Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • 1980-1984  (2)
Collection
Publisher
Years
Year
  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Biological cybernetics 50 (1984), S. 157-165 
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract Interaction mechanisms between excitatory and inhibitory impulse sequences operating on neurons play an important role for the processing of information by the nervous system. For instance, the convergence of excitatory and inhibitory influences on retinal ganglion cells to form their receptive fields has been taken as an example for the process of neuronal sharpening by lateral inhibition. In order to analyze quantitatively the functional behavior of such a system, Shannon's entropy method for multiple access channels has been applied to biological two-inputs-one-output systems using the theoretical model developed by Tsukada et al. (1979). Here we give an extension of this procedure from the point of view to reduce redundancy of information in the input signal space of single neurons and attempt to obtain a new interpretation for the information processing of the system. The concept for the redundancy reducing mechanism in single neurons is examined and discussed for the following two processes. The first process is concerned with a signal space formed by superposing two random sequences on the input of a neuron. In this process, we introduce a coding technique to encode the inhibitory sequence by using the timing of the excitatory sequence, which is closely related to an encoding technique of multiple access channels with a correlated source (Marko, 1966, 1970, 1973; Slepian and Wolf, 1973) and which is an invariant transformation in the input signal space without changing the information contents of the input. The second process is concerned with a procedure of reducing redundant signals in the signal space mentioned before. In this connection, it is an important point to see how single neurons reduce the dimensionality of the signal space via transformation with a minimum loss of effective information. For this purpose we introduce the criterion that average transmission of information from signal space to the output does not change when redundant signals are added. This assumption is based on the fact that two signals are equivalent if and only if they have identical input-output behavior. The mechanism is examined and estimated by using a computer-simulated model. As the result of such a simulation we can estimate the minimal segmentation in the signal space which is necessary and sufficient for temporal pattern sensitivity in neurons.
    Type of Medium: Electronic Resource
    Signatur Availability
    BibTip Others were also interested in ...
  • 2
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract In order to characterize temporal pattern sensitivity in the cat ganglion cells, a new analysis technique by semi-Markov models which was developed in the previous papers (Tsukada et al., 1975–1977) was applied to input-output relations of the receptive-field. Three types of statistical spot stimuli positioned in the center region of receptive fields were used. Each type of stimulus has an identical histogram in the inter-stimulus intervals and therefore the same mean and variance, but different correlations between adjacent inter-stimulus intervals (Type 1, positive; Type 2, negative; and Type 3, independent processes). From the output spike trains of cat retinal ganglion cells to each stimulus, mean, variance, and histogram were computed. As the result of investigating these data, we could draw the following conclusion from the resultant output interval histograms. The receptive-field-center responses of cat ganglion cells can be classified into two groups (Types L and N) according to the difference of responsiveness to the three types of statistical spot stimuli. A Type L response has the same histogram in interspike intervals for all three stimuli, and is not sensitive to the temporal pattern, while a Type N response has three different forms depending on each type of stimulus showing high sensitivity to the temporal pattern. These results were also simulated by the Markov chain model and discussed with relation to neural coding and classification of ganglion cell types.
    Type of Medium: Electronic Resource
    Signatur Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...