Measuring vowel percepts in human listeners with behavioral response-triggered averaging

A vowel can be largely defined by the frequencies of its first two formants, but the absolute frequencies for a given vowel vary from talker to talker and utterance to utterance. Given this variability, it is unclear what criteria listeners use to identify vowels. To estimate the vowel features for which people listen, we adapted a noise-based reverse-correlation method from auditory neurophysiological studies and vision research (Gold et al., 1999). Listeners presented with the stimulus, which had a random spectrum with levels in 60 frequency bins changing every 0.5 s, were asked to press a key whenever they heard the vowels [a] or [i:]. Reverse-correlation was used to average the spectrum of the noise prior to each key press, thus estimating the features of the vowels for which the participants were listening. The formant frequencies of these reverse-correlated vowels were similar to those of their respective whispered vowels. The success of this response-triggered technique suggests that it may prove useful for estimating other internal representations, including perceptual phenomena like tinnitus. References: Gold, J., Bennett, P. J., and Sekuler, A. B. (1999). “Identification of band-pass filtered faces and letters by human and ideal observers,” Vis. Res. 39(21), 3537–3560

The internal representation of vowel spectra investigated using behavioral response-triggered averaging

Listeners presented with noise were asked to press a key 13 whenever they heard the vowels [a] or [i:]. The noise had a random spectrum, with levels in 60 frequency bins changing every 0.5 s. Reverse correlation was used to average the spectrum of the noise prior to each key press, thus estimating the features of the vowels for which the participants were listening. The formant frequencies of these reverse-correlated vowels were similar to those of their respective whispered vowels. The success of this response-triggered technique suggests that it may prove useful for estimating other internal representations, including perceptual phenomena like tinnitus

Application of the normalization data analysis technique for single specimen R-curve determination

The authors conclude that the normalization technique for single specimen R-curve and J{sub IC} determination can be very effective. Much like EPD, this technique requires some user interpretation/judgement during data analysis and may be difficult to standardize or fully automate even with strict analysis rules

Mathematical properties of neuronal TD-rules and differential Hebbian learning: a comparison

A confusingly wide variety of temporally asymmetric learning rules exists related to reinforcement learning and/or to spike-timing dependent plasticity, many of which look exceedingly similar, while displaying strongly different behavior. These rules often find their use in control tasks, for example in robotics and for this rigorous convergence and numerical stability is required. The goal of this article is to review these rules and compare them to provide a better overview over their different properties. Two main classes will be discussed: temporal difference (TD) rules and correlation based (differential hebbian) rules and some transition cases. In general we will focus on neuronal implementations with changeable synaptic weights and a time-continuous representation of activity. In a machine learning (non-neuronal) context, for TD-learning a solid mathematical theory has existed since several years. This can partly be transfered to a neuronal framework, too. On the other hand, only now a more complete theory has also emerged for differential Hebb rules. In general rules differ by their convergence conditions and their numerical stability, which can lead to very undesirable behavior, when wanting to apply them. For TD, convergence can be enforced with a certain output condition assuring that the δ-error drops on average to zero (output control). Correlation based rules, on the other hand, converge when one input drops to zero (input control). Temporally asymmetric learning rules treat situations where incoming stimuli follow each other in time. Thus, it is necessary to remember the first stimulus to be able to relate it to the later occurring second one. To this end different types of so-called eligibility traces are being used by these two different types of rules. This aspect leads again to different properties of TD and differential Hebbian learning as discussed here. Thus, this paper, while also presenting several novel mathematical results, is mainly meant to provide a road map through the different neuronally emulated temporal asymmetrical learning rules and their behavior to provide some guidance for possible applications

Real-time estimation of horizontal gaze angle by saccade integration using in-ear electrooculography

The manuscript proposes and evaluates a real-time algorithm for estimating eye gaze angle based solely on single-channel electrooculography (EOG), which can be obtained directly from the ear canal using conductive ear moulds. In contrast to conventional high-pass filtering, we used an algorithm that calculates absolute eye gaze angle via statistical analysis of detected saccades. The estimated eye positions of the new algorithm were still noisy. However, the performance in terms of Pearson product-moment correlation coefficients was significantly better than the conventional approach in some instances. The results suggest that in-ear EOG signals captured with conductive ear moulds could serve as a basis for lightweight and portable horizontal eye gaze angle estimation suitable for a broad range of applications. For instance, for hearing aids to steer the directivity of microphones in the direction of the user’s eye gaze

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards

Learning the Optimal Control of Coordinated Eye and Head Movements

Various optimality principles have been proposed to explain the characteristics of coordinated eye and head movements during visual orienting behavior. At the same time, researchers have suggested several neural models to underly the generation of saccades, but these do not include online learning as a mechanism of optimization. Here, we suggest an open-loop neural controller with a local adaptation mechanism that minimizes a proposed cost function. Simulations show that the characteristics of coordinated eye and head movements generated by this model match the experimental data in many aspects, including the relationship between amplitude, duration and peak velocity in head-restrained and the relative contribution of eye and head to the total gaze shift in head-free conditions. Our model is a first step towards bringing together an optimality principle and an incremental local learning mechanism into a unified control scheme for coordinated eye and head movements