Input: What is the potential of SNNs in modeling the visual system?

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Title: Deep Spiking Neural Networks with High Representation Similarity Model Visual Pathways of Macaque and Mouse
Publish Date: 22 May 2023
Author List: Zhengyu Ma (from Department of Networked Intelligence, Peng Cheng Laboratory), Yu Liutao (from Department of Networked Intelligence, Peng Cheng Laboratory), Huihui Zhou (from Department of Networked Intelligence, Peng Cheng Laboratory), Allen Brain
Author Affiliation: CORNet-S ConvNeXt-Tiny ConvNeXt-Small EfficientNet, AlexNet RegNetY, ResNet34 ConvNeXt-Base CORNetSEW, ResNet8 ResNet101 SEW-ResNet18 ViT-L, GoogLeNet SEW-ResNet34 SEW-ResNet8 Wide

Figure

Figure 1: To conduct neural representation similarity experiments, we apply three similarity metrics to a layer-by-layer comparison between the responses of models and the neural activities of visual cortex.
Figure 2: For three datasets and three similarity metrics, each point indicates the final representation similarity score of a model.Each pair of SEW ResNet and ResNet with the same depth are linked by a gray solid line.In almost all conditions, SEW ResNet outperforms ResNet by a large margin.
Figure3: For three datasets and three similarity metrics, we plot the trajectories of similarity score with model layer depth.The models are divided into two groups: ResNet and SEW ResNet.The normalized layer depth ranges from 0 (the first layer) to 1 (the last layer).Because the depths of models are not the same, we first discretize the normalized depth into 50 bins, and then apply the cubic spline interpolation to the scores of each model, yielding the smooth trajectories shown in the plot.The fine, semitransparent lines are the trajectories of each model.The thick lines are the average trajectories among each group.
Figure 5: For Macaque-Synthetic dataset, trajectories of similarity score with model layer depth are plotted.The models are divided into two groups: ViT and CNN&SNN.The normalized layer depth ranges from 0 (the first layer) to 1 (the last layer).The calculation and plotting of the trajectories are the same as Figure 3.
Figure6: The basic block of SpikingMobileNet."PW CONV" is the pointwise convolution and "DW CONV" is the depthwise convolution."SN" is the spiking neuron.
Figure 7: Overall model rankings of the similarity scores on Allen Brain mouse dataset.The similarity scores of CNNs, SNNs and vision transformers are shown by blue, green and orange bars, respectively.
Figure 9: Overall model rankings of the similarity scores on Macaque-Synthetic dataset.
Figure 10: The Spearman's rank correlation between the overall model rankings of different metrics.There is a strong correlation between SVCCA and TSVD-Reg, but RSA has weaker correlations with them.
The correlation between the similarity scores and the model depth.r is Spearman's rank correlation coefficient."-" indicates that there is no significant correlation.
Architectures of SNNs."sn" denotes the spiking neuron."g = 32" denotes the grouped convolutions with 32 groups.The hyper-parameters of the spike-element-wise block are shown in the brackets with the number of stacked blocks outside.

abstract

Deep artificial neural networks (ANNs) play a major role in modeling the visual pathways of primate and rodent. However, they highly simplify the computational properties of neurons compared to their biological counterparts. Instead, Spiking Neural Networks (SNNs) are more biologically plausible models since spiking neurons encode information with time sequences of spikes, just like biological neurons do.
However, there is a lack of studies on visual pathways with deep SNNs models. In this study, we model the visual cortex with deep SNNs for the first time, and also with a wide range of state-of-the-art deep CNNs and ViTs for comparison. Using three similarity metrics, we conduct neural representation similarity experiments on three neural datasets collected from two species under three types of stimuli.
Based on extensive similarity analyses, we further investigate the functional hierarchy and mechanisms across species. Almost all similarity scores of SNNs are higher than their counterparts of CNNs with an average of 6.6%. Depths of the layers with the highest similarity scores exhibit little differences across mouse cortical regions, but vary significantly across macaque regions, suggesting that the visual processing structure of mice is more regionally homogeneous than that of macaques.
Besides, the multi-branch structures observed in some top mouse brain-like neural networks provide computational evidence of parallel processing streams in mice, and the different performance in fitting macaque neural representations under different stimuli exhibits the functional specialization of information processing in macaques.
Taken together, our study demonstrates that SNNs could serve as promising candidates to better model and explain the functional hierarchy and mechanisms of the visual system. Originally, the prototype of deep neural networks is inspired by the biological vision system . To date, deep neural networks not only occupy an unassailable position in the field of computer vision , but also become better models of the biological visual cortex compared to traditional models in the neuroscience community (Khaligh-Razavi and Kriegeskorte 2014; .
They have been successful at predicting the neural responses in primate visual cortex, matching the hierarchy of ventral visual stream (Güc ¸lü and van Gerven 2015; , and even controlling neural activity . Moreover, as training paradigms of mice and techniques for collecting neural activity (de Vries et al. 2020) have been greatly improved, there is a strong interest in exploring mouse visual cortex.
Deep neural networks also play an important role in revealing the functional mechanisms and structures of mouse visual cortex . Compared to biological networks, Artificial Neural Networks discard the complexity of neurons . Spiking Neural Networks, incorporating the concept of time and spikes, are more biologically plausible models .
To be more specific, because of their capabilities of encoding information with spikes, capturing the dynamics of biological neurons, and extracting spatio-temporal features, deep SNNs are highly possible to yield brain-like representations ). However, deep SNNs have not been employed to model visual cortex due to the immaturity of training algorithms.
Recently, a state-ofthe-art directly trained deep SNN , makes it possible to use deep SNNs as visual cortex models. Contributions. In this work, we conduct large-scale neural representation similarity experiments on SNNs and other high-performing deep neural networks to study the brain's visual processing mechanisms, with three datasets and three similarity metrics (Figure ).
Specifically, to the best of our knowledge, we are the first to use deep SNNs to fit complex biological neural representations and explore the biological visual cortex. We summarize our main contributions in four points as follows. • We find that SNNs outperform their counterparts of CNNs with the same depth and almost the same architectures in almost all experiments.
In addition, even with very different depths and architectures, SNNs can achieve top performance in most conditions. • By making a more direct comparison between macaques and mice for the first time, we reveal the differences in the visual pathways across the two species in terms of the homogeneity of visual regions and the increases of receptive field sizes across cortical visual pathways, which is consistent with previous physiological work.
• The multi-branch structures in neural networks benefit neural representation similarity to mouse visual cortex, providing computational evidence that parallel information processing streams are widespread between cortical regions in the mouse visual system. • Comparing the results of two macaque neural datasets under different stimuli, we reveal that the macaque vision system may have functional specialization for processing human faces and other natural scenes.
Altogether, as the first work to apply deep SNNs to fit neural representations, we shed light on visual processing mechanisms in both macaques and mice, demonstrating the potential of SNNs as a novel and powerful tool for research on the visual system. Our codes and appendix are available at https://github.com/Grasshlw/SNN-Neural-Similarity.
There are plenty of computational models of macaque and mouse visual systems for exploring the visual processing mechanisms recently. We summarize some of the outstanding work in the following. The network models of macaque visual system. In the early days, studies basically used simple feedforward neural networks as the models of the macaque visual system (Khaligh-Razavi and Kriegeskorte 2014; .
Recently, some bio-inspired or more complex models achieved better performance in fitting the neural representations of macaque visual cortex . proposed a brainlike shallow CNN with recurrent connections to better match the macaque ventral visual stream. By mimicking the primary stage of the primate visual system, VOneNets ) performed more robustly in image recognition while better simulating macaque V1.
Moreover, the representations learned by unsupervised neural networks ) also effectively matched the neural activity of macaque ventral visual stream. Although the above work developed many bio-inspired structures, the networks are still traditional ANNs in nature. Our work introduces deep SNNs for the first time to explore the visual processing mechanisms of macaque visual system.
The network models of mouse visual system. Largescale mouse neural dataset provided an experimental basis for model studies of mouse visual system (de Vries et al. 2020; . conducted comparisons between the representations of mouse visual cortex and the VGG16 trained on the Im-ageNet dataset. In , they developed a single neural network to model both the dorsal and ventral pathways with showing the functional specializations.
What's more, a large survey of advanced deep networks ) revealed some hierarchy and functional properties of mice. Similar to the studies of macaque visual system, deep SNNs have never been used to model the mouse visual system. In this work, we not only use SNNs as one of the candidates to fit the representations of mouse visual cortex, but also conduct direct comparisons between macaques and mice to further investigate the functional hierarchy and mechanisms of the two species.
Our work is conducted with three neural datasets. These datasets are recorded from two species under three types of stimuli. More specifically, there are neural responses of mouse visual cortex to natural scene stimuli, and responses of macaque visual cortex to face image and synthetic image stimuli. Allen Brain mouse dataset.
It is part of the Allen Brain Observatory Visual Coding dataset ) col-lected using Neuropixel probes from 6 regions simultaneously in mouse visual cortex. Compared to two-photon calcium imaging, Neuropixel probes simultaneously record the spikes across many cortical regions with high temporal resolution.
In these experiments, mice are presented with 118 250-ms natural scene stimuli in random orders for 50 times. Hundreds to thousands of neurons are recorded for each brain region. To get the stable neurons, we first concatenate the neural responses (average number of spikes in 10-ms bins across time) under 118 images for each neuron, and then preserve the neurons whose split-half reliability across 50 trials reaches at least 0.8.
Macaque-Face dataset. This dataset ) is composed of neural responses of 159 neurons in the macaque anterior medial (AM) face patch under 2,100 real face stimuli, recorded with Tungsten electrodes. For this dataset, we compute the average number of spikes in a time window of 50-350ms after stimulus onset and exclude eleven neurons with noisy responses by assessing the neurons' noise ceiling.
The details of the preprocessing procedure are the same as . Macaque-Synthetic dataset. This dataset is also about macaque neural responses which are recorded by electrodes under 3,200 synthetic image stimuli, and used for neural prediction in the initial version of Brain-Score . The image stimuli are generated by adding a 2D projection of a 3D object model to a natural background.
The objects consist of eight categories, each with eight subclasses. The position, pose, and size of each object are randomly selected. 88 neurons of V4 and 168 neurons of IT are recorded. The neural responses are preprocessed to the form of average firing rate and can be downloaded from Brain-Score. Since the core visual function of macaque and mouse visual cortex is to recognize objects, the basic premise of model selection is that the model has good performance on object recognition tasks (e.g.
classification on ImageNet). Based on this premise, we employ 12 SNNs, 43 CNNs, and 26 vision transformers, all of which are pretrained on the Ima-geNet dataset and perform well in the classification task. As for SNNs, we use SEW ResNet as the base model, which is the deepest and SOTA directly trained SNN .
Furthermore, by combining the residual block used in SEW ResNet and the hierarchy of the visual cortex, we build several new SNNs and train them on the ImageNet using SpikingJelly ) (see Appendix A for model structures and the details of model training). As for CNNs and vision transformers, we use 44 models from the Torchvision model zoo , 22 models from the Timm model zoo ) and 3 models from the brain-like CNNs, CORnet family ).
In the feature extraction procedures of all models, we feed the same set of images used in biological experiments to the pretrained models and obtain features from all chosen layers. Different from CNNs and vision transformers, the features of SNNs are spikes in multiple time steps. To obtain the representation similarity between biological visual cortex and computational models, we apply three similarity metrics to computing similarity scores: representational similarity analysis (RSA) , regression-based encoding method and singular vector canonical correlation analysis (SVCCA) .
RSA has already been widely used to analyze neural representations of a model and a brain to different stimuli at the population level, while the regression-based encoding method directly fits the model features to neural activity data. SVCCA is originally proposed to compare features of deep neural networks, and then Buice 2019) used it to compare representation matrices from mouse visual cortex and DNNs, which demonstrated its effectiveness.
With the same model and same cortical region, we use these metrics for a layer-by-layer comparison to compute the similarity scores. The maximum similarity score across layers for a given cortical region is considered to be the level of representation similarity between the model and the cortical region.
Finally, in a given dataset, we take the average score of all cortical regions as the final similarity score for each model, which gives the overall model rankings. The implementation of each similarity metric is as follows. RSA. For two response matrices R ∈ R n×m from each layer of models and each cortical region, where n is the number of units/neurons and m is the number of stimuli, we calculate the representational similarity between the responses to each pair of image stimuli using the Pearson correlation coefficient r, yielding two representational dissimilarity matrices (RDM ∈ R m×m , where each element is the correlation distance 1 − r).
Then, the Spearman rank correlation coefficient between the flattened upper triangles of these two matrices is the metric score. Regression-Based Encoding Method. Firstly, we run truncated singular value decomposition (TSVD) to reduce the feature dimension of model layers to 40. Secondly, the features after dimensionality reduction are fitted to the representations of each neuron by ridge regression.
Finally, we compute the Pearson correlation coefficient between the predicted and ground-truth representations of each neuron and take the mean of all correlation coefficients as the metric score. More specifically, we apply leave-one-out crossvalidation to obtain predicted representations of each neuron.
For simplicity, we name this method 'TSVD-Reg'. SVCCA. For both the responses of model layers and cortical regions, we use TSVD to reduce the dimension of unit/neuron to 40, yielding two reduced representation matrices. Then we apply canonical correlation analysis (CCA) to these two matrices to obtain a vector of correlation coefficients (the length of the vector is 40).
The metric score is the mean of the vector. Because of the invariance of CCA to affine transformations , in this procedure, we only need to ensure that the stimulus dimension is consistent and aligned, even if the unit/neuron dimension is different. Dimensionality reduction plays an important role in this method to make the number of model features comparable to the number of neurons in cortical regions, since the former usually far exceeds the latter.
In addition, dimensionality reduction helps to determine which features are important to the original data, while CCA suffers in important feature detection. Using just CCA performs badly, which has been proven by . To check how similar the models are to the visual cortex's mechanisms in visual processing, we rank the final similarity scores of all models and conduct comparisons among three types of models (CNNs, SNNs, and vision transformers).
Specially, we focus on comparing SNN (SEW ResNet) and CNN (ResNet) with the same depth and almost the same architectures (Figure ). The final similarity score of a model is the average similarity score across all cortical regions. (The overall rankings can be found in Appendix B and the comparisons among three types of models are shown in Appendix C.)
Allen brain mouse dataset. No single model achieves the highest final similarity scores with all three metrics. For a fair comparison, we apply the paired t-test to SEW ResNet and ResNet with the same depth. For all three metrics, SEW ResNet performs better than ResNet by a large margin (t = 5.857, p = 0.004; t = 7.666, p = 0.002; t = 7.592, p = 0.002) 1 . 1 The results of the three similarity metrics are separated by semicolons, in the order of SVCCA, TSVD-Reg, and RSA.
Other Macaque-Face dataset. For both SVCCA and TSVD-Reg, Wide-SEW-ResNet14 and Wide-SEW-ResNet8 achieve the first and second highest final similarity scores respectively. But for RSA, TNT-S and Inception-ResNet-V2 take their place and outperform other models by a large margin. As for SEW ResNet and ResNet, the former performs significantly better than the latter for both SVCCA and TSVD-Reg (t = 8.195, p = 0.001; t = 7.528, p = 0.002).
However, the difference is not significant for RSA (t = 1.117, p = 0.327). Specifically, the similarity score of SEW ResNet152 is only slightly higher than that of ResNet152, and at the depth of 50 and 101, SEW ResNet's scores are lower than ResNet's. Macaque-Synthetic dataset. Similar to the results of Allen Brain dataset, no model performs best for all three metrics.
SEW ResNet performs moderately better than ResNet (t = 3.354, p = 0.028; t = 3.824, p = 0.019; t = 2.343, p = 0.079). The only contrary is that SEW ResNet18 performs worse than ResNet18 for RSA. Further, to check the details of comparison between the SNNs and their CNN counterparts, we analyze the trajectories of similarity score across model layers (Figure ).
As for ResNet and SEW ResNet with the same depth, the trends of their similarities across model layers are almost the same, but the former's trajectory is generally below the latter's. In other words, the similarity scores of SEW ResNet are higher than those of ResNet at almost all layers. Taken together, the results suggest that when the overall results that appear below also correspond to the three metrics in this order, unless the correspondence is stated in the text.
architectures and depth are the same, SNNs with spiking neurons perform consistently better than their counterparts of CNNs with an average increase of 6.6%. Besides, SEW ResNet14 also outperforms the brain-like recurrent CNN, CORnet-S, with the same number of layers (see more details in Appendix B). Two properties of SNNs might contribute to the higher similarity scores.
On the one hand, IF neurons are the basic neurons of spiking neural networks. The IF neuron uses several differential equations to roughly approximate the membrane potential dynamics of biological neurons, which provides a more biologically plausible spike mechanism for the network. On the other hand, the spiking neural network is able to capture the temporal features by incorporating both time and binary signals, just like the biological visual system during information processing.
To figure out the distinctions in the functional hierarchy between macaques and mice, for each cortical region, we obtain the normalized depth of the layer that achieves the highest similarity score in each model. Then, we divide models (excluding vision transformers) into two groups based on their depths and conduct investigations on these two groups separately.
A nonparametric ANOVA is applied to each group for testing whether layer depths change significantly across cortical regions. For mouse visual cortex (Figure (a)), taking the deep model group as an example, ANOVA shows overall significant changes in depth across cortical regions for TSVD-Reg and RSA (Friedman's χ 2 = 49.169,
p = 2.0 × 10 −9 ; χ 2 = 19.455, p = 0.002). But there is no significant change for SVCCA (χ 2 = 8.689, p = 0.122). According to these results, the differences in depth across regions are indeterminacy and irregular. Meanwhile, the trends of layer depth between some regions contradict the hierarchy observed in physiological experiments of mice (those between VISp and VISrl for TSVD-Reg and between VISal and VISpm for RSA).
However, for macaque visual cortex (Figure (b)), there are significant differences (t = −5.451, p = 6.5 × 10 −6 ; t = −8.312, p = 2.8 × 10 −9 ; t = −3.782, p = 6.9 × 10 −4 , also taking the deep model group as an example) between V4 and IT, and the trend is consistent with the information processing hierarchy in primate visual cortex.
The comparative analyses of the best layer depths of the shallow and deep model groups also exhibit the differences between macaques and mice. For mouse visual cortex, the best layer depths of shallow models are significantly higher than those of deep models. Compared to deep models, most shallow models achieve the top similarity scores in intermediate and even later layers.
Differently, for macaque visual cortex, the depth of models has little effect on the depth of the most similar layer. What's more, we find that the most similar layer of mouse visual cortex always occurs after the 28 × 28 feature map is downsampled to 14 × 14, which leads to the layer depths' difference between shallow and deep models.
Nevertheless, the best layer of macaque IT appears in the last part of networks, where the feature map has been downsampled more times. In summary, our results might reveal two distinctions in the functional hierarchy between macaques and mice. First, there is a distinct functional hierarchical structure of macaque ventral visual pathway, while there might be no clear sequential functional hierarchy in mouse visual cortex.
One explanation is that the mouse visual cortex is organized into a parallel structure and the function of mouse cortical regions are more generalized and homogeneous than those of macaques. Another possibility would be that even though the sequential relations exist among mouse cortical regions as proposed in anatomical and physiological work, they are too weak for the current deep neural networks to capture.
Additionally, mice perform more complex visual tasks than expected with a limited brain capacity . Consequently, the neural responses of mouse visual cortex may contain more information not related to object recognition that neural networks focus on. Secondly, it is well known that the units in the neural networks get larger receptive fields after downsampling, and through the analyses of differences between two groups of models based on depth, we find the feature map of the best layer for mouse is downsampled fewer times than that for macaque.
Based on these results, we provide computational evidence that the increased ratio of the receptive field size in cortical regions across the mouse visual pathway is smaller than those across the macaque visual pathways, which echoes some physio- Macaque-Face dataset --- Table : The correlation between the similarity scores and the number of parameters.
r is Spearman's rank correlation coefficient. "-" indicates that there is no significant correlation. To explore the processing mechanisms in the visual cortex of macaques and mice, we investigate the model properties from the whole to the details. As shown in Table and 2, we first measure the correlation between the similarity scores and the sizes (i.e. the number of trainable parameters and the depth) of network models.
For Allen Brain mouse dataset, there are significant negative correlations between the similarity scores and the number of parameters for three metrics while there is no correlation with the depth. Conversely, for the two macaque neural datasets, the similarity scores are highly correlated with the depth of networks, but not with the number of parameters.
Specifically, there is a positive correlation for Macaque-Face dataset while a negative correlation for Macaque-Synthetic dataset. (We also apply the linear regression to analyze the correlation between the similarity scores and the model size. The results are consistent with Spearman's rank correlation and are shown in Appendix E).
Based on these results, we further investigate more detailed properties of neural networks to explain the processing mechanisms in the visual cortex. For the mouse dataset, on the one hand, the best layer depths show non-significant changes across the mouse cortical regions as mentioned in the previous section.
On the other hand, the similarity scores of the mouse dataset are only correlated with the number of model parameters but not with the depth of models. It calls into the question whether any detailed structures in the neural networks help to reduce the number of parameters and improve its similarity to mouse visual cortex.
Therefore, we explore the commonalities between models that have the top 20% representation similarities (see Appendix D) for Allen Brain dataset. As expected, the top models contain similar structures, such as fire module, inception module, and depthwise separable convolution. All these structures essentially process information through multiple branches/channels and then integrate the features from each branch.
The models with this type of structure outperform other models (t = 2.411, p = 0.024; t = 3.030, p = 0.007; t = 1.174, p = 0.247). Moreover, we apply the depthwise separable convolution to SNNs, which yields a positive effect. The representation similarity of Spiking-MobileNet is higher than SEW-ResNet50 with a similar depth (+0.8%; +3.9%; +12.1%).
In fact, some studies using multiple pathways simulate the functions of mouse visual cortex to some extent . Our results further suggest that not only the mouse visual cortex might be an organization of parallel structures, but also there are extensive parallel information processing streams between each pair of cortical regions .
For the two macaque datasets with different stimuli, not only are the model rankings significantly different, but also the correlations between the similarity scores and the model depth are totally opposite. These results corroborate the following two processing mechanisms in macaques: the ventral visual stream of primate visual cortex possesses canonical coding principles at different stages; the brain exhibits a high degree of functional specialization, such as the visual recognition of faces and other objects, which is reflected in the different neural responses of the corresponding region (although the face patch AM is a sub-network of IT, they differ in the neural representations).
Besides, as shown in Figure , The calculation and plotting of the trajectories are the same as Figure . the similarity scores of vision transformers reach the maximum in the early layers and then decrease. Differently, the scores of CNNs and SNNs keep trending upwards, reaching the maximum in almost the last layer.
On the other hand, Appendix C shows that vision transformers perform well in Macaque-Face dataset but poorly in Macaque-Synthetic dataset. Considering the features extraction mechanism of vision transformers, it divides the image into several patches and encodes each patch as well as their internal relation by self-attention.
This mechanism is effective for face images that are full of useful information. However, the synthetic image consists of a central target object and a naturalistic background. When vision transformers are fed with this type of stimuli, premature integration of global information can lead to model representations containing noise from the unrelated background.
What's more, when we take all models with the top 20% representation similarities as a whole for analyses, as described in the above paragraph, the properties that enable networks to achieve higher neural similarity are not yet clear. Taken together, the computational mechanism of the better models may reveal core processing divergence to different types of stimuli in the visual cortex.
In this work, we take large-scale neural representation similarity experiments as a basis, aided by analyses of the similarities across models and the visual cortical regions. Compared to other work, we introduce SNNs in the similarity analyses with biological neural responses for the first time, showing that SNNs achieve higher similarity scores than CNNs that have the same depth and almost the same architectures.
As analyzed in Section 3.1, two properties of SNNs might serve as the explanations for their high similarity scores. The subsequent analyses of the models' simulation performance and structures indicate significant differences in functional hierarchies between macaque and mouse visual cortex. As for macaques, we observed a clear sequential hi-erarchy.
However, as for mouse visual cortex, some work ) exhibits that the trend of the model feature complexity roughly matches the processing hierarchy, but other work suggests that the cortex ) is organized into a parallel structure. Our results are more supportive of the latter. Furthermore, we provide computational evidence not only that the increased ratio of the receptive field size in cortical regions across the mouse visual pathway is smaller than those across the macaque visual pathway, but also that there may be multiple pathways with parallel processing streams between mouse cortical regions.
Our results also clearly reveal that the processing mechanisms of macaque visual cortex differ to various stimuli. These findings provide us with new insights into the visual processing mechanisms of macaque and mouse, which are the two species that dominate the research of biological vision systems and differ considerably from each other.
Compared to CNNs, the study of task-driven deep SNNs is just in its initial state. Although we demonstrate that SNNs outperform their counterparts of CNNs, SNNs exhibit similar properties as CNNs in the further analyses. In this work, we only build several new SNNs by taking the hints from the biological visual hierarchy, while many well-established structures and learning algorithms in CNNs have not been applied to SNNs yet.
In addition, the neural datasets used in our experiments are all collected under static image stimuli, lacking rich dynamic information to some certain, which may not fully exploit the properties of SNNs. Given that SNNs perform well in the current experiments, we hope to explore more potential of SNNs in future work.
In conclusion, as more biologically plausible neural networks, SNNs may serve as a shortcut to explore the biological visual cortex. With studies on various aspects of SNNs, such as model architectures, learning algorithms, processing mechanisms, and neural coding methods, it's highly promising to better explain the sophisticated, complex, and diverse vision systems in the future.

Implementation Details of SNNs Spiking Neuron Model

For all SNNs, we use the Integrate-and-Fire (IF) model as the spiking neuron model, which acts as the activation layer in neural networks. As mentioned in , V t , X t and S t denote the state (membrane voltage), input (current) and output (spike) of the spiking neuron model respectively at time-step t, and the dynamics of the IF model can be described as follows:
(1) (2) (3) While V t is the membrane voltage after the trigger of a spike, H t is also the membrane voltage, but after charging and before a spike firing. Θ(x) is the unit step function, so S t equals 1 when H t is greater than or equal to the threshold voltage V thresh and 0 otherwise. Meanwhile, when a spike fires, V t is reset to V reset .
Here, we set V thresh = 1 and V reset = 0. In addition, because Θ(x) is non-differentiable at 0, the surrogate gradient method is applied to approximate the derivative function during back-propagation. Here, we use the inverse tangent function as the surrogate gradient function and the derivative function is
(5) In our experiments on SNNs, we not only use SEW ResNet proposed by ), but also build several new SNNs. On the one hand, we improve the spike-elementwise block in SEW ResNet with new architectures referring to studies on ResNet , as shown in Table . On the other hand, as the multi-branch structures in CNNs increase neural representation similarity to mouse visual cortex, we use depthwise separable convolutions and follow the overall architecture of MobileNetV2 to build the SpikingMobileNet, the basic block of which is shown in Figure .
Our implementation is based on SpikingJelly , an open-source framework of deep SNN. We use the ImageNet dataset to pre-train the new SNNs. Following the settings for training SEW ResNet , we train the models for 320 epochs on 8 GPUs (NVIDIA V100), using SGD with a mini-batch size of 32. The momentum is 0.9 and the weight decay is 0. The initial learning rate is 0.1 and we decay it with a cosine annealing, where the maximum number of iterations is the same as the number of epochs.
For all SNNs, we set the simulation duration T = 4.

Overall model rankings

The results of model rankings are shown in Figure , 8 and 9. We also apply the Spearman's rank correlation to the overall model rankings of different metrics, which is shown in Figure .

Score Comparisons among Model Groups

We conduct comparisons of similarity scores among CNNs, SNNs, and vision transformers. The results are shown in Figure .

Overall CNN rankings

The results of CNN rankings are shown in Figure , 13 and 14.

Correlations between the Model Sizes and the Similarity Scores

The results of linear regression to model sizes and the similarity scores are shown in Figure , 16 and 17.

The ImageNet Accuracy and the Similarity Scores

The results are shown in Figure .

Answer: SNNs have the potential to better model and explain the functional hierarchy and mechanisms of the visual system.


Input: What is the water depth in the Greater Ekofisk Area?

Context:
Filip Fremo Minge – Ekofisk
Author: Filip Fremo Minge
Posted on 1. October 2019 12. October 2019
— Sunset over Ekofisk. Photo: Husmo Foto/Norwegian Petroleum Museum
The three are operated by ConocoPhillips on behalf of the Ekofisk licensees. The area also embraces former producers Albuskjell, Cod, Edda, Tor, West Ekofisk and Tommeliten G.
These fields all lie within production licence 018 apart from Tommeliten G, which was operated by Statoil from 1976 to 2003.
In all, 31 installations have been positioned in the Greater Ekofisk Area.
First Norwegian offshore field
Ekofisk began production on 15 June 1971, following its discovery in the autumn of 1969. Development of the field has occurred in several phases.
Its central facilities were installed during the early 1970s, with oil initially being buoy-loaded into tankers. From 1975, it has been piped to Teesside in the UK. The gas has been landed by pipeline at Emden in Germany from 1977.
ekofisk i et nøtteskall, engelsk
Jacked up six metres
The water depth in the Greater Ekofisk Area is 70-75 metres. However, declining pressure in the Ekofisk reservoir over the years has caused the seabed to subside.
Efforts began as early as 1985 to safeguard the installations against the effects of this development, and the steel platforms in the Ekofisk Complex were jacked up by six metres in 1987.
In addition, a protective breakwater was installed around the Ekofisk tank in 1989. The rate of seabed subsidence has declined sharply in recent years.
Waterflooding improves recovery
The Ekofisk 2/4 K water injection platform became operational in December 1987 as part of efforts to improve Ekofisk’s recovery factor – the share of petroleum in place actually produced.
Waterflooding capacity on the field to help maintain reservoir pressure was later expanded several times, and had reached just over 500 000 barrels per day by 2019.
Measured in barrels of oil equivalent, the recovery factor on Ekofisk has risen from an original estimate of 17 per cent to over 50 per cent.
Ekofisk I and II plus licence extension
The first phase of development and production on Ekofisk began with initial oil output from the converted Gulftide jack-up rig in 1971 and ended with the start-up of Ekofisk II in 1998.
Large parts of the Greater Ekofisk Area were restructured in the latter year, leading to plans for removing 15 installations – 14 steel platforms and the process facilities on the Ekofisk tank.
plattformer, historie, 2004, driftsenter åpnet,
Embla 2/7 D. Photo: ConocoPhillips/Norwegian Petroleum Museum
Designated Ekofisk I, these redundant structures include Ekofisk 2/4 A, 2/4 B, 2/4 FTP, 2/4 Q, 2/4 H, 2/4 R, 2/4 P and 2/4 T.
In addition come the Edda 2/7 C, Albuskjell 1/6 A, Albuskjell 2/4 F, Cod 7/11 A, West Ekofisk 2/4 D, Norpipe 36/22 A and Norpipe 37/4 A installations.
The concrete part of the tank – Ekofisk 2/4 T – will remain. Gulftide was removed as far back as 1974. Two platforms owned by other companies – Ekofisk 2/4 G and 2/4 S – have also gone.
A new plan for development and operation (PDO) of the field (Ekofisk II) was approved in 1994, at the same time as the Ekofisk licence was extended to 2028.
This creates a new Ekofisk Complex with two structures – the Ekofisk 2/4 X wellhead unit installed in the autumn of 1996 and the Ekofisk 2/4 J processing and transport platform in 1997.
Ekofisk II became operational in August 1998 and is intended to produce until 2028. Ekofisk, Eldfisk and Embla are tied back to the new complex, as was Tor until it shut down in December 2015.
Ekofisk West
historie, forsidebilde, 2003, ekofisk vekst godkjent i statsråd
Ekofisk Growth. Illustration: Ståle Ådland
In December 2002, soon after the Conoco-Phillips merger had been announced, the Ekofisk West project was presented to improve oil and gas recovery. Process capacity and reliability on Ekofisk were also to be enhanced.
This development primarily involved the construction and installation of a new platform, Ekofisk 2/4 M, with processing facilities and 24 new wells drilled over five years.
The latter could contribute to improved recovery both because there were more wells and because they would tap new locations in the reservoir. On stream in 2005, 2/4 M was linked to the Ekofisk Complex with a bridge.
Process capacity for produced water was also to be increased through upgrading on Ekofisk 2/4 J and Eldfisk 2/7 E. A third measure concerned laying a power cable from the Ekofisk Complex to 2/4 K in order to make electricity supplies more efficient.
New developments: Eldfisk II and Ekofisk South
Eldfisk 2/7 S løft
The deck of Eldfisk 2/7 S being mated with the steel jacket. Foto: Øyvind Sætre/ConocoPhillips
The plan for development and operation (PDO) of Eldfisk II, approved by the Storting (parliament) on 9 June 2011, includes a new wellhead, process and accommodation platform – Eldfisk 2/7 S.
In addition come 42 new wells as well as upgrades to existing platforms which extend their commercial life.
The PDO for Ekofisk South involves the construction of a new wellhead platform – Ekofisk 2/4 Z – as well as a new subsea water injection facility and 44 additional wells.
ConocoPhillips Norge, 2004.
Ministry of Petroleum and Energy, press release, “Vekstprosjekt på Ekofisk godkjent”, 6 June 2003.
https://www.stortinget.no/no/Saker-og-publikasjoner/Saker/Sak/?p=50343
https://www.stortinget.no/globalassets/pdf/innstillinger/stortinget/2010-2011/inns-201011-398.pdf
https://www.regjeringen.no/no/aktuelt/klart-for-40-nye-ar-pa-ekofisk-feltet/id642376/)
Published 1. October 2019 • Updated 12. October 2019
— Gassterminalen i Emden. Foto: Husmo Foto/Norsk Oljemuseum
Oil terminal in Teesside
Olje- og gassterminalene, engelsk,
Teesside terminal. Brian Henderson Thynne takes samples of refrigerated propane. Photo: Husmo Foto/Norwegian Petroleum Museum
The terminal at Teesside in north-east England receives oil and natural gas liquids (NGL) by pipeline from the Ekofisk field. It comprises stabilisation, NGL fractionation, storage tanks for crude oil and an export port.
After arriving through the Norpipe Oil line, crude and NGL are separated and the oil goes through a stabilisation process before reaching the 10 storage tanks, which each hold 750 000 barrels.
The NGLs go to the fractionation facility, with a daily capacity of 64 000 barrels, for separation into methane, ethane, propane, and normal and iso butane.
While the methane (natural gas) is used to fuel the plant, the other products (now known as liquefied petroleum gases – LPG) are made liquid by cooling and stored for export by sea.
One reason for the choice of Teesside as the landfall for the Ekofisk pipeline was the opportunity it offered to install deepwater quays.
The terminal has four of these, with those for crude oil able to handle tankers up to 150 000 deadweight tonnes. The LPG quays can accept carriers loading as much as 60 000 cubic metres.
Two of the crude oil quays lie on the main channel of the River Tees, while the others have been installed in dredged docks.
Gas terminal in Emden
Gas arriving at the Emden terminal from the Ekofisk Complex enters nine parallel treatment trains for cleaning, metering and onward distribution to the buyers.
The North Sea gas is very clean, and needs only limited treatment to remove small amounts of sulphur compounds using an absorption process. Impure molecules from the gas accumulate on the surface of small particles, which act as filter spheres.
Each of the nine trains comprises four process columns and a process oven. The gas enters the top of a column and leaves through the base after passing through the filter spheres.
That leaves the gas ready for sale, and it is piped to the fiscal metering station before entering the buyer receiving pipelines and distribution network.
Three separate commercial pipeline systems connect to the terminal, operated by Ruhrgas, BEB and Gastransport Services (previously Gasunie) respectively. They pipe the gas away on behalf of the gas buyers.
The Norsea Gas Terminal in Emden was officially opened in September 1977 by Norwegian industry minister Bjartmar Gjerde and Phillips executive Gordon Goerin.
Ranking as the first gas sales deal for the Norwegian continental shelf, the Ekofisk agreement paved the way for later contracts covering other fields off Norway.
Regularity at the Emden terminal has been very high, with its own equipment never causing shutdowns. Maintenance takes place when other parts of the system are off line.
The terminal has a daily capacity of about 2.1 million cubic feet of gas per day.
Gas transport restructured
Norpipe AS owned the gas pipeline from Ekofisk to Emden until the transport system for the Norwegian offshore sector was restructured at 1 January 2003.
Norsea Gas A/S furthermore served as the formal owner of the Emden facility, with Phillips Petroleum and then ConocoPhillips as operator for both pipeline and terminal.
olje- og gassterminalene,
Teesside gas terminal. Photo: Husmo Foto/Norwegian Petroleum Museum
Since 2007, Norway’s state-owned Gassco company has been responsible for technical operation of the facilities on behalf of their owners.
That included operator responsibility for the H7 and B11 booster platforms along the gas pipeline, which were shut down in 2007 and 2013 respectively and have since been removed.
The Gassled partnership is a project collaboration embracing 10 companies which collective own large parts of the gas infrastructure on the Norwegian continental shelf (NCS).
A substantial proportion of Norway’s gas deliveries to Germany continues to arrive at the Emden terminal, including the volumes piped from Ekofisk.
Preliminary planning for a new terminal in the German port began in 2011, with Gassled taking the investment decision for this development in the autumn of 2012.
Construction work began in the following year, with the new facility being built on an unused part of the existing terminal site.
The new terminal has not expanded export capacity. But its functionality is well adapted to future processing needs for fields in the Greater Ekofisk Area and other parts of the NCS sending gas through the Norpipe system.
It was officially opened on 24 May 2016 by Elisabeth Aspaker, the Norwegian government minister for the EU and the European Economic Area. That closed a chapter in Ekofisk’s history.
Source: ConocoPhillips Norge
— Gas pipes at Ekofisk. Photo: Husmo Foto/Norwegian Petroleum Museum
In addition to ConocoPhillips’ own production from Ekofisk, these pipelines carry gas and oil from the company’s fields in the UK sector and from other fields on the Norwegian and British continental shelves.
The three fields in the Greater Ekofisk Area are also tied together by pipelines.
Oil pipeline to Teesside
rørledningene, engelsk,
Pipes and oil tanks at the Teesside plant. Photo: ConocoPhillips/Norwegian Petroleum Museum
The pipeline linking Ekofisk with the terminal for oil and natural gas liquids (NGL) at Teesside on the north-east English coast became operational in October 1975.
Pumps raise the pressure of the oil and NGL before they start their journey to land. Two pumping stations – 37/4 A and 36/22 A – originally stood along the pipeline to maintain this pressure, but have now been disconnected and removed.
The pipeline was installed with the ability to carry a million barrels per day. However, that much capacity has never been required.
In the UK sector, a 24-inch pipeline has been tied in with a Y connection to receive input from several British fields – including the J block developments operated by ConocoPhillips.
Output from the Greater Ekofisk Area is supplemented by crude from Valhall, Hod, Ula and Gyda heading for Teesside, optimising pipeline utilisation and thereby boosting value creation.
The pipeline is owned by Norpipe Oil AS and operated by ConocoPhillips.
Gas pipeline to Emden
Sandbags and gravel were used to cover Norpipe to Emden. Photo: Unknown/Norwegian Petroleum Museum
This pipeline became operational in September 1977. The starting pressure of around 132 bar is provided by compressors on the Ekofisk Complex.
The 443-kilometre distance to Emden was split into three equal sections, with platforms B11 and H7 located at the intermediate points to provide boosting if required.
However, additional compression was seldom needed on the final stage to Emden. H7 was shut down in 2007 and B11 in 2013, and both have since been removed.
These two booster platforms were located in the German sector of the North Sea, while the pipeline also crosses the Danish sector.
The pipeline has been trenched or covered with sand. Its final section passes the island of Juist before making landfall on the coast of East Friesland to the north of Emden.
Its daily capacity is roughly 59.4 million standard cubic metres (2.1 billion cubic feet). In addition to gas from the Greater Ekofisk Area, it carries output from Valhall, Hod, Ula, Gyda and the Statpipe system (primarily Statfjord and Gullfaks).
Posted on 24. June 2017 25. October 2019
Embla 2/7 D
This unmanned wellhead facility is remotely controlled from Eldfisk 2/7 S located 5.2 kilometres to the north, where oil and gas output from the platform is also processed.
Unmanned and remotely operated wellhead platform
On stream 12 May 1993
— Embla 2/7 D. Photo: ConocoPhillips
sokkelkart, illustrasjon, blokker, lisens, forsidebilde, engelsk,
Hand-colored map of the licenses of the first licensing round on the Norwegian continental shelf. Norwegian Continental Shelf Map, 1965.
The Phillips group was awarded block 2/7 as early as 1965, and the Embla reservoir lies in the southern part of this acreage. Drilling began there in 1974 to depths of 4 500-5 000 metres, but pressure and temperature in the wells were too high for testing with the available equipment.
The first production well was not drilled and tested until 1988, followed by a second in 1990. Both yielded very promising results, and the field came on stream in May 1993.
Embla comprises a sandstone reservoir at least 250 million years old. The other fields in the Greater Ekofisk Area comprise fine-grained carbonate rocks deposited about 70 million years ago.
The Embla reservoir has a temperature of 160°C compared with the 125°C normally found in the chalk formations 1 000 metres higher up, and its pressure is almost twice as high.
Fabricated by Heerema in the Netherlands, the Embla 2/7 D jacket (support structure) was installed by the M 7000 crane vessel. It stands 84 metres high and weighs 2 300 tonnes.
A 5.2-kilometre subsea umbilical from Eldfisk comprises three power cables for electricity supply and eight fibreoptic lines handling data transmission and telecommunication.
Eldfisk 2/7 S, embla,
Eldfisk 2/7 S. Photo: ConocoPhillips
The platform has six production wells and an average daily output of roughly 7 000 barrels of oil. All processing and metering took place on Eldfisk 2/7 FTP until 2015, and has now been switched to Eldfisk 2/7 S.
A 14-inch flowline linked 2/7 D with 2/7 FTP and runs today to 2/7 S. Produced at Wick in Scotland, this line was floated out to the field in one piece.
Topside equipment includes the wellhead area, helideck (built by Vindholmen Services in Arendal), crane, control room, workshop, test separator and glycol pump.
Normally unmanned, the platform is maintained as and when required and therefore incorporates a simplified accommodation module with lounge, mess, coffee room, galley, changing room, WC and 12 emergency beds.
More about platforms
Ekofisk 2/4 Z
This installation is a wellhead platform in the Ekofisk Complex.
Gulftide
This four-leg jack-up drilling rig was built in Glasgow during 1967 for Ocean Drilling & Exploration Co.
Posted on 1. September 2019 8. October 2019
— Gulftide with Ekofisk 2/4 A in the background. Photo: Aker Mek. Verksted/Norwegian Petroleum Museum
Gulftide was converted to cope with conditions on Ekofisk in the Åmøy Fjord near Stavanger. This jack-up drilling rig was equipped with process equipment and its derrick, helideck, hangar and legs were reinforced.
To win time, it was decided that the discovery well and three appraisals drilled on Ekofisk by Ocean Viking would be completed for production.
Principles for producing from Gulftide were relatively simple. Output flowed from the subsea wellheads to the platform, where it went through two-stage separation to remove gas and water.
With pressure also reduced, the gas was flared off and the oil sent on by flowlines to two loading buoys where shuttle tankers moored to take on cargo.
utbyggingen,
Tankskipet Donovania laster olje fra lastebøyen på Ekofisk. I bakgrunnen skimtes så vidt Gulftide. Foto: ConocoPhillips/Norsk Oljemuseum
Production could only continue while ships were loading. As soon as one tanker had been filled, the oil stream was diverted to the vessel waiting at the other loading buoy.
The problem with this approach was manifested when weather conditions – strong winds and/or high waves – forced the tankers to leave the buoys.
If that happened, production from the wellheads had to be suspended immediately. Given the prevailing weather on Ekofisk, that happened regularly. Output was halted for 20 per cent of the time during the first year.
https://ekofisk.industriminne.no/wp-content/uploads/sites/2/2019/09/Building-Ekofisk.mp4
Gulftide was replaced as the temporary production installation in 1974 by the permanent Ekofisk 2/4 A (Alpha) and 2/4 B (Bravo) platforms for production, drilling and quarters.
In addition came the Ekofisk 2/4 C (Charlie) production, drilling and compression facility, the Ekofisk 2/4 FTP (field terminal platform) for production and risers, and Ekofisk 2/4 Q for accommodation.
Oil and gas were produced by 2/4 A, B and C through their own wells for processing in their separation plants and piping on the 2/4 FTP for a three-stage separation process.
At the same time, the tanker loading buoys were moved further from the platforms and the Ekofisk 2/4 T oil storage tank became operational.
This facility was extremely advantageous, because it allowed production to continue virtually regardless of whether bad weather prevented tankers from connecting to the buoys.
Ekofisktanken ble satt i drift i 1974. Foto: ConocoPhillips/Norsk Oljemuseum
The 2/4 FTP platform, where oil and gas from the three producing facilities was processed, had been planned to handle the level of output estimated for the main field.
Clear restrictions had been imposed by the Norwegian government on the amount of gas Phillips was allowed to flare. That also set a ceiling for oil production, since gas accompanies it up from the reservoir.
The solution was to install two powerful compression packages on 2/4 C in order to inject the gas under pressure back into the producing formation.
Accommodation facilities had to be provided on the two first platforms, 2/4 A and B. Where 2/4 C and FTP were concerned, however, they were tied together with bridges and to 2/4 Q.
Published 1. September 2019 • Updated 8. October 2019
Posted on 9. April 2019 25. October 2019
Jack-up drilling rig
Built 1967 in Glasgow for Ocean Drilling & Exploration Co.
Began test production on Ekofisk 15 June 1971
Produced on Ekofisk until 1974
— Gulftide at theEkofisk field. Photo: Terje Tveit/Norwegian Petroleum Museum
gulftide,
Gulftide. Photo: Unknown/Norwegian Petroleum Museum
A mere 17 months after the Ekofisk discovery was announced in December 1969, Gulftide was ready to come on stream as a temporary production platform.
Its official inauguration took place on 9 June, with initial test output commencing on 15 June. Full production began on 8 July.
The rig was chosen because it was available on the market. Established equipment for processing oil and gas was tailored to the limited space on board. Separate flowlines carried wellstreams from four subsea wells. Oil, gas and water were separated on board, with the gas flared and the oil piped to two buoys for loading into shuttle tankers.
Work on the process equipment was relatively simple. The problem was to tailor it to the rig. The subsea wellheads had to be reinforced to meet the demands posed by the North Sea, and a buoy loading system needed to be developed for waters where this technology had never been used before.
To gain time, it was decided that the three appraisal wells drilled by Ocean Viking to map the extent of the field – in addition to the discovery well – would be completed for production.
Første testflamme tent på Ekofisk. På Gulftide
1973, Teddy Broadhurst, gulftide,
arbeidsliv, hjelpearbeider
Gulftide, separator – på bildet kan man se at det er fire brønner.
arbeidsliv, gulftide, pionerkultur, arbeid, dekk, Norges første havbunnsbrønner, historie, 1971,
The producers would be topped with hydraulically controlled wellheads. Such equipment had been tried out on the seabed earlier, but on a limited scale and not in the deep and rough waters found on Ekofisk. This challenge was overcome by having the wellheads manufactured and then reinforced at the Phillips base in Dusavik outside Stavanger. Flowlines and control cables would also be laid from each well to Gulftide, with production comingled in a single riser to the topsides.
Weather conditions also represented a major problem when designing the loading buoys. Phillips itself had experience with such facilities, but the concept had only been used before in harbour-like conditions and waters no deeper than 27 metres. They were now to stand in 70 metres in the middle of the North Sea.
Gulftide was converted in the Åmøy Fjord outside Stavanger to cope with conditions on Ekofisk. The processing facilities were installed and reinforcements made to the derrick, helideck, hangar and leg structures.
Gulftide, Ekofisk 2/4 A, boretårn, flare, 1971, utbygging,
Gulftide with Ekofisk 2/4 A in the background. Photo: Aker Mek. Verksted/Norwegian Petroleum Museum
Planning began in late 1970, when Phillips received approval to begin laying the flowlines between wellheads and rig. Brown & Root won this contract, with the first oil pipelines on the Norwegian continental shelf laid by the Hugh W Gordon laybarge.
The production principle on Gulftide was relatively simple. Output flowed from the subsea wellheads to the rig, where it passed through two separation levels to be split into oil and gas while the huge pressure was reduced.
Gas was flared off and the oil was piped to one of the loading buoys where a shuttle tanker was moored. Production could only take place when a ship was present.
Offisiell åpning av norsk oljeproduksjon,
The Greek tanker, Theogennitor, unloads crude oil from loading buoys on the Ekofisk field. Gulftide in the background. Photo: ConocoPhillips/Norwegian Petroleum Museum
As soon as one tanker had become fully laden, the oil flow was switched to the other buoy where another ship was waiting to take on cargo.
The problem with this approach arose when weather conditions meant the tankers had to cast off from the buoys because of strong winds or high waves. The rig then had to shut down production from the wellheads immediately.
Given the weather conditions found on Ekofisk, output regularly had to cease. Production was suspended for 20 per cent of the first year for this reason.
Output began cautiously on 8 July 1971 from a single well. The second producer came on stream that September, the third was ready the following month and all four were producing by February 1972. They each flowed 10 000 barrels of oil per day.
Source: Kvendseth, Stig, Giant discovery, 1988.
Published 9. April 2019 • Updated 25. October 2019
Norpipe H-7
This platform served as a pumping/compressor station to maintain pressure in the 443-kilometre Norpipe gas pipeline from Ekofisk to Emden in Germany, which became operational in September 1977.
Kjappe fakta::
Compressor platform on Ekofisk-Emden gas pipeline
Installed 1976
Operational 1977
Shut down 29 October 2007
Removed 2013
— Norpipe GNSC-H7. Photo: Husmo Foto/Norwegian Petroleum Museum
Gas received initial compression to 132 bar at the Ekofisk Complex. The pipeline was divided into three equal lengths, with Norpipe GNSC B11 positioned at the end of the first third to maintain pressure as and when required.
From there, the gas then travelled the next third of the distance to the second and virtually identical compressor platform, H7.
This was also responsible for maintaining pressure, but additional compression was seldom required on this final leg of the journey to Emden.
Both platforms stood on the German continental shelf, but 48 kilometres of the pipeline also ran across the Danish North Sea sector.
The pipeline is trenched or covered with sand. On its final approach to the coast of East Friesland, it passes beneath the island of Juist before making landfall north of Emden.
Capacity in Norpipe is about 60 million standard cubic metres (scm) or 2.1 billion cubic feet per day. In addition to output from the Ekofisk-area fields, it carries gas from Valhall, Ula and the Statpipe system – primarily Statfjord and Gullfaks. Gas was also transported for a time from Hod and Gyda, but that has ceased.
fritid, Norpipe GNSC-H7,
Magnus Refsland and Werner Hein have pulled the crab trap (full of starfish) on the Norpipe H-7 platform. Photo: Husmo Foto/Norwegian Petroleum Museum
Built in 1976, the B11 platform had six decks. Its permanent staffing totalled 14 people, but various service personnel were also often on board. The regular crew included three in catering.
The 11 Phillips employees comprised the offshore installation manager, the nurse/radio operator, eight operators and a roustabout.
In addition to their direct function, the operators covered various other trades which meant the crew was self-sufficient in most circumstances.
Both platforms obtained a satellite antenna in 1986 which allowed them to received Norwegian TV, while the 24-bed accommodation were redecorated in 1981 and upgraded in the summer of 1990.
Work on the upgrading largely comprised converting all cabins to doubles with shower and WC. The galley and changing rooms were renewed and changing facilities for women provided.
A new module with a lounge for non-smokers, a smoking room, gym and pool room was also installed. During this work, the West Gamma accommodation rig was positioned alongside.
Upgrading equipment on the platform was also initiated in 1990. While the pipeline’s original daily capacity had been estimated at 2 100 million standard cubic feet, this was found to have declined after a number of years to 1 975 million.
To return to the original capacity, the compressors needed to be upgraded and power supply from the turbines increased. This was done both on the Ekofisk tank and on the H7 and B11 platforms. Gas coolers on the tank were replaced as well.
Norpipe GNSC-H7, yrker, radiooperatør,
Radio operator Torleif Førland on the platform Norpipe H-7, with his amateur radio. Photo: Husmo Foto/Norwegian Petroleum Museum
The control systems were also upgraded in parallel. Control panels on turbines and compressors were replaced and metering instruments installed to conduct measurements in this equipment.
While the nearest neighbour to B11 was a Danish oil field, H7 stood in the middle of the shipping channel. M/S Hero broke down 15 nautical miles west of the latter platform at around 13.00 on 12 November 1977.
By 21.00, the ship was still adrift and heading directly for H7, and all 14 crew on the platform made ready to evacuate by helicopter – the waves were too high for the lifeboats. The wreck passed at 21.40 with a clearance of 400 metres.
German cargo carrier Reint collided with H7 on 30 September 1995, despite efforts by the standby ship to avert the threat. Production was halted as a safety measure, but the platform luckily suffered only minor damage. The collision was caused by inadequate watchkeeping on the ship’s bridge.
Operator responsibility for B11 and H7 was transferred at the beginning of 2003 to Norway’s state-owned Gassco company, which runs the Norwegian gas transport network.
This change had little significance for operation of the platforms, since the actual work was still carried out by ConocoPhillips as a technical service provider to Gassco.
H7 was shut down in 2007, and removal had been completed in 2013. In connection with preparations to remove the structure, operator responsibility was transferred to Statoil as the company in charge of the project on Gassco’s behalf.
Published 24. August 2016 • Updated 22. October 2019
Phillips inundates Sola with oil revenues
person by Kristin Øye Gjerde
Stavanger and neighbouring Sola were the first Norwegian local authorities to experience fantastic oil-related growth after the award of the first exploration licences in 1965.
— Phillips er i ferd med å etablere seg på Norscobasen nederst til høyre Ca 1972 Foto: Norsk fly og flyfoto/Norsk Oljemuseum
The Shell refinery at Risavika in Sola was completed two years later, while the Norsco base in Tananger became operational as early as 1966.
But things really took off once the Ekofisk field had been discovered in the autumn of 1969 and started trial production on 14 July 1971.
Operator Phillips Petroleum Company moved its offices from the Dusavik base outside Stavanger to Tananger in Sola, and Shell could finally start refining Norwegian rather than imported crude.
Sola’s population now rose steadily from 8 400 in 1965 to 15 000 two decades later, and jobs grew even faster – from about 2 000 in 1970 to almost 8 000 in 1985. That averages 10 per cent annually.
Phillips and Shell became cornerstone companies. A large part of their workforce, particularly in Phillips, worked offshore. In addition came newly established oil supply firms.
More jobs were also created in retail, public administration, education, health and social care, personal services and so forth.
Although traditional agriculture remained important for the local authority, the number of farmers gradually declined as a result of mechanisation.[REMOVE]Fotnote: This article is based on the chapter “Elverket i Oljealderen” in I det regionale spenningsfelt. Sola Energi 1913-1999, Kristin Øye Gjerde.
Boreskipet Drillship ligger ved kai på Norscobasen i Tananger (1968). Foto: NOM/Norsk Fly og Flyfoto
Boreskipet Drillship ligger ved kai på Norscobasen i Tananger (1968). Foto: Norsk Fly og Flyfoto/Norsk Oljemuseum
The “agio tax”
The sharp rise in Sola’s revenues was attributable entirely to the oil industry, and it found itself in an enviable position during this period. Tax revenues rose even faster than population and jobs.
To give an indication, the local authority’s overall income from wealth and income taxes rose from NOK 9.3 million in 1966 to NOK 198 million in 1990. The biggest growth came in 1978-82, when it averaged 39 per cent a year.[REMOVE]Fotnote: Sola local authority, plans.
The secret behind this sharp increase was the tax paid by the oil companies – primarily Phillips – on agio, or the percentage fee charged when exchanging one currency for another.
Under Norwegian law at the time, the companies paid tax on their interest income to the local authority where they had their head office. In making this rule, however, the government had failed to take account of the considerable sums involved.
As operator of the Greater Ekofisk Area, Phillips had placed capital to be used for new investment in banks around the world – particularly the UK.
These deposits yielded substantial interest payments, and tax was payable on converting this income into Norwegian kroner.[REMOVE]Fotnote: Toralv Torstenbø, former chief executive officer in Sola local authority, interviewed by Kristin Øye Gjerde, 22 February 2001.
Sola council is said to have almost gone into shock the first time Phillips paid this agio tax. It suddenly had more money than it could spend.
During the 1970s and early 1980s, Sola’s municipal income always exceeded the budgeted amount. Large sums could be transferred every year to a capital fund.
Since the local authority was in a growth phase, additional funding was needed for the big developments it faced. While the rest of Norway experienced a slump in the late 1970s, Sola continued in top gear without a sign of unemployment.
Net income tax revenues came to NOK 55.5 million in 1978, while net spending was NOK 31.9 million. And these fantastic results went on improving.
By 1982, wealth and income taxes yielded NOK 203.4 million – compared with a budget of NOK 146 million, which was upgraded to NOK 190 million during the year.
According to Toralv Torstensbø, the financial controller, agio tax accounted for almost half this amount – in other words, as much as the tax paid by all other enterprises, private individuals and industry in Sola.
Its chief executive officer became a little overweening. In his comments on the 1982 budget, he declared that it would be “natural for Sola local authority to feel a strong regional responsibility and not to be too strict about the traditional division of costs between state, county and local authority.”
In line with this open-handed policy, Sola paid for both road projects and an upper secondary modern school which the county council was supposed to fund.[REMOVE]Fotnote: Chief executive officer’s budget proposal for Sola local authority covering 1974-85.
Tightening up petroleum tax
This unexpected prosperity undoubtedly created some jealously in the neighbouring local authorities, and the media began to show an interest in the issue.
Local daily Stavanger Aftenblad interviewed Sola’s chief executive and controller in 1981, when its photographer took a shot which illustrated the boundless wealth – Torstensbø stood showering hundred-krone notes over his colleague.
This story was not only read by the paper’s regular subscribers. The following day, 150 copies were distributed to members of the Storting (parliament).
That in turn prompted Centre Party representative Lars Velsand to make a passionate speech in which he described the position as a misuse of tax revenues.
He called on the government to intervene so that individual local authorities were unable to benefit in this way. Nor was he alone in finding it unreasonable that a small community like Sola should get so much money.
The result was an amendment to the Petroleum Tax Act on 11 June 1982, which specified that the proceeds from the agio tax should be transferred in future to central government.
Løfteskipet Uglen i aksjon ved Norscobasen i juli 1980. Foto: NOM/Norsk Fly og Flyfoto
Løfteskipet Uglen i aksjon ved Norscobasen i juli 1980. Foto: Norsk Fly og Flyfoto/Norsk Oljemuseum
Unfortunately, however, Sola had got used to consuming these revenues. It is easy to learn expensive habits, but not so straightforward to shrug them off again.
Matters had become a little unusual when the council’s executive board adopted the style of the oil company chiefs and took a helicopter outing during an ordinary budget meeting.[REMOVE]Fotnote: Oskar Goa, former chief technical officer in Sola local authority, interviewed by Kristin Øye Gjerde, 23 October 2000.
However, most of the tax money benefitted the general public. Paying for Sola upper secondary school and new national and county highways is an example of this.
The council also invested on local authority school buildings and community facilities such as the big sports complex at Åsen, with an outdoor athletics ground and two modern indoor arenas. Dysjaland and Tananger also acquired new sports arenas.
A new cultural centre built in central Sola has a distinctive architecture in brick and glass, with a grassed roof to blend with the surrounding Jæren landscape. With two stages and a public library, this became the community’s main venue for events and so forth.
The local authority thereby built up a very good infrastructure. Power cables were laid in the same trenches as water and sewage pipes, a network of cycle lanes was built and street lighting installed.
On the downside, virtually all these investments boosted operating expenses. The council’s running costs rose by an annual average of 30 per cent in 1978-84, with the biggest growth in the last three years of the period.
So the calls by Storting representatives to transfer agio tax receipts from councils to central government represented a real threat to local politicians.
Sola joined forces with other local authorities in the same position, including Stavanger, Oslo and Bærum as well as Rogaland county council.
A delegation met the Storting’s standing committee on finance to present their case, and secured a commitment to accept a phased reduction in revenues over four years.
The local authorities would receive 80 per cent of agio tax receipts during the first year, then 60 per cent, 40 per cent and finally 20 per cent.[REMOVE]Fotnote: Amendment to the Petroleum Tax Act adopted on 14 May 1982.
In reality, however, the run-down percentages were adjusted to extend over five years in annual steps of 80, 60, 20, 20 and 20 per cent. The total amount going to the local authorities was the same.
The arrangement was controversial to the last, and also uncertain because it had to be approved in each annual government budget.
Living within its means
After the tax change, Sola’s chief executive officer saw the writing on the wall. It seemed “to be unquestionable that [Sola] has seen its best days in purely financial terms and must return to setting tougher priorities for various assignments,” he asserted in connection with the budget process for 1983.[REMOVE]Fotnote: Chief executive officer’s budget proposal for Sola local authority, 1983.
It took the politicians a little longer to accept this reality, but they were forced to reduce investment and operating expenditures in the years which followed.
Cutting back on the new sports arenas and cultural centre was not very desirable. Nor was it pleasant to have to slow down. But savings had to be made, and long-terms spending plans were removed from the budget for possible reintroduction later.
A raft of measures were stripped from the budget in 1985, such as extensions to and modernisation of schools, sports arenas and swimming pools, a new somatic nursing home, housing for the intellectually disabled and sheltered housing. Grants for national and county roads were reduced.[REMOVE]Fotnote: Chief executive officer’s budget proposal for Sola local authority, 1985.
Once the government’s compensation scheme had ended, Torstensbø – now chief executive officer – told Stavanger Aftenblad that he did not want to paint too gloomy a picture.
“But it’s clear that we must set much more moderate financial priorities than we’ve been used to. To sum up the position, we were previously flush with cash and poor in facilities. We’re now flush with facilities and poor in cash.”[REMOVE]Fotnote: Stavanger Aftenblad, ”Alt blir dyrere i det rike Sola”, 19 May 1987.
Sola kulturhus fotografert vinteren 2004
Rogaland county council also raised the question of whether it would be possible to establish a permanent arrangement which allowed local authorities and counties to benefit from some of the tax revenues paid by local oil companies.
The council pointed out that it was otherwise normal practice for Norwegian companies to pay taxes to the local communities they were based in.
This request was turned by Labour finance minister Gunnar Berge because the councils concerned still benefitted from bigger tax payments by oil company employees and on property.[REMOVE]Fotnote: Stavanger Aftenblad, “Rogaland reiser skattekrav på ny”, 16 January 1988.
According to Torstensbø, this was only partly true. The big oil companies were not so significant for Sola’s income once the agio tax was excluded.
About NOK 2 million was received annually from Phillips, primarily in property tax. The most important taxpayers in the local authority were the roughly 90 companies at Aker Base. These were service providers such as Halliburton, Schlumberger and Baker Hughes.
At the same time, Sola acquired a steadily growing number of affluent residents and a growing share of its revenue came from income tax. Despite the cut-backs, it remained prosperous.
Published 29. July 2019 • Updated 29. July 2019
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