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• A.K.Vidybida. "Estimation of possible selectivity and sensitivity of a cooperative system to low-intensive microwave radiation".
  (Talk made on the "Electromagnetic Hypersensitivity, 2nd Copenhagen Conference", Copenhagen, 1995),
  Published in "Physics of the Alive", Vol.3, No.1, 1995, p.38-39.
  PDF
  
• A.K.Vidybida. "Selectivity improvement of olfactory projection neurons at low concentration of odors"
  (Talk made online October 2, 2020 to students of Medical Faculty of Yozgat Bozok University (Turkey)).
  Abstract: A possible mechanism that provides increased selectivity of olfactory bulb projection neurons, as compared
  to that of the primary olfactory receptor neurons, has been proposed. The mechanism operates at low concentrations
  of the odor molecules, when the lateral inhibition mechanism becomes inefficient. The mechanism proposed is based
  on a threshold-type reaction to the stimuli received by a projection neuron from a few receptor neurons, the stochastic
  nature of these stimuli, and the existence of electrical leakage in the projection neurons. The mechanism operates
  at the level of the single individual projection neuron and does not require the involvement of other bulbar neurons.
  (see paper, or preprint)
  Presentation
  
• A.K.Vidybida. "Calculating permutation entropy without permutations"
  (Talk made online March 11, 2021 at Bogolyubov Readings (Kyiv)).
  Abstract: A method for analyzing sequential data sets, similar to the permutation entropy one,
  is discussed. The characteristic features of this method are as follows: it preserves information about equal values,
  if any, in the embedding vectors; it is exempt from combinatorics; and it delivers the same entropy
  value as does the permutation method, provided the embedding vectors do not have equal components.
  In the latter case, this method can be used instead of the permutation one. If embedding vectors have equal
  components, this method could be more precise in discriminating between similar data sets. (see paper, or preprint)
  Presentation, Zoom extended Seminar talk (Ukrainian)
  
• A.Vidybida, O.Shchur. "Firing statistics of a neuron with delayed feedback inhibition stimulated with a renewal point process"
  (Talk made online to the Neural Coding 2021 Workshop)
  Abstract: Recently, the importance of cortical disinhibition – the transient ceasing of inhibition – was
  recognized for various functions, for instance, learning and memory [2]. It was shown that for fast-
  spiking interneurons the main source of inhibition is autaptic transmission [1]. The latter means
  that such neurons send synaptic connections not only to other cells but also to themselves. We
  study the impact of inhibitory autapse on neuronal activity. We consider a class of non-adaptive
  spiking neuron models with delayed feedback inhibition. The neuron is stimulated with a series
  of excitatory impulses, representing a stochastic point renewal process. We calculate exactly the
  probability density function (PDF) p(t) for the distribution of output interspike intervals (ISIs).
  The calculation is based on the known PDF p0(t) for the same neuron without feedback and the
  PDF of ISIs for the input stream pin(t). Obtained results are applied to the case of a neuron
  with threshold 2 when the time intervals between input impulses are distributed according to the
  Erlang distribution.
  References
  [1] C. Deleuze, G. S. Bhumbra, A. Pazienti, J. Louren¸co, C. Mailhes, A. Aguirre, M. Beato,
  and A. Bacci. Strong preference for autaptic self-connectivity of neocortical PV interneurons
  facilitates their tuning to γ-oscillations. PLOS Biology, 17(9):e3000419, sep 2019.
  [2] J. J. Letzkus, S. B. Wolff, and A. Lu¨thi. Disinhibition, a Circuit Mechanism for Associative
  Learning and Memory. Neuron, 88(2):264–276, 2015.
  Presentation.
  
• A.Vidybida, O.Shchur, V.Mochulska. "From chaos to clock in reverberating neural net. Case study"
  (Talk made online to the Neural Coding 2021 Workshop)
  Abstract: It is now accepted (see, e.g. [1]) that knowing only neuronal types and their interconnections (connectome)
  is not enough for understanding how a certain type of spatio-temporal activity emerges in the brain. A concept of
  dynome is proposed as a “collection of experimental and modeling observations” [1], aimed at obtaining a mechanistic
  explanation of various complex brain dynamics.
  We believe that complex dynamics is an inherent feature of neural networks with delayed interneuronal communication.
  A modeling illustration of this feature can be found, e.g. in [2, 3]. In this contribution we propose yet another example of
  complex dynamical behavior in a simple delayed neural net.
  We model numerically a fully connected deterministic network of 25 LIF neurons placed at 5x5 lattice nodes.
  Propagation delays are taken proportional to the interneuronal distances. The network is initially stimulated with a short
  sequence of 25 input impulses, each triggering one of the 25 neurons. The sequence of the triggering moments
  constitutes a stimulus specificity. After the initial stimulation, the network runs on its own, without external influence.
  A stimulus has been found which triggers a seemingly chaotic behavior of the network’s state parameters, such as
  voltage of a neuron. This type of dynamics lasts for a long time (Tr = 7.3 minutes) as compared to the lattice diagonal
  x propagation delay, Td = 5.7 milliseconds. After that, the dynamics becomes periodic with period Tp = 9.6 milliseconds.
  The relaxation dynamics is positively checked for being chaotic by several standard tests such as 0-1 test,
  arithmetic entropy, and sensitivity to small perturbations of the input stimulus.
  References
  [1] N. J. Kopell, H. J. Gritton, M. A. Whittington, and M. A. Kramer. Beyond the connectome: The dynome.
  Neuron, 83(6):1319–1328, 2014.
  [2] A. K. Vidybida. Testing of information condensation in a model reverberating spiking neural network.
  International Journal of Neural Systems, 21(3):187–198, 2011.
  [3] A. Vidybida and O. Shchur. Information reduction in a reverberatory neuronal network through
  convergence to complex oscillatory firing patterns. BioSystems, 161:24–30, 2017.
  Presentation, video1, video2,
  
• A.K.Vidybida. "Physical mechanism of signal processing in biological neural networks"
  (Talk made online to XII Conference of Young Scientists PROBLEMS OF THEORETICAL PHYSICS, Kyiv, Ukraine December 21–23, 2021)
  Zoom recorded (Ukrainian) Presentation.