Biology subjects

Telenczuk, B.

Publications and source records attributed to Telenczuk, B..

5 recordsLinked to original sources

bioRxiv2018

Two types of slow waves in anesthetized and sleeping brains

Sleep slow waves are known to participate in memory consolidation, yet slow waves occurring under anesthesia present no positive effects on memory. Here, we shed light onto this paradox, based on a combination of extracellular recordings in vivo, in vitro, and computational models. We find two types of slow waves, based on analyzing the temporal patterns of successive slow-wave events. The first type is consistently observed in natural slow-wave sleep, while the second is shown to be ubiquitous under anesthesia. Network models of spiking neurons predict that the two slow wave types emerge due to a different gain on inhibitory vs excitatory cells and that different levels of spike-frequency adaptation in excitatory cells can account for dynamical distinctions between the two types. This prediction was tested in vitro by varying adaptation strength using an agonist of acetylcholine receptors, which demonstrated a neuromodulatory switch between the two types of slow waves. Finally, we show that the first type of slow-wave dynamics is more sensitive to external stimuli, which can explain how slow waves in sleep and anesthesia differentially affect memory consolidation, as well as provide a link between slow-wave dynamics and memory diseases.

neuroscience↗

bioRxiv2018

Contribution of the axon initial segment to action potentials recorded extracellularly

Action potentials (APs) are electric phenomena that are recorded both intracellularly and extracellularly. APs are usually initiated in the short segment of the axon called the axon initial segment (AIS). It was recently proposed that at onset of an AP the soma and the AIS form a dipole. We study the extracellular signature (the extracellular action potential, EAP) generated by such a dipole. First, we demonstrate the formation of the dipole and its extracellular signature in detailed morphological models of a reconstructed pyramidal neuron. Then, we study the EAP waveform and its spatial dependence in models with axonal AP initiation and contrast it with the EAP obtained in models with somatic AP initiation. We show that in the models with axonal AP initiation the dipole forms between somatodendritic compartments and the AIS, and not between soma and dendrites as in the classical models. Soma-dendrites dipole is present only in models with somatic AP initiation. Our study has consequences for interpreting extracellular recordings of single-neuron activity and determining electrophysiological neuron types, but also for better understanding the origins of the high-frequency macroscopic electric fields recorded in the brain.\n\nNew & NoteworthyWe studied the consequences of the action potential (AP) initiation site on the extracellular signatures of APs. We show that: (1) at the time of AP initiation the action initial segment (AIS) forms a dipole with the soma, (2) the width but not (3) amplitude of the extracellular AP generated by this dipole increases with the soma-AIS distance. This may help to monitor dynamic changes in the AIS position in experimental in vivo recordings.

neuroscience↗

bioRxiv2018

Maximum entropy models reveal the correlation structure in cortical neural activity during wakefulness and sleep

Maximum Entropy models can be inferred from large data-sets to uncover how local interactions generate collective dynamics. Here, we employ such models to investigate the characteristics of neurons recorded by multielectrode arrays in the cortex of human and monkey throughout states of wakefulness and sleep. Taking advantage of the separation of excitatory and inhibitory types, we construct a model including this distinction. By comparing the performances of Maximum Entropy models at predicting neural activity in wakefulness and deep sleep, we identify the dominant interactions between neurons in each brain state. We find that during wakefulness, dominant functional interactions are pairwise while during sleep, interactions are population-wide. In particular, inhibitory neurons are shown to be strongly tuned to the inhibitory population. This shows that Maximum Entropy models can be useful to analyze data-sets with excitatory and inhibitory neurons, and can reveal the role of inhibitory neurons in organizing coherent dynamics in cerebral cortex.

neuroscience↗

bioRxiv2017

Inverse correlation processing by neurons with active dendrites

Many neurons possess dendrites enriched with sodium channels and are capable of generating action potentials. However, the role of dendritic sodium spikes remain unclear. Here, we study computational models of neurons to investigate the functional effects of dendritic spikes. In agreement with previous studies, we found that point neurons or neurons with passive dendrites increase their somatic firing rate in response to the correlation of synaptic bombardment for a wide range of input conditions, i.e. input firing rates, synaptic conductances or refractory periods. However, neurons with active dendrites show the opposite behavior: for a wide range of conditions the firing rate decreases as a function of correlation. We found this property in three types of models of dendritic excitability: a Hodgkin-Huxley model of dendritic spikes, a model with integrate-and-fire dendrites, and a discrete-state dendritic model. We conclude that neurons equipped with with fast dendritic spikes confer much broader computational properties to neurons, sometimes opposite to that of point neurons.

neuroscience↗

bioRxiv2017

Encoding variable cortical states with short-term spikepatterns

Neurons in the primary somatosensory cortex (S1) respond to peripheral stimulation with synchronised bursts of spikes, which lock to the macroscopic 600 Hz EEG waves. The mechanism of burst generation and synchronisation in S1 is not yet understood. Using models of single-neuron responses fitted to unit recordings from macaque monkeys, we show that these synchronised bursts are the consequence of correlated synaptic inputs combined with a refractory mechanism. In the presence of noise these models reproduce also the observed trial-to-trial response variability, where individual bursts represent one of many stereotypical temporal spike patterns. When additional slower and global excitability fluctuations are introduced the single-neuron spike patterns are correlated with the population activity, as demonstrated in experimental data. The underlying biophysical mechanism of S1 responses involves thalamic inputs arriving through depressing synapses to cortical neurons in a high-conductance state. Our findings show that a simple feedforward processing of peripheral inputs could give rise to neuronal responses with non-trivial temporal and population statistics. We conclude that neural systems could use refractoriness to encode variable cortical states into stereotypical short-term spike patterns amenable to processing at neuronal time scales (tens of milliseconds).\n\nSignificance statementNeurons in the hand area of the primary somatosensory cortex respond to repeated presentation of the same stimulus with variable sequences of spikes, which can be grouped into distinct temporal spike patterns. In a simplified model, we show that such spike patterns are product of synaptic inputs and intrinsic neural properties. This model can reproduce both single-neuron and population responses only when a private variability in each neuron is combined with a multiplicative gain shared over whole population, which fluctuates over trials and might represent the dynamical state of the early stages of sensory processing. This phenomenon exemplifies a general mechanism of transforming the ensemble cortical states into precise temporal spike patterns at the level of single neurons.

neuroscience↗