Response of Spiking Neurons to Correlated Inputs

The effect of a temporally correlated afferent current on the firing rate of a leaky integrate-and-fire (LIF) neuron is studied. This current is characterized in terms of rates, auto and cross-correlations, and correlation time scale $\tau_c$ of excitatory and inhibitory inputs. The output rate $\nu_{out}$ is calculated in the Fokker-Planck (FP) formalism in the limit of both small and large $\tau_c$ compared to the membrane time constant $\tau$ of the neuron. By simulations we check the analytical results, provide an interpolation valid for all $\tau_c$ and study the neuron's response to rapid changes in the correlation magnitude.Comment: 4 pages, 3 figure

Dynamical mean-field theory of spiking neuron ensembles: response to a single spike with independent noises

Dynamics of an ensemble of $N$-unit FitzHugh-Nagumo (FN) neurons subject to white noises has been studied by using a semi-analytical dynamical mean-field (DMF) theory in which the original $2 N$-dimensional {\it stochastic} differential equations are replaced by 8-dimensional {\it deterministic} differential equations expressed in terms of moments of local and global variables. Our DMF theory, which assumes weak noises and the Gaussian distribution of state variables, goes beyond weak couplings among constituent neurons. By using the expression for the firing probability due to an applied single spike, we have discussed effects of noises, synaptic couplings and the size of the ensemble on the spike timing precision, which is shown to be improved by increasing the size of the neuron ensemble, even when there are no couplings among neurons. When the coupling is introduced, neurons in ensembles respond to an input spike with a partial synchronization. DMF theory is extended to a large cluster which can be divided into multiple sub-clusters according to their functions. A model calculation has shown that when the noise intensity is moderate, the spike propagation with a fairly precise timing is possible among noisy sub-clusters with feed-forward couplings, as in the synfire chain. Results calculated by our DMF theory are nicely compared to those obtained by direct simulations. A comparison of DMF theory with the conventional moment method is also discussed.Comment: 29 pages, 2 figures; augmented the text and added Appendice

Self-oriented perfectionism and socially prescribed perfectionism: Differential relationships with intrinsic and extrinsic motivation and test anxiety

Previous studies suggest that self-oriented and socially prescribed perfectionism show differential relationships with intrinsic–extrinsic motivation and test anxiety, but the findings are ambiguous. Moreover, they ignored that test anxiety is multidimensional. Consequently, the present study re-investigated the relationships in 104 university students examining how the two forms of perfectionism are related to intrinsic–extrinsic motivation and multidimensional test anxiety (worry, emotionality, interference, lack of confidence, and total anxiety). Regarding motivation, self-oriented perfectionism showed positive correlations with intrinsic reasons for studying, and socially prescribed perfectionism positive correlations with extrinsic reasons. Regarding test anxiety, only socially prescribed perfectionism showed positive correlations with total anxiety. Moreover, socially prescribed perfectionism showed positive correlations with interference and lack of confidence, whereas self-oriented perfectionism showed positive correlations with worry, but negative correlations with interference and lack of confidence. The findings confirm that socially prescribed perfectionism is a maladaptive form of perfectionism associated with extrinsic motivation for studying and higher anxiety in exams. Self-oriented perfectionism, however, is an ambivalent form associated with intrinsic motivation for studying and with both higher and lower anxiety (higher worry, lower interference, lower lack of confidence) in exams

Population rate codes carried by mean, fluctuation and synchrony of neuronal firings

A population of firing neurons is expected to carry information not only by mean firing rate but also by fluctuation and synchrony among neurons. In order to examine this possibility, we have studied responses of neuronal ensembles to three kinds of inputs: mean-, fluctuation- and synchrony-driven inputs. The generalized rate-code model including additive and multiplicative noise (H. Hasegawa, Phys. Rev. E {\bf 75} (2007) 051904) has been studied by direct simulations (DSs) and the augmented moment method (AMM) in which equations of motion for mean firing rate, fluctuation and synchrony are derived. Results calculated by the AMM are in good agreement with those by DSs. The independent component analysis (ICA) of our results has shown that mean firing rate, fluctuation (or variability) and synchrony may carry independent information in the population rate-code model. The input-output relation of mean firing rates is shown to have higher sensitivity for larger multiplicative noise, as recently observed in prefrontal cortex. A comparison is made between results obtained by the integrate-and-fire (IF) model and our rate-code model.Comment: 20 pages, 10 figures, accepted in Physica A (revised version of arXiv:0706.3489

Multi-modal virtual scenario enhances neurofeedback learning

In the past decade neurofeedback (NF) has become the focus of a growing body of research. With real-time functional magnetic resonance imaging (fMRI) enabling online monitoring of emotion-related areas, such as the amygdala, many have begun testing its therapeutic benefits. However, most existing NF procedures still use monotonic uni-modal interfaces, thus possibly limiting user engagement and weakening learning efficiency. The current study tested a novel multi-sensory NF animated scenario (AS) aimed at enhancing user experience and improving learning. We examined whether relative to a simple uni-modal 2D interface, learning via an interface of complex multi-modal 3D scenario will result in improved NF learning. As a neural-probe, we used the recently developed fMRI-inspired EEG model of amygdala activity (“amygdala-EEG finger print”; amygdala-EFP), enabling low-cost and mobile limbic NF training. Amygdala-EFP was reflected in the AS by the unrest level of a hospital waiting room in which virtual characters become impatient, approach the admission desk and complain loudly. Successful downregulation was reflected as an ease in the room unrest level. We tested whether relative to a standard uni-modal 2D graphic thermometer (TM) interface, this AS could facilitate more effective learning and improve the training experience. Thirty participants underwent two separated NF sessions (1 week apart) practicing downregulation of the amygdala-EFP signal. In the first session, half trained via the AS and half via a TM interface. Learning efficiency was tested by three parameters: (a) effect size of the change in amygdala-EFP following training, (b) sustainability of the learned downregulation in the absence of online feedback, and (c) transferability to an unfamiliar context. Comparing amygdala-EFP signal amplitude between the last and the first NF trials revealed that the AS produced a higher effect size. In addition, NF via the AS showed better sustainability, as indicated by a no-feedback trial conducted in session 2 and better transferability to a new unfamiliar interface. Lastly, participants reported that the AS was more engaging and more motivating than the TM. Together, these results demonstrate the promising potential of integrating realistic virtual environments in NF to enhance learning and improve user’s experience

Perceived coach autonomy support, basic need satisfaction and the well- and ill-being of elite youth soccer players: A longitudinal investigation

Objectives: Drawing from the Basic Needs Theory [BNT; Ryan, R. M., & Deci, E. L. (2002). An overview of self-determination theory. In E. L. Deci & R. M. Ryan (Eds.), Handbook of self-determination research (pp. 3-33). Rochester, NY: University of Rochester Press], the major purpose of the present study was to test a hypothesized sequence of temporal relationships between perceptions of coach autonomy support, basic need satisfaction and indices of well- and ill-being. A subsidiary aim was to ascertain the assumed mediational role of basic need satisfaction in explicating the perceived autonomy support and well-/ill-being relationships over time.\ud \ud Method: Participants (N = 54 males) from an elite youth soccer academy in the UK completed a multi-section questionnaire tapping the targeted variables on six occasions across two competitive seasons.\ud \ud Results: Multi-level regression analyses revealed that perceptions of coach autonomy support positively predicted within-person changes and between-person mean differences in basic need satisfaction and well-being over time. \ud \ud Satisfaction scores for the needs for competence and relatedness were found to predict within-person changes in subjective vitality. These same needs partially mediated the coach autonomy support-subjective vitality link over the two seasons.\ud \ud Conclusions: The findings partially support the tenets of BNT, and are discussed in terms of their practical application to participants involved in an elite youth sport setting. \u

Examining exercise dependence symptomatology from a self-determination perspective

Background: Based on the theoretical propositions of Self-Determination Theory (SDT; Deci & Ryan, 1985) this study examined whether individuals classified as “nondependent-symptomatic” and “nondependent-asymptomatic” for exercise dependence differed in terms of the level of exercise-related psychological need satisfaction and self-determined versus controlling motivation they reported. Further, we examined if the type of motivational regulations predicting exercise behaviour differed among these groups. Methods: Participants (N = 339), recruited from fitness, community, and retail settings, completed measures of exercise-specific psychological need satisfaction, motivational regulations, exercise behaviour and exercise dependence. Results: Individuals who were nondependent-symptomatic for exercise dependence reported higher levels of competence need satisfaction and all forms of motivational regulation, compared to nondependent-asymptomatic individuals. Introjected regulation approached significance as a positive predictor of strenuous exercise behaviour for symptomatic individuals. Identified regulation was a positive predictor of strenuous exercise for asymptomatic individuals. Conclusions: The findings reinforce the applicability of SDT to understanding engagement in exercise

Dynamical principles in neuroscience

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA

Stochastic Resonance of Ensemble Neurons for Transient Spike Trains: A Wavelet Analysis

By using the wavelet transformation (WT), we have analyzed the response of an ensemble of $N$ (=1, 10, 100 and 500) Hodgkin-Huxley (HH) neurons to {\it transient} $M$-pulse spike trains ($M=1-3$) with independent Gaussian noises. The cross-correlation between the input and output signals is expressed in terms of the WT expansion coefficients. The signal-to-noise ratio (SNR) is evaluated by using the {\it denoising} method within the WT, by which the noise contribution is extracted from output signals. Although the response of a single (N=1) neuron to sub-threshold transient signals with noises is quite unreliable, the transmission fidelity assessed by the cross-correlation and SNR is shown to be much improved by increasing the value of $N$: a population of neurons play an indispensable role in the stochastic resonance (SR) for transient spike inputs. It is also shown that in a large-scale ensemble, the transmission fidelity for supra-threshold transient spikes is not significantly degraded by a weak noise which is responsible to SR for sub-threshold inputs.Comment: 20 pages, 4 figure

Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale

Sensory processing is associated with gamma frequency oscillations (30–80 Hz) in sensory cortices. This raises the question whether gamma oscillations can be directly involved in the representation of time-varying stimuli, including stimuli whose time scale is longer than a gamma cycle. We are interested in the ability of the system to reliably distinguish different stimuli while being robust to stimulus variations such as uniform time-warp. We address this issue with a dynamical model of spiking neurons and study the response to an asymmetric sawtooth input current over a range of shape parameters. These parameters describe how fast the input current rises and falls in time. Our network consists of inhibitory and excitatory populations that are sufficient for generating oscillations in the gamma range. The oscillations period is about one-third of the stimulus duration. Embedded in this network is a subpopulation of excitatory cells that respond to the sawtooth stimulus and a subpopulation of cells that respond to an onset cue. The intrinsic gamma oscillations generate a temporally sparse code for the external stimuli. In this code, an excitatory cell may fire a single spike during a gamma cycle, depending on its tuning properties and on the temporal structure of the specific input; the identity of the stimulus is coded by the list of excitatory cells that fire during each cycle. We quantify the properties of this representation in a series of simulations and show that the sparseness of the code makes it robust to uniform warping of the time scale. We find that resetting of the oscillation phase at stimulus onset is important for a reliable representation of the stimulus and that there is a tradeoff between the resolution of the neural representation of the stimulus and robustness to time-warp. Author Summary Sensory processing of time-varying stimuli, such as speech, is associated with high-frequency oscillatory cortical activity, the functional significance of which is still unknown. One possibility is that the oscillations are part of a stimulus-encoding mechanism. Here, we investigate a computational model of such a mechanism, a spiking neuronal network whose intrinsic oscillations interact with external input (waveforms simulating short speech segments in a single acoustic frequency band) to encode stimuli that extend over a time interval longer than the oscillation's period. The network implements a temporally sparse encoding, whose robustness to time warping and neuronal noise we quantify. To our knowledge, this study is the first to demonstrate that a biophysically plausible model of oscillations occurring in the processing of auditory input may generate a representation of signals that span multiple oscillation cycles.National Science Foundation (DMS-0211505); Burroughs Wellcome Fund; U.S. Air Force Office of Scientific Researc