5,030 research outputs found
Network Plasticity as Bayesian Inference
General results from statistical learning theory suggest to understand not
only brain computations, but also brain plasticity as probabilistic inference.
But a model for that has been missing. We propose that inherently stochastic
features of synaptic plasticity and spine motility enable cortical networks of
neurons to carry out probabilistic inference by sampling from a posterior
distribution of network configurations. This model provides a viable
alternative to existing models that propose convergence of parameters to
maximum likelihood values. It explains how priors on weight distributions and
connection probabilities can be merged optimally with learned experience, how
cortical networks can generalize learned information so well to novel
experiences, and how they can compensate continuously for unforeseen
disturbances of the network. The resulting new theory of network plasticity
explains from a functional perspective a number of experimental data on
stochastic aspects of synaptic plasticity that previously appeared to be quite
puzzling.Comment: 33 pages, 5 figures, the supplement is available on the author's web
page http://www.igi.tugraz.at/kappe
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
Information Theoryâs failure in neuroscience: on the limitations of cybernetics
In Cybernetics (1961 Edition), Professor Norbert Wiener noted that âThe role of information and the technique of measuring and transmitting information constitute a whole discipline for the engineer, for the neuroscientist, for the psychologist, and for the sociologistâ. Sociology aside, the neuroscientists and the psychologists inferred âinformation transmittedâ using the discrete summations from Shannon Information Theory. The present author has since scrutinized the psychologistsâ approach in depth, and found it wrong. The neuroscientistsâ approach is highly related, but remains unexamined. Neuroscientists quantified âthe ability of [physiological sensory] receptors (or other signal-processing elements) to transmit information about stimulus parametersâ. Such parameters could vary along a single continuum (e.g., intensity), or along multiple dimensions that altogether provide a Gestalt â such as a face. Here, unprecedented scrutiny is given to how 23 neuroscience papers computed âinformation transmittedâ in terms of stimulus parameters and the evoked neuronal spikes. The computations relied upon Shannonâs âconfusion matrixâ, which quantifies the fidelity of a âgeneral communication systemâ. Shannonâs matrix is square, with the same labels for columns and for rows. Nonetheless, neuroscientists labelled the columns by âstimulus categoryâ and the rows by âspike-count categoryâ. The resulting âinformation transmittedâ is spurious, unless the evoked spike-counts are worked backwards to infer the hypothetical evoking stimuli. The latter task is probabilistic and, regardless, requires that the confusion matrix be square. Was it? For these 23 significant papers, the answer is No
A point process framework for modeling electrical stimulation of the auditory nerve
Model-based studies of auditory nerve responses to electrical stimulation can
provide insight into the functioning of cochlear implants. Ideally, these
studies can identify limitations in sound processing strategies and lead to
improved methods for providing sound information to cochlear implant users. To
accomplish this, models must accurately describe auditory nerve spiking while
avoiding excessive complexity that would preclude large-scale simulations of
populations of auditory nerve fibers and obscure insight into the mechanisms
that influence neural encoding of sound information. In this spirit, we develop
a point process model of the auditory nerve that provides a compact and
accurate description of neural responses to electric stimulation. Inspired by
the framework of generalized linear models, the proposed model consists of a
cascade of linear and nonlinear stages. We show how each of these stages can be
associated with biophysical mechanisms and related to models of neuronal
dynamics. Moreover, we derive a semi-analytical procedure that uniquely
determines each parameter in the model on the basis of fundamental statistics
from recordings of single fiber responses to electric stimulation, including
threshold, relative spread, jitter, and chronaxie. The model also accounts for
refractory and summation effects that influence the responses of auditory nerve
fibers to high pulse rate stimulation. Throughout, we compare model predictions
to published physiological data and explain differences in auditory nerve
responses to high and low pulse rate stimulation. We close by performing an
ideal observer analysis of simulated spike trains in response to sinusoidally
amplitude modulated stimuli and find that carrier pulse rate does not affect
modulation detection thresholds.Comment: 1 title page, 27 manuscript pages, 14 figures, 1 table, 1 appendi
Stimulus-invariant processing and spectrotemporal reverse correlation in primary auditory cortex
The spectrotemporal receptive field (STRF) provides a versatile and
integrated, spectral and temporal, functional characterization of single cells
in primary auditory cortex (AI). In this paper, we explore the origin of, and
relationship between, different ways of measuring and analyzing an STRF. We
demonstrate that STRFs measured using a spectrotemporally diverse array of
broadband stimuli -- such as dynamic ripples, spectrotemporally white noise,
and temporally orthogonal ripple combinations (TORCs) -- are very similar,
confirming earlier findings that the STRF is a robust linear descriptor of the
cell. We also present a new deterministic analysis framework that employs the
Fourier series to describe the spectrotemporal modulations contained in the
stimuli and responses. Additional insights into the STRF measurements,
including the nature and interpretation of measurement errors, is presented
using the Fourier transform, coupled to singular-value decomposition (SVD), and
variability analyses including bootstrap. The results promote the utility of
the STRF as a core functional descriptor of neurons in AI.Comment: 42 pages, 8 Figures; to appear in Journal of Computational
Neuroscienc
A hypothesis on improving foreign accents by optimizing variability in vocal learning brain circuits
Rapid vocal motor learning is observed when acquiring a language in early childhood, or learning to speak another language later in life. Accurate pronunciation is one of the hardest things for late learners to master and they are almost always left with a non-native accent. Here I propose a novel hypothesis that this accent could be improved by optimizing variability in vocal learning brain circuits during learning. Much of the neurobiology of human vocal motor learning has been inferred from studies on songbirds. Jarvis (2004) proposed the hypothesis that as in songbirds there are two pathways in humans: one for learning speech (the striatal vocal learning pathway), and one for production of previously learnt speech (the motor pathway). Learning new motor sequences necessary for accurate non-native pronunciation is challenging and I argue that in late learners of a foreign language the vocal learning pathway becomes inactive prematurely. The motor pathway is engaged once again and learners maintain their original native motor patterns for producing speech, resulting in speaking with a foreign accent. Further, I argue that variability in neural activity within vocal motor circuitry generates vocal variability that supports accurate non-native pronunciation. Recent theoretical and experimental work on motor learning suggests that variability in the motor movement is necessary for the development of expertise. I propose that there is little trial-by-trial variability when using the motor pathway. When using the vocal learning pathway variability gradually increases, reflecting an exploratory phase in which learners try out different ways of pronouncing words, before decreasing and stabilizing once the âbestâ performance has been identified. The hypothesis proposed here could be tested using behavioral interventions that optimize variability and engage the vocal learning pathway for longer, with the prediction that this would allow learners to develop new motor patterns that result in more native-like pronunciation
Electrical vestibular stimulation in humans. A narrative review
Background: In patients with bilateral vestibulopathy, the
regular treatment options, such as medication, surgery, and/
or vestibular rehabilitation, do not always suffice. Therefore,
the focus in this field of vestibular research shifted to electri-
cal vestibular stimulation (EVS) and the development of a
system capable of artificially restoring the vestibular func-
tion. Key Message: Currently, three approaches are being
investigated: vestibular co-stimulation with a cochlear im-
plant (CI), EVS with a vestibular implant (VI), and galvanic
vestibular stimulation (GVS). All three applications show
promising results but due to conceptual differences and the
experimental state, a consensus on which application is the
most ideal for which type of patient is still missing. Summa-
ry: Vestibular co-stimulation with a CI is based on âspread of
excitation,â which is a phenomenon that occurs when the
currents from the CI spread to the surrounding structures
and stimulate them. It has been shown that CI activation can
indeed result in stimulation of the vestibular structures.
Therefore, the question was raised whether vestibular co-
stimulation can be functionally used in patients with bilat-
eral vestibulopathy. A more direct vestibular stimulation
method can be accomplished by implantation and activa-
tion of a VI. The concept of the VI is based on the technology
and principles of the CI. Different VI prototypes are currently
being evaluated regarding feasibility and functionality. So
far, all of them were capable of activating different types of
vestibular reflexes. A third stimulation method is GVS, which
requires the use of surface electrodes instead of an implant-
ed electrode array. However, as the currents are sent through
the skull from one mastoid to the other, GVS is rather unspe-
cific. It should be mentioned though, that the reported
spread of excitation in both CI and VI use also seems to in-
duce a more unspecific stimulation. Although all three ap-
plications of EVS were shown to be effective, it has yet to be
defined which option is more desirable based on applicabil-
ity and efficiency. It is possible and even likely that there is a
place for all three approaches, given the diversity of the pa-
tient population who serves to gain from such technologies
I, NEURON: the neuron as the collective
Purpose â In the last half-century, individual sensory neurons have been bestowed with characteristics of the whole human being, such as behavior and its oft-presumed precursor, consciousness. This anthropomorphization is pervasive in the literature. It is also absurd, given what we know about neurons, and it needs to be abolished. This study aims to first understand how it happened, and hence why it persists.
Design/methodology/approach â The peer-reviewed sensory-neurophysiology literature extends to hundreds (perhaps thousands) of papers. Here, more than 90 mainstream papers were scrutinized.
Findings â Anthropomorphization arose because single neurons were cast as âobserversâ who âidentifyâ, âcategorizeâ, ârecognizeâ, âdistinguishâ or âdiscriminateâ the stimuli, using math-based algorithms that reduce (âdecodeâ) the stimulus-evoked spike trains to the particular stimuli inferred to elicit them. Without âdecodingâ, there is supposedly no perception. However, âdecodingâ is both unnecessary and unconfirmed. The neuronal âobserverâ in fact consists of the laboratory staff and the greater society that supports them. In anthropomorphization, the neuron becomes the collective.
Research limitations/implications â Anthropomorphization underlies the widespread application to neurons Information Theory and Signal Detection Theory, making both approaches incorrect.
Practical implications â A great deal of time, money and effort has been wasted on anthropomorphic Reductionist approaches to understanding perception and consciousness. Those resources should be diverted into more-fruitful approaches.
Originality/value â A long-overdue scrutiny of sensory-neuroscience literature reveals that anthropomorphization, a form of Reductionism that involves the presumption of single-neuron consciousness, has run amok in neuroscience. Consciousness is more likely to be an emergent property of the brain
Synchronization and Redundancy: Implications for Robustness of Neural Learning and Decision Making
Learning and decision making in the brain are key processes critical to
survival, and yet are processes implemented by non-ideal biological building
blocks which can impose significant error. We explore quantitatively how the
brain might cope with this inherent source of error by taking advantage of two
ubiquitous mechanisms, redundancy and synchronization. In particular we
consider a neural process whose goal is to learn a decision function by
implementing a nonlinear gradient dynamics. The dynamics, however, are assumed
to be corrupted by perturbations modeling the error which might be incurred due
to limitations of the biology, intrinsic neuronal noise, and imperfect
measurements. We show that error, and the associated uncertainty surrounding a
learned solution, can be controlled in large part by trading off
synchronization strength among multiple redundant neural systems against the
noise amplitude. The impact of the coupling between such redundant systems is
quantified by the spectrum of the network Laplacian, and we discuss the role of
network topology in synchronization and in reducing the effect of noise. A
range of situations in which the mechanisms we model arise in brain science are
discussed, and we draw attention to experimental evidence suggesting that
cortical circuits capable of implementing the computations of interest here can
be found on several scales. Finally, simulations comparing theoretical bounds
to the relevant empirical quantities show that the theoretical estimates we
derive can be tight.Comment: Preprint, accepted for publication in Neural Computatio
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