Topic
Binding neuron
About: Binding neuron is a research topic. Over the lifetime, 20 publications have been published within this topic receiving 156 citations.
Papers
TL;DR: In this article, the influence of geometric size of the network on its ability to condense information was investigated in a network model consisting of five fully connected neurons, and the Shannon's formula was used to calculate the amount of information in any input spike train and in any periodic state.
Abstract: Information about external world is delivered to the brain in the form of structured in time spike trains. During further processing in higher areas, information is subjected to a certain condensation process, which results in formation of abstract conceptual images of external world, apparently, represented as certain uniform spiking activity partially independent on the input spike trains details. Possible physical mechanism of condensation at the level of individual neuron was discussed recently. In a reverberating spiking neural network, due to this mechanism the dynamics should settle down to the same uniform/ periodic activity in response to a set of various inputs. Since the same periodic activity may correspond to different input spike trains, we interpret this as possible candidate for information condensation mechanism in a network. Our purpose is to test this possibility in a network model consisting of five fully connected neurons, particularly, the influence of geometric size of the network, on its ability to condense information. Dynamics of 20 spiking neural networks of different geometric sizes are modelled by means of computer simulation. Each network was propelled into reverberating dynamics by applying various initial input spike trains. We run the dynamics until it becomes periodic. The Shannon's formula is used to calculate the amount of information in any input spike train and in any periodic state found. As a result, we obtain explicit estimate of the degree of information condensation in the networks, and conclude that it depends strongly on the net's geometric size.
26 citations
TL;DR: It is expected that every output spike of single neuron is immediately fed back into its input and concluded that the feedback presence can radically alter spiking statistics.
Abstract: The binding neuron model [A.K. Vidybida,BioSystems 48, 263 (1998)] is inspired by numerical simulation of Hodgkin-Huxley-type point neuron [A.K. Vidybida,Biol. Cybern. 74, 539 (1996)], as well as by the leaky integrate-and-fire (LIF) model [J.P. Segundo, D. Perkel, H. Wyman, H. Hegstad, G.P. Moore,Kybernetic 4, 157 (1968)]. In the binding neuron, the trace of an input is remembered for a fixed period of time after which it disappears completely. This is in the contrast with the above two models, where the postsynaptic potentials decay exponentially and can be forgotten only after triggering. The finiteness of memory in the binding neuron allows one to construct fast recurrent networks for computer modeling [A.K. Vidybida,BioSystems 71, 205 (2003)]. Recently, [A.K. Vidybida,BioSystems 89, 160 (2007)], the finiteness is utilized for exact mathematical description of the output stochastic process if the binding neuron is driven with the Poisson input stream. In this paper, it is expected that every output spike of single neuron is immediately fed back into its input. For this construction, externally fed with Poisson stream, the output stream is characterized in terms of interspike interval (ISI) probability density distribution if the neuron has threshold 2. For higher thresholds, the distribution is calculated numerically. The distributions are compared with those found for binding neuron without feedback, and for leaky integrator. It is concluded that the feedback presence can radically alter spiking statistics.
20 citations
TL;DR: The finiteness of memory in the binding neuron allows one to construct fast recurrent networks for computer modelling and is utilized for exact mathematical description of the output stochastic process, if the binding neurons is driven with the poissonian input stream.
Abstract: The binding neuron model is inspired by numerical simulation of Hodgkin–Huxley-type point neuron [Vidybida, A.K., 1996. Neuron as time coherence discriminator. Biol. Cybern. 74, 539–544], as well as by the leaky integrate-and-fire model [Segundo, J.P., Perkel, D., Wyman, H., Hegstad, H., Moore, G.P., 1968. Input–output relations in computer-simulated nerve cell. Kybernetic 4, 157–171]. In the binding neuron, the trace of an input is rememberd for a fixed period of time after which it disappears completely. This is in contrast with the above two models, where the postsynaptic potentials decay exponentially and can be forgotten only after triggering. As usual, the binding neuron fires when the number of input impulses stored in it attains a definite threshold. The finiteness of memory in the binding neuron allows one to construct fast recurrent networks for computer modelling [Vidybida, A.K., 2003. Computer simulation of inhibition-dependent binding in a neural networl. BioSystems 71, 205–212]. In this paper, the finiteness is utilized for exact mathematical description of the output stochastic process, if the binding neuron is driven with the poissonian input stream. For threshold 2 the output non-poissonian stream is characterized in terms of probability density distribution of interspike intervals, for threshold 3 the transmission function is obtained.
14 citations
TL;DR: It is concluded that introduction of delayed inhibitory feedback can radically change neuronal output firing statistics and is as well distinct from what was found previously by a similar method for excitatory neuron with delayed feedback.
Abstract: Activity of inhibitory neuron with delayed feedback is considered in the framework of point stochastic processes. The neuron receives excitatory input impulses from a Poisson stream, and inhibitory impulses from the feedback line with a delay. We investigate here, how does the presence of inhibitory feedback affect the output firing statistics. Using binding neuron (BN) as a model, we derive analytically the exact expressions for the output interspike intervals (ISI) probability density, mean output ISI and coefficient of variation as functions of model's parameters for the case of threshold 2. Using the leaky integrate-and-fire (LIF) model, as well as the BN model with higher thresholds, these statistical quantities are found numerically. In contrast to the previously studied situation of no feedback, the ISI probability densities found here both for BN and LIF neuron become bimodal and have discontinuity of jump type. Nevertheless, the presence of inhibitory delayed feedback was not found to affect substantially the output ISI coefficient of variation. The ISI coefficient of variation found ranges between 0.5 and 1. It is concluded that introduction of delayed inhibitory feedback can radically change neuronal output firing statistics. This statistics is as well distinct from what was found previously (Vidybida and Kravchuk, 2009) by a similar method for excitatory neuron with delayed feedback.
13 citations
TL;DR: In this paper, a binding neuron with delayed feedback was considered and the ISI distribution of the output stream of the neuron was analyzed. But the authors focused on the effect of delayed feedback on the neuron's output.
Abstract: A binding neuron (BN) with delayed feedback is considered. The neuron is fed externally with a Poisson stream of intensity λ. The neuron’s output spikes are fed back into its input with time delay Δ. The resulting output stream of the BN is not Poissonian. The main purpose of this paper is to find interspike intervals (ISI) distribution of the output stream. For BN with threshold 2 the exact mathematical expressions as functions of λ, Δ and BN’s internal memory, τ are derived for the ISI distribution and coefficient of variation. For higher thresholds these quantities are found numerically. The distributions found are characterized with jumps, derivative discontinuities and include singularity of Dirac’s δ-function type. The ISI coefficient of variation found is a unimodal function of input intensity, with the maximum value considerably bigger than unity. It is concluded that delayed feedback presence can radically alter neuronal output firing statistics.
13 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 1 |
| 2018 | 2 |
| 2017 | 1 |
| 2015 | 1 |
| 2014 | 2 |
| 2013 | 3 |