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1.  Experimental Implementation of a Biometric Laser Synaptic Sensor 
Sensors (Basel, Switzerland)  2013;13(12):17322-17331.
We fabricate a biometric laser fiber synaptic sensor to transmit information from one neuron cell to the other by an optical way. The optical synapse is constructed on the base of an erbium-doped fiber laser, whose pumped diode current is driven by a pre-synaptic FitzHugh–Nagumo electronic neuron, and the laser output controls a post-synaptic FitzHugh–Nagumo electronic neuron. The implemented laser synapse displays very rich dynamics, including fixed points, periodic orbits with different frequency-locking ratios and chaos. These regimes can be beneficial for efficient biorobotics, where behavioral flexibility subserved by synaptic connectivity is a challenge.
doi:10.3390/s131217322
PMCID: PMC3892820  PMID: 24351638
fiber laser; electronic circuit; neuron; synapse; artificial intelligence; neuroengineering; biorobotics; pump modulation
2.  Adaptive enhancement of learning protocol in hippocampal cultured networks grown on multielectrode arrays 
Learning in neuronal networks can be investigated using dissociated cultures on multielectrode arrays supplied with appropriate closed-loop stimulation. It was shown in previous studies that weakly respondent neurons on the electrodes can be trained to increase their evoked spiking rate within a predefined time window after the stimulus. Such neurons can be associated with weak synaptic connections in nearby culture network. The stimulation leads to the increase in the connectivity and in the response. However, it was not possible to perform the learning protocol for the neurons on electrodes with relatively strong synaptic inputs and responding at higher rates. We proposed an adaptive closed-loop stimulation protocol capable to achieve learning even for the highly respondent electrodes. It means that the culture network can reorganize appropriately its synaptic connectivity to generate a desired response. We introduced an adaptive reinforcement condition accounting for the response variability in control stimulation. It significantly enhanced the learning protocol to a large number of responding electrodes independently on its base response level. We also found that learning effect preserved after 4–6 h after training.
doi:10.3389/fncir.2013.00087
PMCID: PMC3662887  PMID: 23745105
multielectrode arrays; hippocampal cultures; closed-loop; learning in vitro; learning in neural networks
3.  A Homeostatic Model of Neuronal Firing Governed by Feedback Signals from the Extracellular Matrix 
PLoS ONE  2012;7(7):e41646.
Molecules of the extracellular matrix (ECM) can modulate the efficacy of synaptic transmission and neuronal excitability. These mechanisms are crucial for the homeostatic regulation of neuronal firing over extended timescales. In this study, we introduce a simple mathematical model of neuronal spiking balanced by the influence of the ECM. We consider a neuron receiving random synaptic input in the form of Poisson spike trains and the ECM, which is modeled by a phenomenological variable involved in two feedback mechanisms. One feedback mechanism scales the values of the input synaptic conductance to compensate for changes in firing rate. The second feedback accounts for slow fluctuations of the excitation threshold and depends on the ECM concentration. We show that the ECM-mediated feedback acts as a robust mechanism to provide a homeostatic adjustment of the average firing rate. Interestingly, the activation of feedback mechanisms may lead to a bistability in which two different stable levels of average firing rates can coexist in a spiking network. We discuss the mechanisms of the bistability and how they may be related to memory function.
doi:10.1371/journal.pone.0041646
PMCID: PMC3407243  PMID: 22848555
4.  Adaptive and Phase Selective Spike Timing Dependent Plasticity in Synaptically Coupled Neuronal Oscillators 
PLoS ONE  2012;7(3):e30411.
We consider and analyze the influence of spike-timing dependent plasticity (STDP) on homeostatic states in synaptically coupled neuronal oscillators. In contrast to conventional models of STDP in which spike-timing affects weights of synaptic connections, we consider a model of STDP in which the time lags between pre- and/or post-synaptic spikes change internal state of pre- and/or post-synaptic neurons respectively. The analysis reveals that STDP processes of this type, modeled by a single ordinary differential equation, may ensure efficient, yet coarse, phase-locking of spikes in the system to a given reference phase. Precision of the phase locking, i.e. the amplitude of relative phase deviations from the reference, depends on the values of natural frequencies of oscillators and, additionally, on parameters of the STDP law. These deviations can be optimized by appropriate tuning of gains (i.e. sensitivity to spike-timing mismatches) of the STDP mechanism. However, as we demonstrate, such deviations can not be made arbitrarily small neither by mere tuning of STDP gains nor by adjusting synaptic weights. Thus if accurate phase-locking in the system is required then an additional tuning mechanism is generally needed. We found that adding a very simple adaptation dynamics in the form of slow fluctuations of the base line in the STDP mechanism enables accurate phase tuning in the system with arbitrary high precision. Adaptation operating at a slow time scale may be associated with extracellular matter such as matrix and glia. Thus the findings may suggest a possible role of the latter in regulating synaptic transmission in neuronal circuits.
doi:10.1371/journal.pone.0030411
PMCID: PMC3295799  PMID: 22412830
5.  Spiking Signatures of Spontaneous Activity Bursts in Hippocampal Cultures 
Dense dissociated hippocampal cultures are known to generate spontaneous bursting electrical activity which can be recorded by multielectrode arrays. We have analyzed spatio-temporal profiles of the distribution of spikes in the bursts recorded after 2 weeks in vitro. We have found a statistically significant similarity between the spiking patterns in sequential bursting events, we refer to these spiking patterns as spiking signatures. Such spiking signatures may appear in different parts of the bursts, including the activation patterns – the first spike times in the bursts, and deactivation patterns – the last spike times in the bursts. Moreover, these patterns may display apparent time scaling, e.g., they may be replayed in the subsequent bursts at different speeds, while preserving the spiking order. We discuss how such properties of the bursts may be associated with the formation of repeatable signaling pathways in cultured networks in vitro.
doi:10.3389/fncom.2011.00046
PMCID: PMC3213406  PMID: 22087091
neuronal network; microelectrode array; bursts in vitro; spiking patterns

Results 1-5 (5)