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Mykola Lysetskiy

Publications and source records attributed to Mykola Lysetskiy.

4 recordsLinked to original sources

Long- and short-term plasticity at mossy fiber synapses on mossy cells in the rat dentate gyrus.

Mossy cells give rise to the commissural and associational pathway of the dentate gyrus, and receive their major excitatory inputs from the mossy fibers of granule cells. Through these feed-back excitatory connections, mossy cells have been suggested to play important roles in both normal signal processing in learning and memory, as well as in seizure propagation. However, the nature of the activity-dependent modifications of the mossy fiber inputs to mossy hilar cells is not well understood. We studied the long- and short-term plasticity properties of the mossy fiber-mossy cell synapse, using the minimal stimulation technique in slices in whole cell recorded mossy cells retrogradely prelabeled with the fluorescent dye DiO from the contralateral dentate gyrus. Following tetanic stimulation, mossy fiber synapses showed significant NMDA receptor-independent long-term potentiation (LTP), associated with increased excitatory postsynaptic currents (EPSC) amplitude and decreased failure rates. Coefficient of variance and failure rate analyses suggested a presynaptic locus of LTP induction. Mossy fiber synapses on mossy cells also showed activity-dependent short-term modification properties, including both frequency-dependent facilitation (stimuli at higher frequencies evoked larger EPSCs with lower failure rates) and burst facilitation (each EPSC in a burst had a larger amplitude and higher probability of occurrence than the preceding EPSCs within the burst). The data show that mossy fiber-mossy cell synapses exhibit both long- and short-term plasticity phenomena that are generally similar to the mossy fiber synapses on CA3 pyramidal cells.

Action Potentials↗

Bifurcating neuron: computation and learning.

The ability of bifurcating processing units and their networks to rapidly switch between different dynamic modes has been used in recent research efforts to model new computational properties of neural systems. In this spirit, we devise a bifurcating neuron based on control of chaos collapsing to a period-3 orbit in the dynamics of a quadratic logistic map (QLM). Proposed QLM3 neuron is constructed with the third iterate of QLM and uses an external input, which governs its dynamics. The input shifts the neuron's dynamics from chaos to one of the stable fixed points. This way the inputs from certain ranges (clusters) are mapped to stable fixed points, while the rest of the inputs is mapped to chaotic or periodic output dynamics. It has been shown that QLM3 neuron is able to learn a specific mapping by adaptively adjusting its bifurcation parameter, the idea of which is based on the principles of parametric control of logistic maps [Proceedings of the International Symposium on Nonlinear Theory and its Applications (NOLTA'97), Honolulu, HI, 1997; Proceedings of SPIE, 2000]. Learning algorithm for the bifurcation parameter is proposed, which employs the error gradient descent method.

Learning↗

Signal processing with temporal sequences in olfactory systems.

The olfactory system is a very efficient biological setup capable of odor information processing with neural signals. The nature of neural signals restricts the information representation to multidimensional temporal sequences of spikes. The information is contained in the interspike intervals within each individual neural signal and interspike intervals between multiple signals. A mechanism of interactions between random excitations evoked by odorants in the olfactory receptors of the epithelium and deterministic operation of the olfactory bulb is proposed in this paper. Inverse Frobenius-Perron models of the bulb's temporal sequences are fitted to the interspike distributions of temporally modulated receptor signals. Ultimately, such pattern matching results in ability to recognize odors and offer a hypothetic model for signal processing occurring in the primary stage of the olfactory system.

Action Potentials↗

Temporal-to-spatial dynamic mapping, flexible recognition, and temporal correlations in an olfactory cortex model.

This paper proposes temporal-to-spatial dynamic mapping inspired by neural dynamics of the olfactory cortex. In our model the temporal structure of olfactory-bulb patterns is mapped to the spatial dynamics of the ensemble of cortical neurons. This mapping is based on the following biological mechanism: while anterior part of piriform cortex can be excited by the afferent input alone, the posterior areas require both afferent and association signals, which are temporally correlated in a specific way. One of the functional types of the neurons in our model corresponds to the cortical spatial dynamics and encodes odor components, and another represents temporal activity of association-fiber signals, which, we suggest, may be relevant to the encoding of odor concentrations. The temporal-to-spatial mapping and distributed representation of the model enable simultaneous rough cluster classification and fine recognition of patterns within a cluster as parts of the same dynamic process. The model is able to extract and segment the components of complex odor patterns which are spatiotemporal sequences of neural activity.

Animals↗