在Lasagne框架中定義神經網絡模型的一般步驟如下：

1. 導入所需的庫和模塊：

import lasagne
import theano
import theano.tensor as T

2. 定義神經網絡的輸入變量：

input_var = T.tensor4('inputs')
target_var = T.ivector('targets')

3. 定義神經網絡架構：

network = lasagne.layers.InputLayer(shape=(None, num_channels, input_width, input_height), input_var=input_var)
network = lasagne.layers.Conv2DLayer(network, num_filters=32, filter_size=(3,3), nonlinearity=lasagne.nonlinearities.rectify)
network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2,2))
network = lasagne.layers.Conv2DLayer(network, num_filters=64, filter_size=(3,3), nonlinearity=lasagne.nonlinearities.rectify)
network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2,2))
network = lasagne.layers.DenseLayer(network, num_units=256, nonlinearity=lasagne.nonlinearities.rectify)

4. 定義輸出層和損失函數：

output_layer = lasagne.layers.DenseLayer(network, num_units=num_classes, nonlinearity=lasagne.nonlinearities.softmax)
prediction = lasagne.layers.get_output(output_layer)
loss = lasagne.objectives.categorical_crossentropy(prediction, target_var).mean()

5. 定義更新規則和優化器：

params = lasagne.layers.get_all_params(output_layer, trainable=True)
updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=0.01, momentum=0.9)

6. 編譯訓練和測試函數：

train_fn = theano.function([input_var, target_var], loss, updates=updates)
test_fn = theano.function([input_var, target_var], loss)

通過以上步驟，您就可以在Lasagne框架中定義一個簡單的神經網絡模型。您可以根據需要修改神經網絡的架構和參數來構建更復雜的模型。