141,153 research outputs found
On Reverse Engineering in the Cognitive and Brain Sciences
Various research initiatives try to utilize the operational principles of
organisms and brains to develop alternative, biologically inspired computing
paradigms and artificial cognitive systems. This paper reviews key features of
the standard method applied to complexity in the cognitive and brain sciences,
i.e. decompositional analysis or reverse engineering. The indisputable
complexity of brain and mind raise the issue of whether they can be understood
by applying the standard method. Actually, recent findings in the experimental
and theoretical fields, question central assumptions and hypotheses made for
reverse engineering. Using the modeling relation as analyzed by Robert Rosen,
the scientific analysis method itself is made a subject of discussion. It is
concluded that the fundamental assumption of cognitive science, i.e. complex
cognitive systems can be analyzed, understood and duplicated by reverse
engineering, must be abandoned. Implications for investigations of organisms
and behavior as well as for engineering artificial cognitive systems are
discussed.Comment: 19 pages, 5 figure
Identifying interactions in the time and frequency domains in local and global networks : a Granger causality approach
Background
Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality.
Results
Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered.
Conclusions
The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data
Reverse-Engineering the brain: The parts are as complex as the whole.
The purpose of this paper is to review the current state of neuroscience research with a focus on what has been achieved to date in unraveling the mysteries of brain operations, major research initiatives, fundamental challenges, and potentially realizable objectives. General research approaches aimed at constructing a wiring diagram of the brain (i.e., connectome), determining how the brain encodes and computes information, and whole brain simulation attempts are reviewed in terms of strategies employed and difficulties encountered. While promising advances have been made during the past 50 years due to electron microscopy, the development of new experimental methods, and the availability of computer-enabled high throughput imaging systems, brain research is still greatly encumbered by inadequate monitoring and recording capabilities. Four hypotheses relating to comprehension through the assembly of parts, formation of memories, influence of genes, and synapse formation are described as plausible explanations even though they cannot be validated at this time. By assessing the feasibility of overcoming the principal problems that beleaguer brain research in comparison with the potential benefits that can be derived from even partial achievement of the goals the author concludes that the significant investment of government funding is justified
How do people learn how to plan?
How does the brain learn how to plan? We reverse-engineer people's underlying learning mechanisms by combining rational process models of cognitive plasticity with recently developed empirical methods that allow us to trace the temporal evolution of people's planning strategies. We find that our Learned Value of Computation model (LVOC) accurately captures people's average learning curve. However, there were also substantial individual differences in metacognitive learning that are best understood in terms of multiple different learning mechanisms -- including strategy selection learning. Furthermore, we observed that LVOC could not fully capture people's ability to adaptively decide when to stop planning. We successfully extended the LVOC model to address these discrepancies. Our models broadly capture people's ability to improve their decision mechanisms and represent a significant step towards reverse-engineering how the brain learns increasingly more effective cognitive strategies through its interaction with the environment
Automatic reconstruction of 3D neuron structures using a graph-augmented deformable model
Motivation: Digital reconstruction of 3D neuron structures is an important step toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low single-to-noise ratio and discontinued segments of neurite patterns
Reverse-engineering the cortical architecture for controlled semantic cognition.
We employ a reverse-engineering approach to illuminate the neurocomputational building blocks that combine to support controlled semantic cognition: the storage and context-appropriate use of conceptual knowledge. By systematically varying the structure of a computational model and assessing the functional consequences, we identified the architectural properties that best promote some core functions of the semantic system. Semantic cognition presents a challenging test case, as the brain must achieve two seemingly contradictory functions: abstracting context-invariant conceptual representations across time and modalities, while producing specific context-sensitive behaviours appropriate for the immediate task. These functions were best achieved in models possessing a single, deep multimodal hub with sparse connections from modality-specific regions, and control systems acting on peripheral rather than deep network layers. The reverse-engineered model provides a unifying account of core findings in the cognitive neuroscience of controlled semantic cognition, including evidence from anatomy, neuropsychology and functional brain imaging
Digital Twin Brain: a simulation and assimilation platform for whole human brain
In this work, we present a computing platform named digital twin brain (DTB)
that can simulate spiking neuronal networks of the whole human brain scale and
more importantly, a personalized biological brain structure. In comparison to
most brain simulations with a homogeneous global structure, we highlight that
the sparseness, couplingness and heterogeneity in the sMRI, DTI and PET data of
the brain has an essential impact on the efficiency of brain simulation, which
is proved from the scaling experiments that the DTB of human brain simulation
is communication-intensive and memory-access intensive computing systems rather
than computation-intensive. We utilize a number of optimization techniques to
balance and integrate the computation loads and communication traffics from the
heterogeneous biological structure to the general GPU-based HPC and achieve
leading simulation performance for the whole human brain-scaled spiking
neuronal networks. On the other hand, the biological structure, equipped with a
mesoscopic data assimilation, enables the DTB to investigate brain cognitive
function by a reverse-engineering method, which is demonstrated by a digital
experiment of visual evaluation on the DTB. Furthermore, we believe that the
developing DTB will be a promising powerful platform for a large of research
orients including brain-inspiredintelligence, rain disease medicine and
brain-machine interface.Comment: 12 pages, 11 figure
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