nils-herrmann/clean_scirepeval huggingface dataset:
Never cross the path of a traveling salesman: The neural network generation of Halstead-Reitan trail making tests

The Halstead-Reitan Trail Making Test (TMT) is one of the most widely used neuropsychological instruments for the assessment of brain damage. Despite its usefulness, however, the TMT has two major disadvantages. It has not been constructed in a principled manner that would facilitate systematic investigation, and there is no established procedure for generating equivalent, but stochastically different, test forms. The reason is that the generation of self-avoiding TMT pathways resembles the finding of near-optimal solutions to the Euclidean Traveling Salesman Problem (TSP) and constitutes a computational problem that is NP-complete. This article describes a practical approach to the problem of generating stochastically different test forms. This approach employs anelastic net neural network to generate TMT forms based on self-avoiding, near-optimal paths, and closed circuits. The usefulness and limitations of this solution are discussed briefly in relation to alternative and complementary problems and procedures.
Interactive visualisation of similarity structures

In many operational contexts, data visualisation techniques are used to convey accurate and easily comprehended information to human analysts. One popular approach is to represent domains of interest in terms of the objects they contain and measures of the similarity between them. Spatial data visualisations can then be generated which place objects so that those which are more similar lie nearer each other. Unfortunately, this approach has limitations when dealing with domains which require the display of very high-dimensional configurations or incorporate fundamentally non-metric structures. This paper develops and demonstrates an interactive approach to spatial data visualisation which addresses both of these weaknesses. By allowing analysts to select objects within the domain and view spatial configurations adjusted to represent the similarity relationships from the perspective of those objects, it is shown to be possible to build up an accurate and useful understanding of high-dimensional and non-metric domains.
Neural Network and Tree Search Algorithms for the Generation of Path-Following (Trail-Making) Tests

Due to their sensitivity to cognitive impairment, simplicity of administration and low cost, path-following tests, such as the HalsteadReitan Trail Making Test (TMT) and the Zahlen-Verbindungs-Test (ZVT), are among the most frequently used neuropsychological tests in the assessment of brain damage. Despite their usefulness, however, such tests have been constructed in an unprincipled manner which does not facilitate repeated testing, systematic investigation or theoretical interpretation. The reason for their unprincipled development is that the selective generation of the required pathways constitutes a computational problem that is NPcomplete. This paper outlines two practical solutions to the problem of generating ZVT matrices. The first employs a Hopfield-style neural network and the second employs a heuristic-driven tree search. The characteristics of the pathways generated by these two solutions are compared, and ways of manipulating the relative difficulty of alternative test forms are discussed.
The Connectionist Construction of Psychological Spaces

The application of connectionist learning procedures to the development of psychological internal representations requires a constraining theory of mental structure. The psychological space construct is advanced for this role and, consequently, a connectionist network which learns the multi-dimensionally scaled representations of a set of stimuli is developed. The model assumes that the function relating similarity to distance in psychological space is an exponential decay function, operates under the family of Minkowskian metrics and is able to determine the appropriate dimensionality of the psychological spaces it derives. The model is demonstrated on both separable and integral stimuli, and the validity of its application of gradient descent optimization principles over the city-block metric is examined. Several modelling extensions are discussed, including means by which the model might learn more general psychophysical mappings, and be able to derive internally the measures of psychological similarit...
Separation of overlapping subpopulations by mutual information

Identifying ancestral sequences is an important first step in understanding population history and dynamics. However, several interesting cases including human genetic variation feature highly overlapping subpopulations, whose ancestral subpopulations differ by fewer than the average number of mutations between members of the same population. In such cases both flat and hierarchical distance-based clustering methods optimize the wrong objective function, and will therefore fail to recover the correct population structure. We present an algorithm for identifying substructure in overlapping populations based on the observation that variations at different sequence positions are uncorrelated. While motivated by a particular biological application, the algorithm can be applied to both discrete and continuous clustering problems for which distancebased measures are inappropriate. We demonstrate the algorithm’s effectiveness in reconstructing human subpopulations.
Encoding Stimulus Information by Spike Numbers and Mean Response Time in Primary Auditory Cortex

Neurons can transmit information about sensory stimuli via their firing rate, spike latency, or by the occurrence of complex spike patterns. Identifying which aspects of the neural responses actually encode sensory information remains a fundamental question in neuroscience. Here we compared various approaches for estimating the information transmitted by neurons in auditory cortex in two very different experimental paradigms, one measuring spatial tuning and the other responses to complex natural stimuli. We demonstrate that, in both cases, spike counts and mean response times jointly carry essentially all the available information about the stimuli. Thus, in auditory cortex, whereas spike counts carry only partial information about stimulus identity or location, the additional availability of relatively coarse temporal information is sufficient in order to extract essentially all the sensory information available in the spike discharge pattern, at least for the relatively short stimuli (< ∼ 100 ms) commonly used in auditory research.
Discrete profile alignment via constrained information bottleneck

Amino acid profiles, which capture position-specific mutation probabilities, are a richer encoding of biological sequences than the individual sequences themselves. However, profile comparisons are much more computationally expensive than discrete symbol comparisons, making profiles impractical for many large datasets. Furthermore, because they are such a rich representation, profiles can be difficult to visualize. To overcome these problems, we propose a discretization for profiles using an expanded alphabet representing not just individual amino acids, but common profiles. By using an extension of information bottleneck (IB) incorporating constraints and priors on the class distributions, we find an informationally optimal alphabet. This discretization yields a concise, informative textual representation for profile sequences. Also alignments between these sequences, while nearly as accurate as the full profile-profile alignments, can be computed almost as quickly as those between individual or consensus sequences. A full pairwise alignment of SwissProt would take years using profiles, but less than 3 days using a discrete IB encoding, illustrating how discrete encoding can expand the range of sequence problems to which profile information can be applied.
Applying an artificial neural network to warfarin maintenance dose prediction.

Oral anticoagulation with warfarin can lead to life-threatening events as a result of either over-anticoagulation or undertreatment. One of the main contributors to an undesirable warfarin effect is the need to adjust its daily dose for a specific patient. The dose is adjusted empirically based on the experience of the clinician, a method that is often imprecise. There is currently no other well-accepted method for predicting the maintenance dose of warfarin.



To describe the application of an artificial neural network to the problem of warfarin maintenance dose prediction.



We designed a neural network that predicts the maintenance dose of warfarin. Data on 148 patients attending a large anticoagulant clinic were collected by file review. Using correlational analysis of the patients' data we selected the best input variables. The network was trained by using the back-propagation algorithm on a subset of our data and the results were validated against the rest of the data. We used a multivariate linear regression to create a comparable model.



The neural network generated reasonable predictions of the maintenance dose (r = 0.823). The results of the linear regression model were similar (r = 0.800).



Neural networks can be applied successfully for warfarin maintenance dose prediction. The results are promising, but further investigation is needed.
Spike-Timing-Dependent Plasticity and Relevant Mutual Information Maximization

Synaptic plasticity was recently shown to depend on the relative timing of the pre- and postsynaptic spikes. This article analytically derives a spike-dependent learning rule based on the principle of information maximization for a single neuron with spiking inputs. This rule is then transformed into a biologically feasible rule, which is compared to the experimentally observed plasticity. This comparison reveals that the biological rule increases information to a near-optimal level and provides insights into the structure of biological plasticity. It shows that the time dependency of synaptic potentiation should be determined by the synaptic transfer function and membrane leak. Potentiation consists of weight-dependent and weight-independent components whose weights are of the same order of magnitude. It further suggests that synaptic depression should be triggered by rare and relevant inputs but at the same time serves to unlearn the baseline statistics of the network's inputs. The optimal depression curve is uniformly extended in time, but biological constraints that cause the cell to forget past events may lead to a different shape, which is not specified by our current model. The structure of the optimal rule thus suggests a computational account for several temporal characteristics of the biological spike-timing-dependent rules.
Are There Representations in Embodied Evolved Agents? Taking Measures

The question of conceptual representation has received considerable attention in philosophy, neuroscience and embodied evolved agents. Numerous theories on the interpretation of the term ‘representation’ exist, and many arguments have been made for and against the existence of representations in animate and animat agents. Our work studies this question in evolved artificial embodied agents in a quantitatively rigorous manner, for the first time. We develop two measures, based on information theory, to account for representations. These measures are studied by applying them to evolved agents performing a visual categorization, generalized XOR task. Our results show that having quantitative measures still leaves one with arbitrary “threshold values” decisions which permit wide freedom in determining the existence of representations. However, and more importantly, our results show that information-theoretic measures can still be used efficiently to identify discriminative neural patterns and internal structures that characterize a representation, if the latter is formed.
Effective Neuronal Learning with Ineffective Hebbian Learning Rules

In this article we revisit the classical neuroscience paradigm of Hebbian learning. We find that it is difficult to achieve effective associative memory storage by Hebbian synaptic learning, since it requires network-level information at the synaptic level or sparse coding level. Effective learning can yet be achieved even with nonsparse patterns by a neuronal process that maintains a zero sum of the incoming synaptic efficacies. This weight correction improves the memory capacity of associative networks from an essentially bounded one to a memory capacity that scales linearly with network size. It also enables the effective storage of patterns with multiple levels of activity within a single network. Such neuronal weight correction can be successfully carried out by activity-dependent homeostasis of the neuron's synaptic efficacies, which was recently observed in cortical tissue. Thus, our findings suggest that associative learning by Hebbian synaptic learning should be accompanied by continuous remodeling of neuronally driven regulatory processes in the brain.
Beyond Hebbian plasticity : Effective learning with ineffective Hebbian learning rules

In this paper we revisit the classical neuroscience paradigm of Hebbian learning. We find that a necessary requirement for effective associative memory learning is that the efficacies of the incoming synapses should be uncorrelated. This is difficult to achieve in a robust manner by Hebbian synaptic learning, since it depends on network level information. Effective learning can yet be achieved by a neuronal process that maintains a zero sum of the incoming synaptic efficacies. This normalization drastically improves the memory capacity of associative networks, from an essentially bounded capacity to one that linearly scales with the network’s size. Such neuronal normalization can be successfully carried out by activity-dependent homeostasis of the neuron’s synaptic efficacies, which was recently observed in cortical tissue. Thus, our findings strongly suggest that effective associative learning with Hebbian synapses alone is biologically implausible and that Hebbian synapses must be continuously remodeled by neuronally-driven regulatory processes in the brain. ∗To whom correspondence should be addressed
Effective Learning Requires Neuronal Remodeling of Hebbian Synapses

This paper revisits the classical neuroscience paradigm of Hebbian learning. We find that a necessary requirement for effective associative memory learning is that the efficacies of the incoming synapses should be uncorrelated. This requirement is difficult to achieve in a robust manner by Hebbian synaptic learning, since it depends on network level information. Effective learning can yet be obtained by a neuronal process that maintains a zero sum of the incoming synaptic efficacies. This normalization drastically improves the memory capacity of associative networks, from an essentially bounded capacity to one that linearly scales with the network's size. It also enables the effective storage of patterns with heterogeneous coding levels in a single network. Such neuronal normalization can be successfully carried out by activity-dependent homeostasis of the neuron's synaptic efficacies, which was recently observed in cortical tissue. Thus, our findings strongly suggest that effective associative learning with Hebbian synapses alone is biologically implausible and that Hebbian synapses must be continuously remodeled by neuronally-driven regulatory processes in the brain.
Effective And Optimal Storage of Memory Patterns With Variable Coding Levels

This paper studies the storage of memory patterns with varying coding levels (fraction of firing neurons within a pattern) in an associative memory network. It was previously shown that effective memory storage (that scales with the network’s size) requires that the synaptic modification rule used during learning explicitly depends on the coding level of the stored memory patterns. We show that the memory capacity of networks storing variably coded memory patterns is inherently bounded and does not scale with the network’s size. These results question the biological feasibility of associative memory learning that uses synaptic level information only. However, we show that applying a neuronal weight correction mechanism that uses local neuronal level information, provides effective memory capacity even when the coding levels vary considerably. Using neuronal weight correction yields near optimal memory performance even with non-optimal synaptic learning rules. These findings provide further support to the idea that neuronal level normalization processes play an important role in governing synaptic plasticity and may be crucial for learning in the nervous system. ∗To whom correspondence should be addressed. 0
Neuronal Regulation: A Mechanism for Synaptic Pruning During Brain Maturation

Human and animal studies show that mammalian brains undergo massive synaptic pruning during childhood, losing about half of the synapses by puberty. We have previously shown that maintaining the network performance while synapses are deleted requires that synapses be properly modified and pruned, with the weaker synapses removed. We now show that neuronal regulation, a mechanism recently observed to maintain the average neuronal input field of a postsynaptic neuron, results in a weight-dependent synaptic modification. Under the correct range of the degradation dimension and synaptic upper bound, neuronal regulation removes the weaker synapses and judiciously modifies the remaining synapses. By deriving optimal synaptic modification functions in an excitatory-inhibitory network, we prove that neuronal regulation implements near-optimal synaptic modification and maintains the performance of a network undergoing massive synaptic pruning. These findings support the possibility that neural regulation complements the action of Hebbian synaptic changes in the self-organization of the developing brain.
Neuronal Regulation Implements Efficient Synaptic Pruning

Human and animal studies show that mammalian brain undergoes massive synaptic pruning during childhood, removing about half of the synapses until puberty. We have previously shown that maintaining network memory performance while synapses are deleted, requires that synapses are properly modified and pruned, removing the weaker synapses. We now show that neuronal regulation, a mechanism recently observed to maintain the average neuronal input field, results in weight-dependent synaptic modification. Under the correct range of the degradation dimension and synaptic upper bound, neuronal regulation removes the weaker synapses and judiciously modifies the remaining synapses. It implements near optimal synaptic modification, and maintains the memory performance of a network undergoing massive synaptic pruning. Thus, this paper shows that in addition to the known effects of Hebbian changes, neuronal regulation may play an important role in the self-organization of brain networks during development.
Synaptic pruning in development: a novel account in neural terms

A fundamental phenomenon in brain development is the reduction in the amount of synapses that occurs between early childhood and puberty. Recent studies in humans, substantiated by studies in monkeys 1,2, show that synaptic density grows steadily until the age of 2 years, remains constant for a few years and then decreases continuously until puberty3,4. The peak level of synaptic density in early childhood is up to 50% to 100% higher than the adult level depending on the brain region.
Synaptic Pruning in Development: A Computational Account

Research with humans and primates shows that the developmental course of the brain involves synaptic overgrowth followed by marked selective pruning. Previous explanations have suggested that this intriguing, seemingly wasteful phenomenon is utilized to remove erroneous synapses. We prove that this interpretation is wrong if synapses are Hebbian. Under limited metabolic energy resources restricting the amount and strength of synapses, we show that memory performance is maximized if synapses are first overgrown and then pruned following optimal minimal-value deletion. This optimal strategy leads to interesting insights concerning childhood amnesia.
Health. Care. Anywhere. Today.

What if clinical quality medical equipment were available to every consumer in a form factor that was inexpensive, accurate, and easy to use? What if this equipment provided information that previously was un-measurable or very difficult to measure? What if the physiological state of individuals, at resolutions measured in thousandths of a second instead of in visits per year, could be measured easily, making it possible to ascertain caloric intake and expenditure, patterns of sleep, contextual activities such as working-out and driving, even parameters of mental state and health. What aspect of healthcare would not change? We present a system that is available today that enables this vision. This award-winning multi-channel wearable physiological monitor has enabled the collection of more than 90 million minutes of data in natural settings from thousands of subjects engaged in diverse activities. Data modeling efforts are resulting in applications that present meaningful and actionable information in real-time to users and their designated collaborators (physicians, family members, counselors, coaches, etc.) We describe the SenseWear system, its design, and a summary of validation studies, current commercial applications, and ongoing research. This discussion will show how the convergence of design for wearability, advances in machine learning, and improvements in wireless technology will manifest the future of health care as personal, ubiquitous, and collaborative.
Use of Architecture-Altering Operations to Dynamically Adapt a Three-Way Analog Source Identification Circuit to Accommodate a New Source

The problem of source identification involves correctly classifying an incoming signal into a category that identifies the signal's source. The problem is difficult because information is not provided concerning each source's distinguishing characteristics and because successive signals from the same source differ. The source identification problem can be made more difficult by dynamically changing the repertoire of sources while the problem is being solved. We used genetic programming to evolve both the topology and the sizing (numerical values) for each component of an analog electrical circuit that can correctly classify an incoming analog electrical signal into three categories. Then, the repertoire of sources was dynamically changed by adding a new source during the run. The paper describes how the architecture-altering operations enabled genetic programming to adapt, during the run, to the changed environment. Specifically, a three-way source identification circuit was evolved and then adapted into a four-way classifier, during the run, thereby successfully handling the additional new source.