Review



recurrent spiking network model  (BioMimetic Therapeutics)

 
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    BioMimetic Therapeutics recurrent spiking network model
    The stimuli, predicted and simulated neural activation patterns. (A) The selected area of an example natural image illustrates the projection of the image to the Gabor filter and ASF calculations of the left visual field. Small dots in the pink square show the cortical density of cells in the visual field. The density of cells in each eccentricity follows the human cortical magnification factor. (B) The contrast energy image from the same natural image as in (A) , built as the output of a series of Gabor filters. These contrast images are the input for estimating the target area summation patters. (C) The area summation target activation pattern, corresponding to the same natural image as in (A) . The color code of the activation pattern borders illustrates the projection of the image from the visual field to the <t>model</t> cortex, with the color code as in (A) . (D) Simulated output pattern for the image in (A) . (E) The normalized strength as a function of the spatial frequency contents for the natural images in Low and Mixed frequencies groups. Error bars represent the standard error of mean. (F) The output of the retina filter to the natural image in (A) , which served as an input to the <t>spiking</t> neural <t>network</t> simulations.
    Recurrent Spiking Network Model, supplied by BioMimetic Therapeutics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/recurrent+spiking+network+model/pmc04717295-158-10-9?v=BioMimetic+Therapeutics
    Average 90 stars, based on 1 article reviews
    recurrent spiking network model - by Bioz Stars, 2026-08
    90/100 stars

    Images

    1) Product Images from "Contextual Modulation is Related to Efficiency in a Spiking Network Model of Visual Cortex"

    Article Title: Contextual Modulation is Related to Efficiency in a Spiking Network Model of Visual Cortex

    Journal: Frontiers in Computational Neuroscience

    doi: 10.3389/fncom.2015.00155

    The stimuli, predicted and simulated neural activation patterns. (A) The selected area of an example natural image illustrates the projection of the image to the Gabor filter and ASF calculations of the left visual field. Small dots in the pink square show the cortical density of cells in the visual field. The density of cells in each eccentricity follows the human cortical magnification factor. (B) The contrast energy image from the same natural image as in (A) , built as the output of a series of Gabor filters. These contrast images are the input for estimating the target area summation patters. (C) The area summation target activation pattern, corresponding to the same natural image as in (A) . The color code of the activation pattern borders illustrates the projection of the image from the visual field to the model cortex, with the color code as in (A) . (D) Simulated output pattern for the image in (A) . (E) The normalized strength as a function of the spatial frequency contents for the natural images in Low and Mixed frequencies groups. Error bars represent the standard error of mean. (F) The output of the retina filter to the natural image in (A) , which served as an input to the spiking neural network simulations.
    Figure Legend Snippet: The stimuli, predicted and simulated neural activation patterns. (A) The selected area of an example natural image illustrates the projection of the image to the Gabor filter and ASF calculations of the left visual field. Small dots in the pink square show the cortical density of cells in the visual field. The density of cells in each eccentricity follows the human cortical magnification factor. (B) The contrast energy image from the same natural image as in (A) , built as the output of a series of Gabor filters. These contrast images are the input for estimating the target area summation patters. (C) The area summation target activation pattern, corresponding to the same natural image as in (A) . The color code of the activation pattern borders illustrates the projection of the image from the visual field to the model cortex, with the color code as in (A) . (D) Simulated output pattern for the image in (A) . (E) The normalized strength as a function of the spatial frequency contents for the natural images in Low and Mixed frequencies groups. Error bars represent the standard error of mean. (F) The output of the retina filter to the natural image in (A) , which served as an input to the spiking neural network simulations.

    Techniques Used: Activation Assay

    Simulation result compared to natural ASF . (A) Examples of the spiking network response associated with high (solid blue curve) and low (solid red curve) efficiency measures to an increasing stimulus patch size, for a representative model neuron located at 14° eccentricity. Response is averaged across 15 neighboring cells. The high-efficiency response is similar to ASF target at the same eccentricity (dashed curve) but with smaller summation field size. (B) Normalized mean distance to ASF (DAS), number of spikes, entropy per spike, and sparseness across all 20 natural images as a function of the number of V1 lateral excitatory-inhibitory and V1-extrastriate feedforward-feedback connections.
    Figure Legend Snippet: Simulation result compared to natural ASF . (A) Examples of the spiking network response associated with high (solid blue curve) and low (solid red curve) efficiency measures to an increasing stimulus patch size, for a representative model neuron located at 14° eccentricity. Response is averaged across 15 neighboring cells. The high-efficiency response is similar to ASF target at the same eccentricity (dashed curve) but with smaller summation field size. (B) Normalized mean distance to ASF (DAS), number of spikes, entropy per spike, and sparseness across all 20 natural images as a function of the number of V1 lateral excitatory-inhibitory and V1-extrastriate feedforward-feedback connections.

    Techniques Used:



    Similar Products

    recurrent spiking network model  (Cortical Dynamics)
    90
    Cortical Dynamics recurrent spiking network model
    Recurrent Spiking Network Model, supplied by Cortical Dynamics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/recurrent+spiking+network+model/pm38733587-202-25-30?v=Cortical+Dynamics
    Average 90 stars, based on 1 article reviews
    recurrent spiking network model - by Bioz Stars, 2026-08
    90/100 stars
    		  Buy from Supplier
    recurrent spiking network model  (BioMimetic Therapeutics)
    90
    BioMimetic Therapeutics recurrent spiking network model
    The stimuli, predicted and simulated neural activation patterns. (A) The selected area of an example natural image illustrates the projection of the image to the Gabor filter and ASF calculations of the left visual field. Small dots in the pink square show the cortical density of cells in the visual field. The density of cells in each eccentricity follows the human cortical magnification factor. (B) The contrast energy image from the same natural image as in (A) , built as the output of a series of Gabor filters. These contrast images are the input for estimating the target area summation patters. (C) The area summation target activation pattern, corresponding to the same natural image as in (A) . The color code of the activation pattern borders illustrates the projection of the image from the visual field to the <t>model</t> cortex, with the color code as in (A) . (D) Simulated output pattern for the image in (A) . (E) The normalized strength as a function of the spatial frequency contents for the natural images in Low and Mixed frequencies groups. Error bars represent the standard error of mean. (F) The output of the retina filter to the natural image in (A) , which served as an input to the <t>spiking</t> neural <t>network</t> simulations.
    Recurrent Spiking Network Model, supplied by BioMimetic Therapeutics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/recurrent+spiking+network+model/pmc04717295-158-10-9?v=BioMimetic+Therapeutics
    Average 90 stars, based on 1 article reviews
    recurrent spiking network model - by Bioz Stars, 2026-08
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    The stimuli, predicted and simulated neural activation patterns. (A) The selected area of an example natural image illustrates the projection of the image to the Gabor filter and ASF calculations of the left visual field. Small dots in the pink square show the cortical density of cells in the visual field. The density of cells in each eccentricity follows the human cortical magnification factor. (B) The contrast energy image from the same natural image as in (A) , built as the output of a series of Gabor filters. These contrast images are the input for estimating the target area summation patters. (C) The area summation target activation pattern, corresponding to the same natural image as in (A) . The color code of the activation pattern borders illustrates the projection of the image from the visual field to the model cortex, with the color code as in (A) . (D) Simulated output pattern for the image in (A) . (E) The normalized strength as a function of the spatial frequency contents for the natural images in Low and Mixed frequencies groups. Error bars represent the standard error of mean. (F) The output of the retina filter to the natural image in (A) , which served as an input to the spiking neural network simulations.

    Journal: Frontiers in Computational Neuroscience

    Article Title: Contextual Modulation is Related to Efficiency in a Spiking Network Model of Visual Cortex

    doi: 10.3389/fncom.2015.00155

    Figure Lengend Snippet: The stimuli, predicted and simulated neural activation patterns. (A) The selected area of an example natural image illustrates the projection of the image to the Gabor filter and ASF calculations of the left visual field. Small dots in the pink square show the cortical density of cells in the visual field. The density of cells in each eccentricity follows the human cortical magnification factor. (B) The contrast energy image from the same natural image as in (A) , built as the output of a series of Gabor filters. These contrast images are the input for estimating the target area summation patters. (C) The area summation target activation pattern, corresponding to the same natural image as in (A) . The color code of the activation pattern borders illustrates the projection of the image from the visual field to the model cortex, with the color code as in (A) . (D) Simulated output pattern for the image in (A) . (E) The normalized strength as a function of the spatial frequency contents for the natural images in Low and Mixed frequencies groups. Error bars represent the standard error of mean. (F) The output of the retina filter to the natural image in (A) , which served as an input to the spiking neural network simulations.

    Article Snippet: We simulated the neural responses of V1 in a biomimetic recurrent spiking network model, to study the relation between the non-linear area summation properties of primate V1 neurons and efficiency of the neural activity at the population level.

    Techniques: Activation Assay

    Simulation result compared to natural ASF . (A) Examples of the spiking network response associated with high (solid blue curve) and low (solid red curve) efficiency measures to an increasing stimulus patch size, for a representative model neuron located at 14° eccentricity. Response is averaged across 15 neighboring cells. The high-efficiency response is similar to ASF target at the same eccentricity (dashed curve) but with smaller summation field size. (B) Normalized mean distance to ASF (DAS), number of spikes, entropy per spike, and sparseness across all 20 natural images as a function of the number of V1 lateral excitatory-inhibitory and V1-extrastriate feedforward-feedback connections.

    Journal: Frontiers in Computational Neuroscience

    Article Title: Contextual Modulation is Related to Efficiency in a Spiking Network Model of Visual Cortex

    doi: 10.3389/fncom.2015.00155

    Figure Lengend Snippet: Simulation result compared to natural ASF . (A) Examples of the spiking network response associated with high (solid blue curve) and low (solid red curve) efficiency measures to an increasing stimulus patch size, for a representative model neuron located at 14° eccentricity. Response is averaged across 15 neighboring cells. The high-efficiency response is similar to ASF target at the same eccentricity (dashed curve) but with smaller summation field size. (B) Normalized mean distance to ASF (DAS), number of spikes, entropy per spike, and sparseness across all 20 natural images as a function of the number of V1 lateral excitatory-inhibitory and V1-extrastriate feedforward-feedback connections.

    Article Snippet: We simulated the neural responses of V1 in a biomimetic recurrent spiking network model, to study the relation between the non-linear area summation properties of primate V1 neurons and efficiency of the neural activity at the population level.

    Techniques: