The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction

Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron's probability of spiking. One popular method, known as maximally informative dimensions (MID), uses an information-theoretic quantity known as "single-spike information" to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex

Perception of categories: from coding efficiency to reaction times

Reaction-times in perceptual tasks are the subject of many experimental and theoretical studies. With the neural decision making process as main focus, most of these works concern discrete (typically binary) choice tasks, implying the identification of the stimulus as an exemplar of a category. Here we address issues specific to the perception of categories (e.g. vowels, familiar faces, ...), making a clear distinction between identifying a category (an element of a discrete set) and estimating a continuous parameter (such as a direction). We exhibit a link between optimal Bayesian decoding and coding efficiency, the latter being measured by the mutual information between the discrete category set and the neural activity. We characterize the properties of the best estimator of the likelihood of the category, when this estimator takes its inputs from a large population of stimulus-specific coding cells. Adopting the diffusion-to-bound approach to model the decisional process, this allows to relate analytically the bias and variance of the diffusion process underlying decision making to macroscopic quantities that are behaviorally measurable. A major consequence is the existence of a quantitative link between reaction times and discrimination accuracy. The resulting analytical expression of mean reaction times during an identification task accounts for empirical facts, both qualitatively (e.g. more time is needed to identify a category from a stimulus at the boundary compared to a stimulus lying within a category), and quantitatively (working on published experimental data on phoneme identification tasks)

Information transmission in oscillatory neural activity

Periodic neural activity not locked to the stimulus or to motor responses is usually ignored. Here, we present new tools for modeling and quantifying the information transmission based on periodic neural activity that occurs with quasi-random phase relative to the stimulus. We propose a model to reproduce characteristic features of oscillatory spike trains, such as histograms of inter-spike intervals and phase locking of spikes to an oscillatory influence. The proposed model is based on an inhomogeneous Gamma process governed by a density function that is a product of the usual stimulus-dependent rate and a quasi-periodic function. Further, we present an analysis method generalizing the direct method (Rieke et al, 1999; Brenner et al, 2000) to assess the information content in such data. We demonstrate these tools on recordings from relay cells in the lateral geniculate nucleus of the cat.Comment: 18 pages, 8 figures, to appear in Biological Cybernetic

Layered Steered Space–Time-Spreading-Aided Generalized MC DS-CDMA

Abstract—We present a novel trifunctional multiple-input– multiple-output (MIMO) scheme that intrinsically amalgamates space–time spreading (STS) to achieve a diversity gain and a Vertical Bell Labs layered space–time (V-BLAST) scheme to attain a multiplexing gain in the context of generalized multicarrier direct-sequence code-division multiple access (MC DS-CDMA), as well as beamforming. Furthermore, the proposed system employs both time- and frequency-domain spreading to increase the number of users, which is also combined with a user-grouping technique to reduce the effects of multiuser interference

Evidence of an Off-resonant Electronic Transport Mechanism in Helicenes

Helical molecules have been identified as potential candidates for investigating electronic transport, spin filtering, or even piezoelectricity. However, the description of the transport mechanism is not straightforward in single molecular junctions. In this work, we study the electronic transport in break junctions of a series of three helical molecules: dithia[$n$]helicenes, with $n=7, 9, 11$ molecular units, and detail the synthesis of two kinds of dithia[11]helicenes, varying the location of the sulfur atoms. Our experimental study demonstrates low conductance values that remain similar across different biases and molecules. Additionally, we assess the length dependence of the conductance for each helicene, revealing an exponential decay characteristic of off-resonant transport. This behaviour is primarily attributed to the misalignment between the energy levels of the molecule-electrodes system. The length dependence trend described above is supported by \textit{ab initio} calculations, further confirming the off-resonant transport mechanism