2011
DOI: 10.1109/tamd.2011.2138705
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Firing Rate Homeostasis for Dynamic Neural Field Formation

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Cited by 8 publications
(4 citation statements)
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“…In comparison to other topological algorithms [67][71], the synaptic weights of each neuron inform about the vicinity to other neurons based on their rank order: that is, neurons with similar rank codes are spatially near. First, we study how the sensory inputs shape the sensory mapping and how multimodal integration occurs between the two maps within an intermediate layer that learns information from both.…”
Section: Introductionmentioning
confidence: 99%
“…In comparison to other topological algorithms [67][71], the synaptic weights of each neuron inform about the vicinity to other neurons based on their rank order: that is, neurons with similar rank codes are spatially near. First, we study how the sensory inputs shape the sensory mapping and how multimodal integration occurs between the two maps within an intermediate layer that learns information from both.…”
Section: Introductionmentioning
confidence: 99%
“…We tested two models against our behavioral data: one biologically inspired model (in line with with the BDNF principles discussed by Glaser and Joublin, 2011) capturing cellular homeostatic principles, and the same model but with the cellular homeostatic term removed. Each was simulated in Matlab using the exact order and content of trials and color values seen by each of the 30 participants in turn.…”
Section: Modelmentioning
confidence: 99%
“…The cellular mechanism would need to toggle the adaptation of a neural unit between "labile" and "stable" dispositions toward changing connection strengths (Bienenstock, Cooper, & Munro, 1982). One suggested candidate for this process is brain-derived neurotrophic factor (BDNF) (Glaser & Joublin, 2011). Using Calcium levels as a proxy for the instantaneous levels of change at a synapse, neural units coding for changes in the level of BDNF can dynamically alter the underlying synaptic excitation/inhibition levels of cells.…”
Section: Introductionmentioning
confidence: 99%
“…It is a common method for researchers to analyze and study brain function qualitatively and quantitatively by using mathematical statistical model to model neuron impulse signals of organisms. Characterizing the characteristics of neuronal impulse signals through the mathematical characteristics of the model and verifying the reasonable assumptions made by these characteristics can help researchers deepen their cognition of the functional connections between different types of neurons in the brain and reveal the functional connections among neurons [2,3,4].Exploring the hidden information inside the neuronal pulse signals [5,6,7,8] can also build the corresponding neural prosthesis according to the pulse signal issuing characteristics of specific brain regions [9], which can be used to make up for the functional loss or abnormality of specific brain regions caused by brain damage and other reasons. With the continuous development of microelectrode array technology and the continuous improvement in the spatial and temporal accuracy of the original data of neuron impulse signals collected by related instruments, it has become very common to use data to drive the construction of statistical models, so as to assist researchers to complete the exploration of functional connections between neurons in different brain regions.…”
Section: Introductionmentioning
confidence: 99%