Representational capacity of a set of independent neurons

The capacity with which a system of independent neuron-like units represents a given set of stimuli is studied by calculating the mutual information between the stimuli and the neural responses. Both discrete noiseless and continuous noisy neurons are analyzed. In both cases, the information grows monotonically with the number of neurons considered. Under the assumption that neurons are independent, the mutual information rises linearly from zero, and approaches exponentially its maximum value. We find the dependence of the initial slope on the number of stimuli and on the sparseness of the representation.Comment: 19 pages, 6 figures, Phys. Rev. E, vol 63, 11910 - 11924 (2000

Spike latency and response properties of an excitable micropillar laser

We present experimental measurements concerning the response of an excitable micropillar laser with saturable absorber to incoherent as well as coherent perturbations. The excitable response is similar to the behavior of spiking neurons but with much faster time scales. It is accompanied by a subnanosecond nonlinear delay that is measured for different bias pump values. This mechanism provides a natural scheme for encoding the strength of an ultrafast stimulus in the response delay of excitable spikes (temporal coding). Moreover, we demonstrate coherent and incoherent perturbations techniques applied to the micropillar with perturbation thresholds in the range of a few femtojoules. Responses to coherent perturbations assess the cascadability of the system. We discuss the physical origin of the responses to single and double perturbations with the help of numerical simulations of the Yamada model and, in particular, unveil possibilities to control the relative refractory period that we recently evidenced in this system. Experimental measurements are compared to both numerical simulations of the Yamada model and analytic expressions obtained in the framework of singular perturbation techniques. This system is thus a good candidate to perform photonic spike processing tasks in the framework of novel neuroinspired computing systems.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Generalized Fisher information matrix in nonextensive systems with spatial correlation

By using the $q$-Gaussian distribution derived by the maximum entropy method for spatially-correlated $N$-unit nonextensive systems, we have calculated the generalized Fisher information matrix of $g_{\theta_n \theta_m}$ for $(\theta_1, \theta_2, \theta_3) = (\mu_q, \sigma_q^2$, $s$), where $\mu_q$, $\sigma_q^2$ and $s$ denote the mean, variance and degree of spatial correlation, respectively, for a given entropic index $q$. It has been shown from the Cram\'{e}r-Rao theorem that (1) an accuracy of an unbiased estimate of $\mu_q$ is improved (degraded) by a negative (positive) correlation $s$, (2) that of $\sigma_q^2$ is worsen with increasing $s$, and (3) that of $s$ is much improved for $s \simeq -1/(N-1)$ or $s \simeq 1.0$ though it is worst at $s = (N-2)/2(N-1)$. Our calculation provides a clear insight to the long-standing controversy whether the spatial correlation is beneficial or detrimental to decoding in neuronal ensembles. We discuss also a calculation of the $q$-Gaussian distribution, applying the superstatistics to the Langevin model subjected to spatially-correlated inputs.Comment: 18 pages, 3 figures: revised version accepted in Phys. Rev.

Functional Clustering Drives Encoding Improvement in a Developing Brain Network during Awake Visual Learning

Sensory experience drives dramatic structural and functional plasticity in developing neurons. However, for single-neuron plasticity to optimally improve whole-network encoding of sensory information, changes must be coordinated between neurons to ensure a full range of stimuli is efficiently represented. Using two-photon calcium imaging to monitor evoked activity in over 100 neurons simultaneously, we investigate network-level changes in the developing Xenopus laevis tectum during visual training with motion stimuli. Training causes stimulus-specific changes in neuronal responses and interactions, resulting in improved population encoding. This plasticity is spatially structured, increasing tuning curve similarity and interactions among nearby neurons, and decreasing interactions among distant neurons. Training does not improve encoding by single clusters of similarly responding neurons, but improves encoding across clusters, indicating coordinated plasticity across the network. NMDA receptor blockade prevents coordinated plasticity, reduces clustering, and abolishes whole-network encoding improvement. We conclude that NMDA receptors support experience-dependent network self-organization, allowing efficient population coding of a diverse range of stimuli.Canadian Institutes of Health Researc

On-Orbit Performance of the Far Ultraviolet Spectroscopic Explorer (FUSE) Satellite

Launch of the Far Ultraviolet Spectroscopic Explorer (FUSE) has been followed by an extensive period of calibration and characterization as part of the preparation for normal satellite operations. Major tasks carried out during this period include initial coalignment, focusing and characterization of the four instrument channels, and a preliminary measurement of the resolution and throughput performance of the instrument. We describe the results from this test program, and present preliminary estimates of the on-orbit performance of the FUSE satellite based on a combination of this data and prelaunch laboratory measurements.Comment: 8 pages, including 3 figures. This paper will appear in the FUSE special issue of ApJ Letter

Differential Encoding of Factors Influencing Predicted Reward Value in Monkey Rostral Anterior Cingulate Cortex

Background: The value of a predicted reward can be estimated based on the conjunction of both the intrinsic reward value and the length of time to obtain it. The question we addressed is how the two aspects, reward size and proximity to reward, influence the responses of neurons in rostral anterior cingulate cortex (rACC), a brain region thought to play an important role in reward processing. Methods and Findings: We recorded from single neurons while two monkeys performed a multi-trial reward schedule task. The monkeys performed 1–4 sequential color discrimination trials to obtain a reward of 1–3 liquid drops. There were two task conditions, a valid cue condition, where the number of trials and reward amount were associated with visual cues, and a random cue condition, where the cue was picked from the cue set at random. In the valid cue condition, the neuronal firing is strongly modulated by the predicted reward proximity during the trials. Information about the predicted reward amount is almost absent at those times. In substantial subpopulations, the neuronal responses decreased or increased gradually through schedule progress to the predicted outcome. These two gradually modulating signals could be used to calculate the effect of time on the perception of reward value. In the random cue condition, little information about the reward proximity or reward amount is encoded during the course of the trial before reward delivery, but when the reward is actually delivered the responses reflect both the reward proximity and reward amount