear_neuron,
-    kfn_1, input_neuron, compute_neuron, neuron, output_neuron, passthrough_neuron, 
+    kfn_1, inputNeuron, computeNeuron, neuron, outputNeuron, passthrough_neuron, 
 
     # function
     instantiate_custom_types, init_neuron, populate_neuron,    
@@ -16,9 +16,9 @@ using Random, Flux, LinearAlgebra
 abstract type Ironpen end
 abstract type knowledgeFn <: Ironpen end
 abstract type neuron <: Ironpen end
-abstract type input_neuron <: neuron end
-abstract type output_neuron <: neuron end
-abstract type compute_neuron <: neuron end
+abstract type inputNeuron <: neuron end
+abstract type outputNeuron <: neuron end
+abstract type computeNeuron <: neuron end
 
 #------------------------------------------------------------------------------------------------100
 
@@ -207,8 +207,8 @@ function kfn_1(kfnParams::Dict)
     end
 
     # add compute neurons
-    for (k, v) in kfn.kfnParams[:compute_neuron]
-        current_type = kfn.kfnParams[:compute_neuron][k]
+    for (k, v) in kfn.kfnParams[:computeNeuron]
+        current_type = kfn.kfnParams[:computeNeuron][k]
         for i = 1:current_type[:numbers]
             n_id = length(kfn.neuronsArray) + 1
             neuron = init_neuron(n_id, current_type[:params], kfn.kfnParams)
@@ -223,12 +223,12 @@ function kfn_1(kfnParams::Dict)
     end
 
     for n in kfn.neuronsArray
-        if typeof(n) <: compute_neuron
+        if typeof(n) <: computeNeuron
             n.firingRateTarget = kfn.kfnParams[:neuron_firing_rate_target]
         end
     end
 
-    # excitatory neuron to inhabitory neuron = 60:40 % of compute_neuron
+    # excitatory neuron to inhabitory neuron = 60:40 % of computeNeuron
     ex_number = Int(floor(0.6 * kfn.kfnParams[:compute_neuron_number]))
     ex_n = [1 for i in 1:ex_number]
     in_number = kfn.kfnParams[:compute_neuron_number] - ex_number
@@ -241,7 +241,7 @@ function kfn_1(kfnParams::Dict)
         try n.ExInType = pop!(ex_in) catch end
     end
 
-    # add ExInType into each compute_neuron subExInType
+    # add ExInType into each computeNeuron subExInType
     for n in reverse(kfn.neuronsArray)
         try     # input neuron doest have n.subscriptionList
             for sub_id in n.subscriptionList
@@ -270,7 +270,7 @@ end
 
 """ passthrough_neuron struct
 """
-Base.@kwdef mutable struct passthrough_neuron <: input_neuron
+Base.@kwdef mutable struct passthrough_neuron <: inputNeuron
     id::Union{Int64,Nothing} = nothing               # ID of this neuron which is it position in knowledgeFn array
     type::String = "passthrough_neuron"
     knowledgeFnName::Union{String,Nothing} = nothing  # knowledgeFn that this neuron belongs to
@@ -300,7 +300,7 @@ end
 
 """ lif_neuron struct
 """
-Base.@kwdef mutable struct lif_neuron <: compute_neuron
+Base.@kwdef mutable struct lif_neuron <: computeNeuron
     id::Union{Int64,Nothing} = nothing   # this neuron ID i.e. position of this neuron in knowledgeFn
     type::String = "lif_neuron"
     ExInType::Integer = 1   # 1 excitatory, -1 inhabitory
@@ -392,7 +392,7 @@ end
 
 """ alif_neuron struct
 """
-Base.@kwdef mutable struct alif_neuron <: compute_neuron
+Base.@kwdef mutable struct alif_neuron <: computeNeuron
     id::Union{Int64,Nothing} = nothing               # this neuron ID i.e. position of this neuron in knowledgeFn
     type::String = "alif_neuron"
     ExInType::Integer = -1  # 1 excitatory, -1 inhabitory
@@ -500,7 +500,7 @@ end
 #------------------------------------------------------------------------------------------------100
 """ linear_neuron struct
 """
-Base.@kwdef mutable struct linear_neuron <: output_neuron
+Base.@kwdef mutable struct linear_neuron <: outputNeuron
     id::Union{Int64,Nothing} = nothing               # ID of this neuron which is it position in knowledgeFn array
     type::String = "linear_neuron"
     knowledgeFnName::Union{String,Nothing} = nothing  # knowledgeFn that this neuron belongs to