Last edited by Zulkikus
Thursday, July 30, 2020 | History

2 edition of Neuronal variability: population responses in normal cat. found in the catalog.

Neuronal variability: population responses in normal cat.

George John Tomko

Neuronal variability: population responses in normal cat.

by George John Tomko

  • 40 Want to read
  • 31 Currently reading

Published in Toronto .
Written in English

    Subjects:
  • Cats,
  • Neutrons,
  • Optic nerve

  • Edition Notes

    ContributionsToronto, University.
    The Physical Object
    Paginationvi, 71 leaves.
    Number of Pages71
    ID Numbers
    Open LibraryOL21624726M

      Neurons in the brain communicate via electric pulses or action potentials of a fraction of a volt, that last about a thousandth of a second. In , Alan Hodgkin and Andrew Huxley received a Cited by: Neural coding is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the individual or ensemble neuronal responses and the relationship among the electrical activity of the neurons in the ensemble. Based on the theory that sensory and other information is represented in the brain by networks of neurons, it is thought that neurons can encode.

      The necessity of auditory cortex for normal sound localization (which is Classification assigned each neural population response in the “test” set to the stimulus azimuth whose mean population response in an independently selected set of “training” trials was most similar. A neural code for auditory space in the cat's superior. These different sources of heterogeneity differentially influence the characteristics of population responses: from introducing variability in how different neurons and neuronal classes respond to and integrate their synaptic inputs, to variations in the magnitude and distribution of Cited by: 2.

      For example, orientation maps constructed from ongoing neuronal activity in the anesthetized cat match those based on visual responses (Tsodyks et al., ; Kenet et al., ). Spatiotemporal correlations of spikes in the visual cortex are similar when the awake animal is simply sitting in darkness or observing natural scenes (Fiser et al Cited by: In the present study, we instead quantified azimuthal information for individual neurons using mutual information (Pecka et al. ) and for the neural population using the performance of neural decoders. Both methods take trial-to-trial variability in firing rate (neural noise) into account, in addition to mean firing rate, thereby providing.


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Neuronal variability: population responses in normal cat by George John Tomko Download PDF EPUB FB2

Development of Neuronal Responses in the Visual System of Cats. No clear statement about such "superacuity" can yet be made about specially reared animals because of the large variation of the "normal" value from one individual to another, although there are some suggestive pieces of evidence.

the cat's eye NEURONAL RESPONSES IN THE Cited by: The sum of the responses to the low and high frequency part of the meow, with the boundary at kHz, was larger than the neuronal response to the natural meow itself, suggesting that strong lateral inhibition is shaping the response to the natural meow.

In this sense, the neurons are by: Bayesian analysis has been applied to neurophysiological data collected from cat primary visual cortex (area 17 or V1). Fig. 1 shows the Bayesian interpretation of responses of anaesthetized cat V1 cells to drifting sine-wave density plots of estimated conditional probabilities for one cell are shown in Fig.

For each stimulus orientation s, a normal distribution (the Cited by:   Extracting information from these population codes is difficult because of the noise inherent in neuronal responses. In most cases of interest, maximum likelihood (ML) Cited by: Fluctuations within areas of the visual network have been shown to account for the trial-bytrial variability in behavioral or neuronal responses to identical stimuli using in vivo recordings in.

Neural and vascular variability and the fMRI-BOLD response in normal aging Article in Magnetic Resonance Imaging 28(4) May with 76 Reads How we measure 'reads'. Introduction. Neural responses in sensory cortices are notoriously variable: the same stimulus can evoke a different response on each presentation (Henry et al.,Tomko and Crapper, ).While there have been great advances in characterizing the detailed patterns and statistical structure of cortical variability (Ecker et al.,Goris et al.,Kohn and Smith,Lin et al Cited by: We performed chronic GCaMP6m imaging in V1 L2/3 of awake mice to study the variability in responses of neuronal populations to drifting gratings (n = 9 mice recorded long term, over 2–5 weeks) and natural movies (n = 4 mice recorded long term; n = 5 mice recorded short term, on a single day; Figure 1A).

We found that the orientation tuning and responses to natural movies of many neurons Cited by: Fig. Variability in a deterministic model network of excitatory and inhibitory neurons, both modeled as leaky integrate-and-fire neurons. e trace as a function of time for a single model neuron.

Spikes (vertical lines) are generated whenever the. Attention is commonly thought to improve behavioral performance by increasing response gain and suppressing shared variability in neuronal populations.

However, both the focus and the strength of attention are likely to vary from one experimental trial to the next, thereby inducing response variability unknown to the experimenter. Here we study analytically how fluctuations in Cited by: from book Computation and Neural The ‘Ideal Homunculus’: Statistical Inference from Neural Population Responses We argue that this is a critical lesson for those interested in neuronal.

a, Population distributions of the direction index of calcium (green, n = 19 windows in 8 cats), glutamate (blue, n = 37 windows in 5 cats) and vessel dilation (red, n = 79 vessels in 18 cats Cited by:   Here we analyze a simple neural population model, where neurons with overlapping receptive fields encode the direction of motion of a stimulus (see Fig.

1 A).We assume that neurons produce spikes independently according to a Poisson process with rate λ i and treat attention as a process that modulates the neurons' gain (see Fig.

1 B).The firing rate of neuron i is given byCited by:   State-dependent variability of neuronal responses to transcranial magnetic stimulation of the visual cortex Brian N. Pasley, * Elena A. Allen, * and Ralph D.

Freeman Helen Wills Neuroscience Institute and the School of Optometry, University of California, Berkeley, Berkeley, CAUSA. In this paper, we show that scaled variability in neuronal response is tuned to stimulus features, as previously observed for drifting grating stimuli in V1 and MT (Ponce-Alvarez et al., One potential mechanism underlying this increased stimulus discriminability is the decreased response variability both at the level of subthreshold membrane potentials [13, 14•] and noise correlations in neuronal populations [11, 16, 22•] during periods of locomotion and high by: Divya Premachandran, Sampath Madhyastha, Vasudha Saralaya, Jai Adithya, Teresa Joy, Sudhanshu Sekar Sahu.

Effect of Prenatal Isotretinoin Exposure on Neuronal Population of Frontal Cortex in Rats. Research Journal of Pharmaceutical, Biological & Chemical Sciences. ; 4(1): Neuron Review Multisensory Interplay Reveals Crossmodal Influences on ‘Sensory-Specific’ Brain Regions, Neural Responses, and Judgments Jon Driver 1,* and Toemme Noesselt 2 * 1UCL Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London WC1N 3AR, UK 2Department of Neurology II, Otto-von-Guericke-Universita¨t, Leipziger Str.

44, Magdeburg. Periodicity coding in the inferior colliculus of the cat. Neuronal mechanisms Article (PDF Available) in Journal of Neurophysiology 60(6) January with Reads. The computation of likelihood is often based on assumptions about the structure of the neuronal response distributions, in particular their variability.

Our neuronal responses could be considered as belonging to the exponential family with linear sufficient statistics (Supplementary Fig. 1), and therefore the logarithm of the likelihood function is a linear function of the neuronal respon Cited by:. But neuronal responses generally exhibit super-Poisson variability 5,7–9,27–28, and the model presented here provides a simple but effective means of explaining this behavior.

Some authors have reported sub-Poisson variability, especially in brief time bins immediately after a sudden stimulus onset 29 – Cited by: EEG, neural population models of; fMRI BOLD, neural population models of; Functional magnetic resonance imaging (fMRI), neural population models of; Multimodal neural population.

The ability to process relevant stimuli selectively is a fundamental function of the primate visual system. The best-understood correlate of this function is the enhanced response of neurons in Cited by: