 	if (lGrpId == ALL) {
 		lengthSyn = networkConfigs[netId].numPostSynNet;
 		posSyn = 0;
-	}
-	else {
+	} else {
 		lengthSyn = 0;
 		for (int lNId = groupConfigs[netId][lGrpId].lStartN; lNId <= groupConfigs[netId][lGrpId].lEndN; lNId++)
 			lengthSyn += dest->Npost[lNId];

	assert(runtimeData[netId].memType == GPU_MEM);
	checkAndSetGPUDevice(netId);

	kernel_doCurrentUpdateD1 << <NUM_BLOCKS, NUM_THREADS >> >(simTimeMs, simTimeSec, simTime);
	CUDA_GET_LAST_ERROR("Kernel execution failed");
}


	CUDA_CHECK_ERRORS(cudaMemset(dest->I_set, 0, networkConfigs[netId].I_setPitch * networkConfigs[netId].I_setLength));

	// synSpikeTime: an array indicates the last time when a synapse got a spike
	if(allocateMem)
		CUDA_CHECK_ERRORS(cudaMalloc((void**)&dest->synSpikeTime, sizeof(int) * networkConfigs[netId].numPreSynNet));
	CUDA_CHECK_ERRORS(cudaMemcpy(dest->synSpikeTime, managerRuntimeData.synSpikeTime, sizeof(int) * networkConfigs[netId].numPreSynNet, cudaMemcpyHostToDevice));

	// neural auxiliary data
	// lastSpikeTime: an array indicates the last time of a neuron emitting a spike
	// neuron firing time
	if(allocateMem)
		CUDA_CHECK_ERRORS(cudaMalloc((void**)&dest->lastSpikeTime, sizeof(int) * networkConfigs[netId].numNAssigned));
	CUDA_CHECK_ERRORS(cudaMemcpy(dest->lastSpikeTime, managerRuntimeData.lastSpikeTime, sizeof(int) * networkConfigs[netId].numNAssigned, cudaMemcpyHostToDevice));

