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adaptation changes were characterized by supralinear increases of response magnitudes Summary and Discussion when entering the mesopic range (presumably due to simultaneous activation of rods and Both global circuit and local output cones), changes of sensitivity, and adjustments measurements demonstrated adaptation of gain and dynamic range of the responses. changes on different time scales. Furthermore, These processes occurred at different temporal both measurements also revealed scales, taking up to several minutes. unexpectedly slow adaptation within the Surprisingly, we found that two distinct photopic regime at the same brightness level, intensity levels within the photopic range, separated from the mesopic state by at least separated by 2 intensity log units log unit of light intensity. These data suggest (corresponding to ND3 and ND1, i.e. about that there are distinct adaptation substates in 10^4 and 10^6 isomerizations/rodsec) also the retina within the photopic regime.

triggered specific adaptation changes that took It is unlikely that these slow adaptation about 15 minutes to stabilize. They were changes can be explained by photoreceptor mainly characterized by a reduction of the saturation, as this should have symmetric response gain of up to 60%. Interestingly, effects on retinal circuits (measured via local those specific changes were only present in field potentials). Furthermore, the responses the OFF responses (to light decrement), increase again and are stable at the next higher whereas the ON responses (to light increment) light levels. Instead, our observations indicate adapted moderately like at all other light level a deployment of some asymmetrical transitions within the photopic range. mechanisms (different in the ON and OFF Analysis of linear filters showed that retinal pathways) to adjust the dynamic range ganglion cells become faster when the light of cells within the photopic range. Yet this level increased. After each change of the asymmetrical mechanism has the same effect neutral density filter, the latency of the cells on all ganglion cells, both ON and OFF, which dropped by 10 to 30ms. These changes were suggests that it is a global, basic mechanism of completed within first 2-5 minutes of retinal adaptation.

recording, and further on, the latency stayed References very stable at each brightness level with the exception of ND3 and ND1 (i.e., the same 1. E.J. Chichilnisky, A simple white noise analysis of brightness levels where strong slow decrease neuronal light responses. Network: Comput. Neural of OFF responses to the 2-sec flashes was Syst. 12 (2001) 199-213.

observed in local field potentials). Namely, 4- ۻ CROSS-FREQUENCY ENTRAINMENT IN FEED-FORWARD OSCILLATORY NETWORK R.A. Tikidji-Hamburyan1,2, C.C. CanavierDepartment of Cell Biology and Anatomy, Health Sciences Center, Louisiana State University, New Orleans, 70112, USA.

A.B.Kogan Research Institute for Neurocybernetics, Southern Federal University,Rostov-on-Don, 344090, Russia rtikid@lsuhsc.edu Transient synchronization of neurons at high frequency frequency range with period Ph and one range, which is often modulated by lower frequency neuron with low frequency firing rate with rhythms, is believed to play an important role in many intrinsic period Pl (Fig 1A). To assume for cognitive functions. The mechanisms for transient simplicity that all neurons in high frequency synchronization as well as role of low frequency activity in this synchronization are still unclear. Here we population have same intrinsic period Ph, let's present minimal model of a feed-forward network and to consider perturbed period P*h due to show that inter-frequency entrainment can synchronize external input. The variation of period (P*h a population of oscillatory neurons firing at high Ph), which may be determined as phase frequency.

resetting = (P*h Ph)/ Ph, depends on phase of oscillator when input was received, Introduction producing a smooth continuous curve of Transient synchronization of neurons in the gamma band (30-100 Hz) is believed to play an important role in attention, memory tasks and cognitive functions [1, 2]. In many brain regions [3, 4], the gamma oscillation is locked to lower frequency oscillations referred as the theta rhythm (8 10Hz) and amplitude phase resetting in a range [0, 1] (Fig. 1B).

of gamma oscillations in is modulated by theta Figure 1. A is the general structure of model. One low frequency neuron L activates high frequency population one. There are at least two open questions (set of cells marked as H). B: Phase resetting curve for with respect to synchronization in the higher an individual neuron are shown.

frequency band. How this synchronization might occur in a regions separated by big In general case periods Ph and Pl are distances without direct connections between aliquant, that means the ratio Pl/Ph is real synchronized regions and whether slow theta number, a residual between the ratio and oscillations can synchronize gamma closed integer equals to (Pl/Ph N); where population.

N=[Pl/Ph]i is the same ratio rounded to integer.

Here we employ rapidly developing As it simple to find, if this residual is phase reset theory, which considers a reaction compensated by phase resetting, external of periodically firing neuron in respect of oscillator phase-locks neurons in high external input as a one-dimensional curve of frequency population. Thereby if there is some phase resetting (PRC), to show that periodic, phase when:

low frequency driver can entrain and Pl synchronize population of high frequency f ( )= N (1), Ph neurons.

where: f() is PRC, a phase locking exists. A Model solution of equation (1) has a simple geometrical representation shown on Fig. 1B:

Let's to consider a population of the ratio (Pl/Ph N) gives a horizontal line independent neurons which fire at high (required resetting); points of interaction XVI between this line and PRC are solutions. An neurons, therefore we should extend our intuitively clear that phase locking will exist theoretical approach to describe heterogeneous only then: population of neurons.

Pl min (f ()) Nmax (f ( )) (2).

Ph Simple straightforward analysis gives the stability criterion for solutions of Eq. (1):

Fig. 2 Converging to steady state attractor leads to 1 f' ( )< (3), synchronization through high frequency neurons H1 H10. Neurons were entrained by low frequency oscillator L.

where: f' is a slope of PRC in solution point. On Fig. 1B stable solution is marked by Let assume that period Ph is distributed green x, while unstable by red circle. A throughout population and (Ph) is a stability of solution depends from PRC slope, distribution. Taking into account that = f -therefore there are stable and unstable regions, (Pl/Ph N) one can obtain distribution of indicated by green and red markers on phases in population:

abscissa correspondingly.

Pl f' () Pl ( )= Synchronization in homogeneous (5).

( ) (f ( )+N )2 f ()+N population of high-frequency neurons.

Last equation don't take into account As in many dynamical systems steady stability criterion (3). To exclude unstable state solution of Eq. (1) gives an attractor ;

branches of PRC, Eq. (5) should include step system converges to asymptotically. In function h, which should equal to zero in general, evolution of phase for high frequency unstable phases. Therefore Eq. (5) should be neurons might be defined as a map:

rewritten as following:

Pl [n+1] =[n ]+ Nf ( [n]) (4).

Ph Pl f'()h(11f' ()) ()= From Eq. (4) it is clear that several f ()+N ( )periods of low frequency oscillation are (6) Pl required for phase-locking of high frequency ( ) neurons. To illustrate this we performed ( ) f +N mapping of phases for ten high-frequency We tested Eq. (6) in case when (Ph) is neurons H1 H10 which were entrained by normal distribution. We performed simulation one low-frequency oscillator L (Fig.2).

of 200000 Morris-Lecar type II neurons(blue Moments of spikes were obtained by mapping curve on Fig.3), mapping of phases for the (4) with PRC shown on Fig.1 A. The neurons same amount of neurons with PRC obtained were initiated by random phases and gradually from Morris-Lecar model (red curve on Fig.3, slip into stable attractor, showing perfect PRC is shown on Fig.1B) and compared these synchrony in a several hundreds milliseconds results with analytical prediction by Eq. (6) after beginning of emulation.

(black curve on Fig.3). From Fig. 3 it is clear that theoretical prediction and results of Synchronization in heterogeneous simulation as well as emulations match very population of high-frequency neurons.

well. Moreover this theory allows to predict existence of a small portion of neurons, which However it is hard to believe that are phase-locked in minor stable region (small intrinsic period for all neurons in biological bump on left side of Fig.3) and explain the population is a same. Nervous system shows mechanism of clustering in a population.

extreme diversity of individual properties of 4- ۻ locking phases may be predicted in a case of heterogeneous population.

Acknowledgments This work was supported by NIH 5R01MH085387 and NIH 5R01NSgrants.

References 1. Gray, C. M., Synchronous oscillations in neuronal Fig. 3 Phase distribution was obtained by direct systems: Mechanisms and functions;

simulation (blue curve), mapping of phases (red curve) J.Comput. Neuroscience 1994, 1, 11-and analytical solution (black curve).

2. Jensen, O., Kaiser, J., Lachaux, J.-P., Human gammafrequency oscillations associated with attention and Conclusion memory; Trends in Neurosciences, 2007, 30, 317 To summarized the most prominent 3. Schack, B.; Vath, N.; Petsche, H.; Geissler, H.-G., results: first cross-frequency entrainment may Mller, E., Phase-coupling of theta-gamma EEG rhythms during short-term memory processing;

lead to synchronization in high-frequency International Journal of Psychophysiology, 2002, 44, population; stability of phase-locking points 143 depends from PRC slope; to converge in 4. Palva, J. M.; Palva, S. & Kaila, K., Phase Synchrony synchrony system request several periods of among Neuronal Oscillations in the Human Cortex; J.

low-frequency oscillations and distribution of Neuroscience, 2005, 25, 3962- XVI VIDEO-BASED ANALYSIS OF HUMAN MOTOR ACTIVITY FOR FUNCTIONAL STATE ESTIMATION A. Vtyurina, S. Anishchenko, M. Petrushan, D. Shaposhnikov A.B.Kogan Research Institute of Neurocybernetics sasha.vtyurina@gmail.com The amount and complexity of man-machine systems estimation [2], speech analysis, interpretation are increasing dramatically. The operator often of data signals from the vehicle: speed, becomes the least reliable element because of their steering position). Speaking of intrusive fatigue and drowsiness. Therefore, the task of techniques, the most reliable correlate of monitoring human functional state, reflected in persons sleepiness is electroencephalogram (EEG).

motor activity, is crucial. In this paper the task of aircraft pilots functional state estimation is considered.

The main disadvantages are that it is hard to measure and analyze under field conditions Introduction and its characteristic parameters are not undisputed [3]. Such parameters as pulse rate Today the number and the complexity and blood pressure are shown to be suitable for of man-machine systems are increasing. And determining the person's global state the most of the technological failures are of (alert/drowsy) [4].

anthropogenic nature. For example, the role Intrusive methods give precise results but played by human performance in aircraft require constant contact between a person and accidents is shown in the Fig. 1.

sensors in order to retrieve signals needed.

Wires and sensors might be uncomfortable and obstructive which is unacceptable when doing potentially dangerous work.

Although intrusive methods are quite uncomfortable in use, they can be of particular interest when creating a training set for identifying person's non-optimal state and searching for correlates between such states and visually registered activity.

Nonintrusive techniques are currently being employed to assess humans alertness level. Using nonintrusive methods, we can monitor visual characteristics such as blinking Figure 1. The role played by human performance in frequency and duration, gaze direction, head civil aircraft accidents [1].

pose. Blinking frequency and duration, the size The majority of accidents happen due of eyelid cleft were shown to be the best to non-optimal human functional state, e.g.

oculomotor indicators for current alertness drowsiness and fatigue. It is noticed that state. Saccadic speed is a reliable indicator of sleepy people exhibit certain observable fatigue [3].

behaviors, including eye gaze, eyelid and pupil Subjective measures such as self-rated movements, head movement, and facial alertness are often used to match monitored expression.

characteristics and current functional state. A widespread 9-step Karolinska Sleepiness Scale There are plenty of techniques for person (KSS) is used for this purpose [3].

functional state controlling. In general, they The objective of this research is to find can be divided into two groups: intrusive out certain repeatable behavioural patterns. We (brain electrical activity, blood pressure consider it as the first step toward estimation) and nonintrusive (video-based and development of the human functional state Doppler radar-based behavioural signals 4- ۻ estimation system. Much of attention is given To estimate motor activity and to infer to the methods of informative features about human functional state, the posterior extraction from the video signal. data analysis is divided into two stages: frame by-frame analysis of the video recorded, Methods extracting features of interest; analyzing the values obtained.

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