Introduction
Over the years, neuroscientific explanations have gained traction in understanding cognitive functions and their relation to the world around us. Moreover, this field also deals with how we interact with the world around us, making sense of it. One such mode of explanation is network explanations. Network explanations are a way of understanding the brain; specifically speaking, it deals with understanding the structure and function of the brain. In his paper Network Representation and complex systems, Rathkopf states, “Where part-whole decomposition is not possible, network science provides a much-needed alternative method of compressing information about the behavior of complex systems…” (Rathkopf, 2018). Towards the end of his article, Rathkopf changes his view by arguing, “If we attempt to apply network representations in cases where our understanding of the individual components is very poor, we are likely to misrepresent the system, and draw bad inferences as a result.” (Rathkopf, 2018). Perhaps to properly understand and interpret a system, a better view might combine network and mechanistic models to see the whole picture. This aligns with Rathkopf’s argument that “network explanations are always an extension of mechanistic explanations” (Rathkopf, 2018).
Research question
What is the significance of network models in understanding the human brain? Is the combination of network and mechanistic models better for fully comprehending the brain?
Explanatory Models in Neuroscience
Explanatory models are substantially beneficial in the understanding of the human brain. There are several explanatory models in neuroscience, such as the constraint-based intelligibility explanatory model and the mechanistic abstraction model (Levy & Bechtel, 2013). Another significant model in understanding the human brain is the network and several other mathematical and computational models. With constraint-based intelligibility, two pivotal aspects are taken into consideration, which are intelligibility and constraint. Therefore, based on the given aspects, intelligibility encompasses acknowledging an intelligent system with understanding the dependencies between the system’s behavior and the aspects that contribute to or influence the behavior (Cao & Daniel, 2021). Some factors that may be involved include the mechanistic components and biological systems. According to Cao and Daniel (2021), with the biological factors, there may be evolutionary goals characterized by developmental or historical constraints that significantly shape the system and an explanation of how a system is significantly associated with various other widespread dependencies.
Cognitive manipulability involves capturing an aspect of explanation that depends on an individual’s capacity or ability for understanding and the models they build (Cao & Daniel, 2021). Therefore, there is often a need for the given explanations to be from the things that individuals important as cognitively available to them. Cao and Daniel (2021) state that, considering solid explanatory situations, the underlying relationship between the mechanistic facts of a system and the aspect that the system does are directly and immediately apparent more s with simple systems (Cao & Daniel, 2021). Therefore, in the given case, the significant path to intelligibility is through a comprehensive mathematical description of the system’s dynamics that allow for identifying the behavior within different situations. Moreover, this is often without the need to perform all the relevant calculations that are pivotal for the prediction.
With the mechanistic model of explanation in neuroscience, models have the explanatory force of virtue based on the fact that they describe the causes and the mechanisms that play a role in the maintenance of the phenomenon in a given domain, including the production and the aspects that underlie (Lu et al., 2022). It is prudent to understand that mechanistic explanations entail the refusal to rest content with the phenomenal descriptions, including how the models and sketches have guided the crucial achievements in the low-eve fields of neuroscience, such as molecular biology and electrophysiology. Consequently, important advances in the mechanistic explanation reveal new knobs and levers within the brain that can manipulate how to give parts behave (Lu et al., 2022). Further, in cognitive neuroscience, the demands of the mechanist model need to be effectively met.
The Haken-Keslo-Bunz Model of Bimanual Coordination (HKB model) has been a significant area of focus in systems neuroscience and computation neuroscience for years. This model mainly accounts for behavioral data (Zednik, 2018). Other than the given model, neuroscientists also often employ simulation models, which are often realistic simulations of the networks on the computer where the attained behavior is studied later. Zednik (2018) add that not only are simulation models neutrally oriented but also incorporate the aspect of the brain function principle and organization. The fundamental objective of simulation models is not to depict networks in the biological brain but to investigate the potential capabilities of various network types in principle. Simulation models are frequently utilized to investigate the impact of modifications to the composition or arrangement of a network, which is regarded as an abstract mathematical construct rather than a concrete biological structure, on its behavioral dynamics or processing of information capabilities (Zednik, 2018).
Dynamic models substantially play an important role in understanding brain function in neuroscience. The given models often highlight significant patterns of change over time in various scientific domains (Zednik, 2018). Within network neuroscience, dynamic models are often traditionally used to describe biological or artificial neural networks’ overall activity over time. Other dynamical models define variables that relate to the states of specific neural units or brain regions and capture how these states rely on the simultaneous activity of other units (Zednik, 2018). These states are said to be dependent on the activity of other units. Notably, dynamical models provide researchers the ability to use the techniques and ideas of dynamical systems theory in order to define the transient or asymptotic behavior of a certain network or unit. Other than the dynamic models Network models also play a crucial role as an explanatory model in neuroscience. According to Craver (2016), adding to the importance of anatomical and vital functional interactions, network models build on the concept by providing vital insight into the most basic structures and construction with mechanisms that propagate the integrative neural process. With significant evidence from both physiological and anatomical perspectives, the cognitive process ideology involves a network of interactions between neuronal populations and brain regions.
Network Models
Network models are one of the most utilized explanatory models used in the understanding of brain functions. According to Sporns (2014), network analysis or model is a graph theory field that is directed towards the organization of pairwise relations, effectively providing a group of concepts for the description of different kinds of networks. In addition, its role is to also aid with the description and discovery of organizations in systems with densely connected components. According to given researchers and philosophers, network models often provide non-mechanistic explanation forms (Sporns, 2014). Further, focusing on the concept of network analysis, network models are often made up of nodes that stand for the network’s relata, among other aspects, such as edges that stand for their relations. A node’s degree refers to the number of edges it shares with the other nodes (Sporns, 2014). Further, the path length between the two nodes refers to the minimum number of edges needed to link them. Therefore, this means that within any given graph, there is often a path between the two nodes (Srivastava et al., 2022). Consequently, when considering a regular network, every node has similar degrees, whereas, in random networks, there is often a random distribution of networks. Nevertheless, few reals networks are often described as usefully random or regular (Pathak et al., 2022). Further, the network model provides an opportunity to describe complex systems.
According to Sporns (2014), network models are very important in understanding brain functions in various ways. For instance, network models can be effectively integrated with mathematic explanations. Therefore network models can be useful in the provision of distinctively mathematical explanations. This is because given brain aspects are often mathematical (Lu et al., 2022). Compared to regular or random networks, small-world networks have a mean route length that is more resistant to the random deletion of nodes than either regular or random networks. This is likely why brain function is so resilient in random cell loss. Small-world networks provide quicker signal transmission compared to random networks, and oscillators linked in small-world networks easily synchronize with one another (Bassett et al., 2018). They are mathematical truths and their significance lies in their ability to explain other things.
Network models are also beneficial in mechanistic explanations. This benefit is often associated with the use of the network models. These uses often include the description of structural connectivity, functional connectivity, and causal connectivity (Faskowitz et al., 2021). Notably, network models of functional connectivity are only sometimes developed for the representation of explanatory information. The network model also provides an appealing framework and guideline for studying significant relationships within interconnected brain mechanisms with their behavior (Kaplan & Craver, 2011). Imperatively, network models are also pivotal in understanding neural codes and network function, and more so at the cellular level. In other dimensions, network models can be very useful in understanding population and ensemble code, including network functions both beyond and at the ensemble level.
It is not a secret that the network model is significantly transforming many areas of neuroscience. The model plays an important role in new neuroscience problems and the development of new techniques for solving the problem without altering the norms of the explanation (Faskowitz et al., 2021). This also means that the explanatory power of the network model stems from its ability to represent the situations of the phenomenon both constitutively and etiologically. Notably, the network models of causal and structural connectivity are often mechanistic (Pathak et al., 2022). The given mechanistic aspect is often associated with the sense that they derive their explanatory force from their virtue explanation. Further, it is also derived from the sense that they explain by representing ways in which explain the phenomenon is situated within the causal structure of the world (Lu et al., 2022). The general conclusion is grounded on the fact that network models are often effective in the description of things that are not and are not intended to exist as explanations of anything at all. Therefore, just as causal-mechanical theories for etiological explanation played a significant solution to many of the problems and challenges confronting Hempel’s covering-law model by adding a set of ontic constraints that sort better explanations from bad (Lu et al., 2022). A causal mechanical theory of network explanation effectively clarifies the reason for some networks as explanatory models, whereas others are not considered explanatory models.
Network models effectively identify network hubs within the brain with ease through the utilization of descriptive measures, which may include centrality, node degree, and the perturbation of vulnerability probing points (Sporns, 2014). Further, based on several structural network research findings on multimodal regions based on distinct and diverse complex response profiles, network models have played an important role in adding a new dimension to the findings by revealing an effective structural basis for multimodality. Moreover, this is often within the interregional projections topology (Sporns, 2014). The network models have also substantially contributed to identifying modules and the network communities. Consequently, the network models have also contributed to the objective identification and the significant quantification of the local and global network attributes creating room for the characterization of different networks across individuals.
Despite the many benefits of the network model in understanding the functions of the brain in neuroscience, an effective understanding of the brain functions, network models need to include mechanistic models, which will play a critical role in the expansion of the network models definition (Rathkopf, 2018). It is imperative to note that the brain is a complex system which means that it requires more complex models and integrated models to understand the brain functionality. This complexity makes the explanatory aspect in itself more challenging as a conceptual hurdle (Sporns, 2014). Therefore including a mechanistic model in the networks model would be more effective in understanding brain function. Further, the outcomes of integrating the mechanistic model and the network model is a more improved understanding and acknowledgment of the different ways and forms in which mechanistic models’ connect with network models is deployed within neuroscience and the general explanation.
Conclusion
Summative network models have been important in revealing the ways in which structural patterns and functional connections encourage and facilitate the interplay between the integrated and segregated processes. According to the network mode, one of the major aspects that arises is that the network communities and the module substantially associated with particular behavior domains and cognition are linked within brain function. Further, the network models are also important in adding vital insight through the revealing of patterns of interactions on the models of brain activation and the application of quantitative measures of network topologies. Consequently, the network models also provide a theoretical framework that includes global and local processes resolving the conflict between distributed and localized processing. However, it is vital to note that network approaches are susceptible to different forms of challenges, such as with data acquisition and network definition. Further, other significant challenges are associated with the interpretation of network measures within the structural and functional aspects of the brain function. Therefore, in order to establish an effective understanding of how the brain functions work in relation to their structural aspects, there is a need for the network models to include the branch of mechanistic models in expanding the definition of network models.
References
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