1,023 research outputs found
Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail
Replicability and reproducibility of computational models has been somewhat understudied by âthe replication movement.â In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original code, serve different functions and fail for different reasons. This means that measures designed to improve model replicability may not enhance (and, in some cases, may actually damage) model reproducibility. We claim that although both are undesirable, low model reproducibility poses more of a threat to long-term scientific progress than low model replicability. In our opinion, low model reproducibility stems mostly from authors' omitting to provide crucial information in scientific papers and we stress that sharing all computer code and data is not a solution. Reports of computational studies should remain selective and include all and only relevant bits of code
Shape recognition and classification in electro-sensing
This paper aims at advancing the field of electro-sensing. It exhibits the
physical mechanism underlying shape perception for weakly electric fish. These
fish orient themselves at night in complete darkness by employing their active
electrolocation system. They generate a stable, high-frequency, weak electric
field and perceive the transdermal potential modulations caused by a nearby
target with different admittivity than the surrounding water. In this paper, we
explain how weakly electric fish might identify and classify a target, knowing
by advance that the latter belongs to a certain collection of shapes. Our model
of the weakly electric fish relies on differential imaging, i.e., by forming an
image from the perturbations of the field due to targets, and physics-based
classification. The electric fish would first locate the target using a
specific location search algorithm. Then it could extract, from the
perturbations of the electric field, generalized (or high-order) polarization
tensors of the target. Computing, from the extracted features, invariants under
rigid motions and scaling yields shape descriptors. The weakly electric fish
might classify a target by comparing its invariants with those of a set of
learned shapes. On the other hand, when measurements are taken at multiple
frequencies, the fish might exploit the shifts and use the spectral content of
the generalized polarization tensors to dramatically improve the stability with
respect to measurement noise of the classification procedure in
electro-sensing. Surprisingly, it turns out that the first-order polarization
tensor at multiple frequencies could be enough for the purpose of
classification. A procedure to eliminate the background field in the case where
the permittivity of the surrounding medium can be neglected, and hence improve
further the stability of the classification process, is also discussed.Comment: 10 pages, 15 figure
Nonhuman gamblers: lessons from rodents, primates, and robots
The search for neuronal and psychological underpinnings of pathological gambling in humans would benefit from investigating related phenomena also outside of our species. In this paper, we present a survey of studies in three widely different populations of agents, namely rodents, non-human primates, and robots. Each of these populations offers valuable and complementary insights on the topic, as the literature demonstrates. In addition, we highlight the deep and complex connections between relevant results across these different areas of research (i.e., cognitive and computational neuroscience, neuroethology, cognitive primatology, neuropsychiatry, evolutionary robotics), to make the case for a greater degree of methodological integration in future studies on pathological gambling
The Structure of Sensorimotor Explanation
The sensorimotor theory of vision and visual consciousness is often described as a radical alternative to the computational and connectionist orthodoxy in the study of visual perception. However, it is far from clear whether the theory represents a significant departure from orthodox approaches or whether it is an enrichment of it. In this study, I tackle this issue by focusing on the explanatory structure of the sensorimotor theory. I argue that the standard formulation of the theory subscribes to the same theses of the dynamical hypothesis and that it affords covering-law explanations. This however exposes the theory to the mere description worry and generates a puzzle about the role of representations. I then argue that the sensorimotor theory is compatible with a mechanistic framework, and show how this can overcome the mere description worry and solve the problem of the explanatory role of representations. By doing so, it will be shown that the theory should be understood as an enrichment of the orthodoxy, rather than an alternative
Deciphering the brain's codes
The two sensory systems discussed use similar algorithms for the synthesis of the neuronal selectivity for the stimulus that releases a particular behavior, although the neural circuits, the brain sites involved, and even the species are different. This stimulus selectivity emerges gradually in a neural network organized according to parallel and hierarchical design principles. The parallel channels contain lower order stations with special circuits for the creation of neuronal selectivities for different features of the stimulus. Convergence of the parallel pathways brings these selectivities together at a higher order station for the eventual synthesis of the selectivity for the whole stimulus pattern. The neurons that are selective for the stimulus are at the top of the hierarchy, and they form the interface between the sensory and motor systems or between sensory systems of different modalities. The similarities of these two systems at the level of algorithms suggest the existence of rules of signal processing that transcend different sensory systems and species of animals
Neural coding tools, based on Information Theory, applied to discrete time series: from electrophysiology to neuroethology
This work is supported by Brazilian agencies Fapesp, CAPES and CNP
Learning, Social Intelligence and the Turing Test - why an "out-of-the-box" Turing Machine will not pass the Turing Test
The Turing Test (TT) checks for human intelligence, rather than any putative
general intelligence. It involves repeated interaction requiring learning in
the form of adaption to the human conversation partner. It is a macro-level
post-hoc test in contrast to the definition of a Turing Machine (TM), which is
a prior micro-level definition. This raises the question of whether learning is
just another computational process, i.e. can be implemented as a TM. Here we
argue that learning or adaption is fundamentally different from computation,
though it does involve processes that can be seen as computations. To
illustrate this difference we compare (a) designing a TM and (b) learning a TM,
defining them for the purpose of the argument. We show that there is a
well-defined sequence of problems which are not effectively designable but are
learnable, in the form of the bounded halting problem. Some characteristics of
human intelligence are reviewed including it's: interactive nature, learning
abilities, imitative tendencies, linguistic ability and context-dependency. A
story that explains some of these is the Social Intelligence Hypothesis. If
this is broadly correct, this points to the necessity of a considerable period
of acculturation (social learning in context) if an artificial intelligence is
to pass the TT. Whilst it is always possible to 'compile' the results of
learning into a TM, this would not be a designed TM and would not be able to
continually adapt (pass future TTs). We conclude three things, namely that: a
purely "designed" TM will never pass the TT; that there is no such thing as a
general intelligence since it necessary involves learning; and that
learning/adaption and computation should be clearly distinguished.Comment: 10 pages, invited talk at Turing Centenary Conference CiE 2012,
special session on "The Turing Test and Thinking Machines
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