Conditioned inhibition of action potential generation in isolated Helix pomatia neurons

Grechenko, T. N.

doi:10.1007/bf01192350

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Such complex networks may exist in all neurons and may control different complex responses and functions. For the first time to our knowledge, conditioned stimulus on the isolated neuron was found in [14]. Molecular networks governing the synapse functioning are not less complex than those controlling the membrane responsibility [15,16].…”

Section: Motivationmentioning

confidence: 94%

Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks

Nikitin¹,

Lukyanova²,

Kunin³

2021

Preprint

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Biological neurons have adaptive nature and perform complex computations involving the filtering of redundant information. Such processing is often associated with Bayesian inference. Yet most common models of neural cells, including biologically plausible, such as Hodgkin-Huxley or Izhikevich do not possess predictive dynamics on the level of a single cell. The modern rules of synaptic plasticity or interconnections weights adaptation also do not provide grounding for the ability of neurons to adapt to the ever-changing input signal intensity. While natural neuron synaptic growth is precisely controlled and restricted by protein supply and recycling, weight correction rules such as widely used STDP are efficiently unlimited in change rate and scale. In the present article, we will introduce new mechanics of interconnection between neuron firing rate homeostasis and weight change by means of STDP growth bounded by abstract protein reserve, controlled by the intracellular optimization algorithm. We will show, how these cellular dynamics help neurons to filter out the intense signals to help neurons keep a stable firing rate. We will also examine that such filtering does not affect the ability of neurons to recognize the correlated inputs in unsupervised mode. Such an approach might be used in the machine learning domain to improve the robustness of AI systems.

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Section: Motivationmentioning

confidence: 94%

Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks

Nikitin¹,

Lukyanova²,

Kunin³

2021

Preprint

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“…Нейрон должен поддерживать постоянный уровень внутриклеточной концентрации ионов 2 Ca + , противодействуя эксайтотоксичности [11]. Показано, что единичный изолированный нейрон способен к обучению [12][13][14] и селективному выбору входного сигнала [15]. В работе [10] представлена абстрактная модель нейронной сети, учитывающая данные процессы и выполненную на основе [16,17].…”

Section: Mодель нейронной сетиunclassified

Analysis of Adaptivity and Plasticity in the Network of Homeostatic Neurons

Никитин¹,

Nikitin²

2016

Math.Biol.Bioinf.

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Here we present the model of bioinspired neuron, incorporating cellular homeostasis. We propose the modified STDP plasticity rule, including influence of the input signal on the neuronal cellular homeostatic state. Properties of the network of proposed neurons have been studied. The network was used to control adaptive behavior of an agent in the multi-task environment. Statistical study was performed to find out the adaptive properties of the network with different levels of homeostatical plasticity. Model of the network, incorporating full homeostatically dependent weight correction was able to control an agent in the environment without direct evaluation function. We conclude that important to pay attention to interaction of the input signal and cellular homeostasis in order to reproduce biologically plausible and adaptive behavior of an agent.

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Conditioned inhibition of action potential generation in isolated Helix pomatia neurons

Cited by 2 publications

References 2 publications

Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks

Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks

Analysis of Adaptivity and Plasticity in the Network of Homeostatic Neurons

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