Oscillator neural network model with distributed native frequencies

We study associative memory of an oscillator neural network with distributed native frequencies. The model is based on the use of the Hebb learning rule with random patterns ($\xi_i^{\mu}=\pm 1$), and the distribution function of native frequencies is assumed to be symmetric with respect to its average. Although the system with an extensive number of stored patterns is not allowed to get entirely synchronized, long time behaviors of the macroscopic order parameters describing partial synchronization phenomena can be obtained by discarding the contribution from the desynchronized part of the system. The oscillator network is shown to work as associative memory accompanied by synchronized oscillations. A phase diagram representing properties of memory retrieval is presented in terms of the parameters characterizing the native frequency distribution. Our analytical calculations based on the self-consistent signal-to-noise analysis are shown to be in excellent agreement with numerical simulations, confirming the validity of our theoretical treatment.Comment: 9 pages, revtex, 6 postscript figures, to be published in J. Phys.

Correlations in spiking neuronal networks with distance dependent connections

Can the topology of a recurrent spiking network be inferred from observed activity dynamics? Which statistical parameters of network connectivity can be extracted from firing rates, correlations and related measurable quantities? To approach these questions, we analyze distance dependent correlations of the activity in small-world networks of neurons with current-based synapses derived from a simple ring topology. We find that in particular the distribution of correlation coefficients of subthreshold activity can tell apart random networks from networks with distance dependent connectivity. Such distributions can be estimated by sampling from random pairs. We also demonstrate the crucial role of the weight distribution, most notably the compliance with Dales principle, for the activity dynamics in recurrent networks of different types

Foliar water uptake: a common water acquisition strategy for plants of the redwood forest

Evaluations of plant water use in ecosystems around the world reveal a shared capacity by many different species to absorb rain, dew, or fog water directly into their leaves or plant crowns. This mode of water uptake provides an important water subsidy that relieves foliar water stress. Our study provides the first comparative evaluation of foliar uptake capacity among the dominant plant taxa from the coast redwood ecosystem of California where crown-wetting events by summertime fog frequently occur during an otherwise drought-prone season. Previous research demonstrated that the dominant overstory tree species, Sequoia sempervirens, takes up fog water by both its roots (via drip from the crown to the soil) and directly through its leaf surfaces. The present study adds to these early findings and shows that 80% of the dominant species from the redwood forest exhibit this foliar uptake water acquisition strategy. The plants studied include canopy trees, understory ferns, and shrubs. Our results also show that foliar uptake provides direct hydration to leaves, increasing leaf water content by 2–11%. In addition, 60% of redwood forest species investigated demonstrate nocturnal stomatal conductance to water vapor. Such findings indicate that even species unable to absorb water directly into their foliage may still receive indirect benefits from nocturnal leaf wetting through suppressed transpiration. For these species, leaf-wetting events enhance the efficacy of nighttime re-equilibration with available soil water and therefore also increase pre-dawn leaf water potentials

Significance of Input Correlations in Striatal Function

The striatum is the main input station of the basal ganglia and is strongly associated with motor and cognitive functions. Anatomical evidence suggests that individual striatal neurons are unlikely to share their inputs from the cortex. Using a biologically realistic large-scale network model of striatum and cortico-striatal projections, we provide a functional interpretation of the special anatomical structure of these projections. Specifically, we show that weak pairwise correlation within the pool of inputs to individual striatal neurons enhances the saliency of signal representation in the striatum. By contrast, correlations among the input pools of different striatal neurons render the signal representation less distinct from background activity. We suggest that for the network architecture of the striatum, there is a preferred cortico-striatal input configuration for optimal signal representation. It is further enhanced by the low-rate asynchronous background activity in striatum, supported by the balance between feedforward and feedback inhibitions in the striatal network. Thus, an appropriate combination of rates and correlations in the striatal input sets the stage for action selection presumably implemented in the basal ganglia

Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains

Stimulus properties, attention, and behavioral context influence correlations between the spike times produced by a pair of neurons. However, the biophysical mechanisms that modulate these correlations are poorly understood. With a combined theoretical and experimental approach, we show that the rate of balanced excitatory and inhibitory synaptic input modulates the magnitude and timescale of pairwise spike train correlation. High rate synaptic inputs promote spike time synchrony rather than long timescale spike rate correlations, while low rate synaptic inputs produce opposite results. This correlation shaping is due to a combination of enhanced high frequency input transfer and reduced firing rate gain in the high input rate state compared to the low state. Our study extends neural modulation from single neuron responses to population activity, a necessary step in understanding how the dynamics and processing of neural activity change across distinct brain states

Expressions of Multiple Neuronal Dynamics during Sensorimotor Learning in the Motor Cortex of Behaving Monkeys

Previous studies support the notion that sensorimotor learning involves multiple processes. We investigated the neuronal basis of these processes by recording single-unit activity in motor cortex of non-human primates (Macaca fascicularis), during adaptation to force-field perturbations. Perturbed trials (reaching to one direction) were practiced along with unperturbed trials (to other directions). The number of perturbed trials relative to the unperturbed ones was either low or high, in two separate practice schedules. Unsurprisingly, practice under high-rate resulted in faster learning with more pronounced generalization, as compared to the low-rate practice. However, generalization and retention of behavioral and neuronal effects following practice in high-rate were less stable; namely, the faster learning was forgotten faster. We examined two subgroups of cells and showed that, during learning, the changes in firing-rate in one subgroup depended on the number of practiced trials, but not on time. In contrast, changes in the second subgroup depended on time and practice; the changes in firing-rate, following the same number of perturbed trials, were larger under high-rate than low-rate learning. After learning, the neuronal changes gradually decayed. In the first subgroup, the decay pace did not depend on the practice rate, whereas in the second subgroup, the decay pace was greater following high-rate practice. This group shows neuronal representation that mirrors the behavioral performance, evolving faster but also decaying faster at learning under high-rate, as compared to low-rate. The results suggest that the stability of a new learned skill and its neuronal representation are affected by the acquisition schedule.United States-Israel Binational Science FoundationIsrael Science FoundationIda Baruch FundRosetrees Trus

Noise Suppression and Surplus Synchrony by Coincidence Detection

The functional significance of correlations between action potentials of neurons is still a matter of vivid debates. In particular it is presently unclear how much synchrony is caused by afferent synchronized events and how much is intrinsic due to the connectivity structure of cortex. The available analytical approaches based on the diffusion approximation do not allow to model spike synchrony, preventing a thorough analysis. Here we theoretically investigate to what extent common synaptic afferents and synchronized inputs each contribute to closely time-locked spiking activity of pairs of neurons. We employ direct simulation and extend earlier analytical methods based on the diffusion approximation to pulse-coupling, allowing us to introduce precisely timed correlations in the spiking activity of the synaptic afferents. We investigate the transmission of correlated synaptic input currents by pairs of integrate-and-fire model neurons, so that the same input covariance can be realized by common inputs or by spiking synchrony. We identify two distinct regimes: In the limit of low correlation linear perturbation theory accurately determines the correlation transmission coefficient, which is typically smaller than unity, but increases sensitively even for weakly synchronous inputs. In the limit of high afferent correlation, in the presence of synchrony a qualitatively new picture arises. As the non-linear neuronal response becomes dominant, the output correlation becomes higher than the total correlation in the input. This transmission coefficient larger unity is a direct consequence of non-linear neural processing in the presence of noise, elucidating how synchrony-coded signals benefit from these generic properties present in cortical networks

Attracting Dynamics of Frontal Cortex Ensembles during Memory-Guided Decision-Making

A common theoretical view is that attractor-like properties of neuronal dynamics underlie cognitive processing. However, although often proposed theoretically, direct experimental support for the convergence of neural activity to stable population patterns as a signature of attracting states has been sparse so far, especially in higher cortical areas. Combining state space reconstruction theorems and statistical learning techniques, we were able to resolve details of anterior cingulate cortex (ACC) multiple single-unit activity (MSUA) ensemble dynamics during a higher cognitive task which were not accessible previously. The approach worked by constructing high-dimensional state spaces from delays of the original single-unit firing rate variables and the interactions among them, which were then statistically analyzed using kernel methods. We observed cognitive-epoch-specific neural ensemble states in ACC which were stable across many trials (in the sense of being predictive) and depended on behavioral performance. More interestingly, attracting properties of these cognitively defined ensemble states became apparent in high-dimensional expansions of the MSUA spaces due to a proper unfolding of the neural activity flow, with properties common across different animals. These results therefore suggest that ACC networks may process different subcomponents of higher cognitive tasks by transiting among different attracting states

CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains

Recent developments in electrophysiological and optical recording techniques enable the simultaneous observation of large numbers of neurons. A meaningful interpretation of the resulting multivariate data, however, presents a serious challenge. In particular, the estimation of higher-order correlations that characterize the cooperative dynamics of groups of neurons is impeded by the combinatorial explosion of the parameter space. The resulting requirements with respect to sample size and recording time has rendered the detection of coordinated neuronal groups exceedingly difficult. Here we describe a novel approach to infer higher-order correlations in massively parallel spike trains that is less susceptible to these problems. Based on the superimposed activity of all recorded neurons, the cumulant-based inference of higher-order correlations (CuBIC) presented here exploits the fact that the absence of higher-order correlations imposes also strong constraints on correlations of lower order. Thus, estimates of only few lower-order cumulants suffice to infer higher-order correlations in the population. As a consequence, CuBIC is much better compatible with the constraints of in vivo recordings than previous approaches, which is shown by a systematic analysis of its parameter dependence