Igor Timofeev

Slow state transitions of sustained neural oscillations by activity-dependent modulation of intrinsic excitability.

Little is known about the dynamics and mechanisms of transitions between tonic firing and bursting in cortical networks. Here, we use a computational model of a neocortical circuit with extracellular potassium dynamics to show that activity-dependent modulation of intrinsic excitability can lead to sustained oscillations with slow transitions between two distinct firing modes: fast run (tonic spiking or fast bursts with few spikes) and slow bursting. These transitions are caused by a bistability with hysteresis in a pyramidal cell model. Balanced excitation and inhibition stabilizes a network of pyramidal cells and inhibitory interneurons in the bistable region and causes sustained periodic alternations between distinct oscillatory states. During spike-wave seizures, neocortical paroxysmal activity exhibits qualitatively similar slow transitions between fast run and bursting. We therefore predict that extracellular potassium dynamics can cause alternating episodes of fast and slow oscillatory states in both normal and epileptic neocortical networks.

Effect of Synaptic Connectivity on Long-Range Synchronization of Fast Cortical Oscillations

Cortical gamma oscillations in the 20- to 80-Hz range are associated with attentiveness and sensory perception and have strong connections to both cognitive processing and temporal binding of sensory stimuli. These gamma oscillations become synchronized within a few milliseconds over distances spanning a few millimeters in spite of synaptic delays. In this study using in vivo recordings and large-scale cortical network models, we reveal a critical role played by the network geometry in achieving precise long-range synchronization in the gamma frequency band. Our results indicate that the presence of many independent synaptic pathways in a two-dimensional network facilitate precise phase synchronization of fast gamma band oscillations with nearly zero phase delays between remote network sites. These findings predict a common mechanism of precise oscillatory synchronization in neuronal networks.

Supervised semi-automatic detection of slow waves in non-anaesthetized mice with the use of neural network approach

Slow waves (SWs) are EEG or local field potential (LFP) events that are present preferentially during slow-wave sleep and reflect periods of synchronized hyperpolarization followed by depolarization of many cortical neurons. We developed a new algorithm of supervised semi-automatic SW detection based on pattern recognition of the original signal with artificial neural network. The method enabled fast analysis of long-lasting recordings in non-anaesthetized freely behaving mice. It allowed finding tens of thousands of SW in 24-hour period of recording with their density in the order of 1.3 SW per second during slow-wave sleep and 0.03 SW per second during waking state. Occasional SWs were also found in REM sleep. The proposed algorithm can be used for off-line and on-line detection of SW. Correspondence to: Igor Timofeev, Centre de Recherche Institut Universitaire en Santé Mentale de Québec (CRIUSMQ), Université Laval, Québec, G1J 2G3, Canada, Tel: 418-663-5747 ext. 6396, Fax: 418-663-8756; E-mail: Igor.Timofeev@phs.ulaval.ca