In vivo cortical neurons are known to exhibit highly irregular spike patterns. Because the intervals between successive spikes fluctuate greatly, irregular neuronal firing makes it difficult to estimate instantaneous firing rates accurately. If, however, the irregularity of spike timing is decoupled from rate modulations, the estimate of firing rate can be improved. Here, we introduce a novel coding scheme to make the firing irregularity orthogonal to the firing rate in information representation. The scheme is valid if an interspike interval distribution can be well fitted by the gamma distribution and the firing irregularity is constant over time. We investigated in a computational model whether fluctuating external inputs may generate gamma process-like spike outputs, and whether the two quantities are actually decoupled. Whole-cell patch-clamp recordings of cortical neurons were performed to confirm the predictions of the model. The output spikes were well fitted by the gamma distribution. The firing irregularity remained approximately constant regardless of the firing rate when we injected a balanced input, in which excitatory and inhibitory synapses are activated concurrently while keeping their conductance ratio fixed. The degree of irregular firing depended on the effective reversal potential set by the balance between excitation and inhibition. In contrast, when we modulated conductances out of balance, the irregularity varied with the firing rate. These results indicate that the balanced input may improve the efficiency of neural coding by clamping the firing irregularity of cortical neurons. We demonstrate how this novel coding scheme facilitates stimulus decoding. Key words: firing irregularity; neural code; balanced synaptic input; brain-machine interface; gamma distribution; information geometry

Cover: Probabilistic decoding of an arm trajectory from spike trains recorded in motor and premotor cortices using a mixture of trajectory models. Each panel corresponds to a snapshot in time during the same arm trajectory. In this goal-directed reach setting, the trajectory estimate (orange) is obtained by taking a weighted combination of the estimates from each goal-specific mixture component (white). The weights, which correspond to the probability of each reach goal at the time of the snapshot, are represented by the saturation of yellow shading of the reach goals (squares). Ellipses denote 95 % confidence intervals. For details see Yu BM, Kemere C, Santhanam G,

Abstract We introduce a mobile spatial interactive application that uses a combination of a GPS, inertial sensing, gestural interaction, probabilistic models and Monte Carlo sampling, with vibration and audio feedback. This system allows the probing or querying of targets in a local area, based on a model of the local environment and specific context variables of interest, to enable a rich, embodied and location–aware spatial interaction. An experiment was conducted to investigate how spatial target selection at different distances, target separations and target widths is affected by a system with added ‘typical’ noise characteristics. Results showed that the successful selection of targets in the virtual environment is maximised with a combination of high angular separation and angular width.

1.1 Background on transcriptional regulation........................... 1 1.2 Genome-scale data on transcriptional regulation........................ 2 1.3 Overview of this tutorial.................................... 3

In defence of model-based inference in

The emergence of similar collective patterns from different self-propelled particle models of animal groups points to a restricted set of ‘‘universal’ ’ classes for these patterns. While universality is interesting, it is often the fine details of animal interactions that are of biological importance. Universality thus presents a challenge to inferring such interactions from macroscopic group dynamics since these can be consistent with many underlying interaction models. We present a Bayesian framework for learning animal interaction rules from fine scale recordings of animal movements in swarms. We apply these techniques to the inverse problem of inferring interaction rules from simulation models, showing that parameters can often be inferred from a small number of observations. Our methodology allows us to quantify our confidence in parameter fitting. For example, we show that attraction and alignment terms can be reliably estimated when animals are milling in a torus shape, while interaction radius cannot be reliably measured in such a situation. We assess the importance of rate of data collection and show how to test different models, such as topological and metric neighbourhood models. Taken together our results both inform the design of experiments on animal interactions and suggest how these data should be best analysed.