2,317 research outputs found
Complexity of multi-dimensional spontaneous EEG decreases during propofol induced general anaesthesia
Emerging neural theories of consciousness suggest a correlation between a specific type of neural dynamical complexity and the level of consciousness: When awake and aware, causal interactions between brain regions are both integrated (all regions are to a certain extent connected) and differentiated (there is inhomogeneity and variety in the interactions). In support of this, recent work by Casali et al (2013) has shown that Lempel-Ziv complexity correlates strongly with conscious level, when computed on the EEG response to transcranial magnetic stimulation. Here we investigated complexity of spontaneous high-density EEG data during propofol-induced general anaesthesia. We consider three distinct measures: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability in the constitution of the set of active channels; and (iii) the novel synchrony coalition entropy (SCE), which measures the variability in the constitution of the set of synchronous channels. After some simulations on Kuramoto oscillator models which demonstrate that these measures capture distinct ‘flavours’ of complexity, we show that there is a robustly measurable decrease in the complexity of spontaneous EEG during general anaesthesia
Brain enhancement through cognitive training: A new insight from brain connectome
Owing to the recent advances in neurotechnology and the progress in understanding of brain cognitive functions, improvements of cognitive performance or acceleration of learning process with brain enhancement systems is not out of our reach anymore, on the contrary, it is a tangible target of contemporary research. Although a variety of approaches have been proposed, we will mainly focus on cognitive training interventions, in which learners repeatedly perform cognitive tasks to improve their cognitive abilities. In this review article, we propose that the learning process during the cognitive training can be facilitated by an assistive system monitoring cognitive workloads using electroencephalography (EEG) biomarkers, and the brain connectome approach can provide additional valuable biomarkers for facilitating leaners' learning processes. For the purpose, we will introduce studies on the cognitive training interventions, EEG biomarkers for cognitive workload, and human brain connectome. As cognitive overload and mental fatigue would reduce or even eliminate gains of cognitive training interventions, a real-time monitoring of cognitive workload can facilitate the learning process by flexibly adjusting difficulty levels of the training task. Moreover, cognitive training interventions should have effects on brain sub-networks, not on a single brain region, and graph theoretical network metrics quantifying topological architecture of the brain network can differentiate with respect to individual cognitive states as well as to different individuals' cognitive abilities, suggesting that the connectome is a valuable approach for tracking the learning progress. Although only a few studies have exploited the connectome approach for studying alterations of the brain network induced by cognitive training interventions so far, we believe that it would be a useful technique for capturing improvements of cognitive function
Getting aligned on representational alignment
Biological and artificial information processing systems form representations
that they can use to categorize, reason, plan, navigate, and make decisions.
How can we measure the extent to which the representations formed by these
diverse systems agree? Do similarities in representations then translate into
similar behavior? How can a system's representations be modified to better
match those of another system? These questions pertaining to the study of
representational alignment are at the heart of some of the most active research
areas in cognitive science, neuroscience, and machine learning. For example,
cognitive scientists measure the representational alignment of multiple
individuals to identify shared cognitive priors, neuroscientists align fMRI
responses from multiple individuals into a shared representational space for
group-level analyses, and ML researchers distill knowledge from teacher models
into student models by increasing their alignment. Unfortunately, there is
limited knowledge transfer between research communities interested in
representational alignment, so progress in one field often ends up being
rediscovered independently in another. Thus, greater cross-field communication
would be advantageous. To improve communication between these fields, we
propose a unifying framework that can serve as a common language between
researchers studying representational alignment. We survey the literature from
all three fields and demonstrate how prior work fits into this framework.
Finally, we lay out open problems in representational alignment where progress
can benefit all three of these fields. We hope that our work can catalyze
cross-disciplinary collaboration and accelerate progress for all communities
studying and developing information processing systems. We note that this is a
working paper and encourage readers to reach out with their suggestions for
future revisions.Comment: Working paper, changes to be made in upcoming revision
Interpreting Deep Visual Representations via Network Dissection
The success of recent deep convolutional neural networks (CNNs) depends on
learning hidden representations that can summarize the important factors of
variation behind the data. However, CNNs often criticized as being black boxes
that lack interpretability, since they have millions of unexplained model
parameters. In this work, we describe Network Dissection, a method that
interprets networks by providing labels for the units of their deep visual
representations. The proposed method quantifies the interpretability of CNN
representations by evaluating the alignment between individual hidden units and
a set of visual semantic concepts. By identifying the best alignments, units
are given human interpretable labels across a range of objects, parts, scenes,
textures, materials, and colors. The method reveals that deep representations
are more transparent and interpretable than expected: we find that
representations are significantly more interpretable than they would be under a
random equivalently powerful basis. We apply the method to interpret and
compare the latent representations of various network architectures trained to
solve different supervised and self-supervised training tasks. We then examine
factors affecting the network interpretability such as the number of the
training iterations, regularizations, different initializations, and the
network depth and width. Finally we show that the interpreted units can be used
to provide explicit explanations of a prediction given by a CNN for an image.
Our results highlight that interpretability is an important property of deep
neural networks that provides new insights into their hierarchical structure.Comment: *B. Zhou and D. Bau contributed equally to this work. 15 pages, 27
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Thinking through the implications of neural reuse for the additive factors method
One method for uncovering the subprocesses of mental processes is the “Additive Factors Method” (AFM). The AFM uses reaction time data from factorial experiments to infer the presence of separate processing stages. This paper investigates the conceptual status of the AFM. It argues that one of the AFM’s underlying assumptions is problematic in light of recent developments in cognitive neuroscience. Discussion begins by laying out the basic logic of the AFM, followed by an analysis of the challenge presented by neural reuse. Following this, implications are analysed and avenues of response considered
Neural reuse: A fundamental organizational principle of the brain
An emerging class of theories concerning the functional structure of the brain takes the reuse of neural circuitry for various cognitive purposes to be a central organizational principle. According to these theories, it is quite common for neural circuits established for one purpose to be exapted (exploited, recycled, redeployed) during evolution or normal development, and be put to different uses, often without losing their original functions. Neural reuse theories thus differ from the usual understanding of the role of neural plasticity (which is, after all, a kind of reuse) in brain organization along the following lines: According to neural reuse, circuits can continue to acquire new uses after an initial or original function is established; the acquisition of new uses need not involve unusual circumstances such as injury or loss of established function; and the acquisition of a new use need not involve (much) local change to circuit structure (e.g., it might involve only the establishment of functional connections to new neural partners). Thus, neural reuse theories offer a distinct perspective on several topics of general interest, such as: the evolution and development of the brain, including (for instance) the evolutionary-developmental pathway supporting primate tool use and human language; the degree of modularity in brain organization; the degree of localization of cognitive function; and the cortical parcellation problem and the prospects (and proper methods to employ) for function to structure mapping. The idea also has some practical implications in the areas of rehabilitative medicine and machine interface design
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