Robert Mill

Multistability in auditory stream segregation: a predictive coding view

Auditory stream segregation involves linking temporally separate acoustic events into one or more coherent sequences. For any non-trivial sequence of sounds, many alternative descriptions can be formed, only one or very few of which emerge in awareness at any time. Evidence from studies showing bi-/multistability in auditory streaming suggest that some, perhaps many of the alternative descriptions are represented in the brain in parallel and that they continuously vie for conscious perception. Here, based on a predictive coding view, we consider the nature of these sound representations and how they compete with each other. Predictive processing helps to maintain perceptual stability by signalling the continuation of previously established patterns as well as the emergence of new sound sources. It also provides a measure of how well each of the competing representations describes the current acoustic scene. This account of auditory stream segregation has been tested on perceptual data obtained in the auditory streaming paradigm.

A neurocomputational model of stimulus-specific adaptation to oddball and Markov sequences.

Stimulus-specific adaptation (SSA) occurs when the spike rate of a neuron decreases with repetitions of the same stimulus, but recovers when a different stimulus is presented. It has been suggested that SSA in single auditory neurons may provide information to change detection mechanisms evident at other scales (e.g., mismatch negativity in the event related potential), and participate in the control of attention and the formation of auditory streams. This article presents a spiking-neuron model that accounts for SSA in terms of the convergence of depressing synapses that convey feature-specific inputs. The model is anatomically plausible, comprising just a few homogeneously connected populations, and does not require organised feature maps. The model is calibrated to match the SSA measured in the cortex of the awake rat, as reported in one study. The effect of frequency separation, deviant probability, repetition rate and duration upon SSA are investigated. With the same parameter set, the model generates responses consistent with a wide range of published data obtained in other auditory regions using other stimulus configurations, such as block, sequential and random stimuli. A new stimulus paradigm is introduced, which generalises the oddball concept to Markov chains, allowing the experimenter to vary the tone probabilities and the rate of switching independently. The model predicts greater SSA for higher rates of switching. Finally, the issue of whether rarity or novelty elicits SSA is addressed by comparing the responses of the model to deviants in the context of a sequence of a single standard or many standards. The results support the view that synaptic adaptation alone can explain almost all aspects of SSA reported to date, including its purported novelty component, and that non-trivial networks of depressing synapses can intensify this novelty response.

Stable individual characteristics in the perception of multiple embedded patterns in multistable auditory stimuli.

The ability of the auditory system to parse complex scenes into component objects in order to extract information from the environment is very robust, yet the processing principles underlying this ability are still not well understood. This study was designed to investigate the proposal that the auditory system constructs multiple interpretations of the acoustic scene in parallel, based on the finding that when listening to a long repetitive sequence listeners report switching between different perceptual organizations. Using the “ABA-” auditory streaming paradigm we trained listeners until they could reliably recognize all possible embedded patterns of length four which could in principle be extracted from the sequence, and in a series of test sessions investigated their spontaneous reports of those patterns. With the training allowing them to identify and mark a wider variety of possible patterns, participants spontaneously reported many more patterns than the ones traditionally assumed (Integrated vs. Segregated). Despite receiving consistent training and despite the apparent randomness of perceptual switching, we found individual switching patterns were idiosyncratic; i.e., the perceptual switching patterns of each participant were more similar to their own switching patterns in different sessions than to those of other participants. These individual differences were found to be preserved even between test sessions held a year after the initial experiment. Our results support the idea that the auditory system attempts to extract an exhaustive set of embedded patterns which can be used to generate expectations of future events and which by competing for dominance give rise to (changing) perceptual awareness, with the characteristics of pattern discovery and perceptual competition having a strong idiosyncratic component. Perceptual multistability thus provides a means for characterizing both general mechanisms and individual differences in human perception.

Characterising switching behaviour in perceptual multi-stability

When people experience an unchanging sensory input for a long period of time, their perception tends to switch stochastically and unavoidably between alternative interpretations of the sensation; a phenomenon known as perceptual bi-stability or multi-stability. The huge variability in the experimental data obtained in such paradigms makes it difficult to distinguish typical patterns of behaviour, or to identify differences between switching patterns. Here we propose a new approach to characterising switching behaviour based upon the extraction of transition matrices from the data, which provide a compact representation that is well-understood mathematically. On the basis of this representation we can characterise patterns of perceptual switching, visualise and simulate typical switching patterns, and calculate the likelihood of observing a particular switching pattern. The proposed method can support comparisons between different observers, experimental conditions and even experiments. We demonstrate the insights offered by this approach using examples from our experiments investigating multi-stability in auditory streaming. However, the methodology is generic and thus widely applicable in studies of multi-stability in any domain.

Abstract stimulus-specific adaptation models

Many neurons that initially respond to a stimulus stop responding if the stimulus is presented repeatedly but recover their response if a different stimulus is presented. This phenomenon is referred to as stimulus-specific adaptation (SSA). SSA has been investigated extensively using oddball experiments, which measure the responses of a neuron to sequences of stimuli. Neurons that exhibit SSA respond less vigorously to common stimuli, and the metric typically used to quantify this difference is the SSA index (SI). This article presents the first detailed analysis of the SI metric by examining the question: How should a system (e.g., a neuron) respond to stochastic input if it is to maximize the SI of its output? Questions like this one are particularly relevant to those wishing to construct computational models of SSA. If an artificial neural network receives stimulus information at a particular rate and must respond within a fixed time, what is the highest SI one can reasonably expect? We demonstrate that the optimum, average SI is constrained by the information in the input source, the length and encoding of the memory, and the assumptions concerning how the task is decomposed.

A Model of Stimulus-Specific Adaptation in Neuromorphic Analog VLSI

Stimulus-specific adaptation (SSA) is a phenomenon observed in neural systems which occurs when the spike count elicited in a single neuron decreases with repetitions of the same stimulus, and recovers when a different stimulus is presented. SSA therefore effectively highlights rare events in stimulus sequences, and suppresses responses to repetitive ones. In this paper we present a model of SSA based on synaptic depression and describe its implementation in neuromorphic analog very-large-scale integration (VLSI). The hardware system is evaluated using biologically realistic spike trains with parameters chosen to reflect those of the stimuli used in physiological experiments. We examine the effect of input parameters and stimulus history upon SSA and show that the trends apparent in the results obtained in silico compare favorably with those observed in biological neurons.

CHAINS: Competition and cooperation between fragmentary event predictors in a Model of Auditory Scene Analysis

This paper presents an algorithm called Chains for separating temporal patterns of events that are mixed together. The algorithm is motivated by the task the auditory system faces when it attempts to analyse an acoustic mixture to determine the sources that contribute to it, and in particular, sources that emit regular sequences. The task is complicated by the fact that a mixture can be interpreted in several ways. For example, a complex pattern may issue from a complex source; or, alternatively, it may arise from the interaction of many simple sources. The idea pursued here is that the brain attempts to account for an incoming sequence in terms of short, fragmentary sequences, called chains. Chains are built as the input arrives and, once built, are used to predict inputs. A group of chains can coalesce to form an organisation, in which the member chains alternately generate predictions. A chain fails upon making an incorrect prediction, and any organisation it belongs to collapses. Several incompatible organisations can exist in parallel. The Chains algorithm thus remains open to multiple interpretations of a sequence. Perceptual multistability, in which the perceptual experience of an ambiguous stimulus switches spontaneously from one interpretation to another, seems to require a similar flexibility of representation.

A multimodal-corpus data collection system for cognitive acoustic scene analysis

We report on the design and the collection of a multi-modal data corpus for cognitive acoustic scene analysis. Sounds are generated by stationary and moving sources (people), that is by omni-directional speakers mounted on peoples heads. One or two subjects walk along predetermined systematic and random paths, in synchrony and out of sync. Sound is captured in multiple microphone systems, including a four MEMS microphone directional array, two electret microphones situated in the ears of a stuffed gerbil head, and a Head Acoustics, head-shoulder unit with ICP microphones. Three micro-Doppler units operating at different frequencies were employed to capture gait and the articulatory signatures as well as location of the people in the scene. Three ground vibration sensors were recording the footsteps of the walking people. A 3D MESA camera as well as a web-cam provided 2D and 3D visual data for system calibration and ground truth. Data were collected in three environments ranging from a well controlled environment (anechoic chamber), an indoor environment (large classroom) and the natural environment of an outside courtyard. A software tool has been developed for the browsing and visualization of the data.

Emergent Feature Sensitivity in a Model of the Auditory Thalamocortical System

If, as is widely believed, perception is based upon the responses of neurons that are tuned to stimulus features, then precisely what features are encoded and how do neurons in the system come to be sensitive to those features? Here we show differential responses to ripple stimuli can arise through exposure to formative stimuli in a recurrently connected model of the thalamocortical system which exhibits delays, lateral and recurrent connections, and learning in the form of spike timing dependent plasticity.

A model of stimulus-specific adaptation in neuromorphic a VLSI

Stimulus-specific adaptation (SSA) is a phenomenon observed in neural systems which occurs when the spike count elicited in a single neuron by external stimuli decreases with repetitions of the same stimulus, and recovers when a different stimulus is presented. SSA therefore effectively highlights rare events in stimulus sequences, and suppresses responses to repetitive ones. In this paper we present a model of SSA based on synaptic depression and describe its implementation in neuromorphic analog VLSI. The hardware system is evaluated using biologically realistic spike trains with parameters chosen to match those used in physiological experiments. We examine the effect of input parameters upon SSA and show that the trends apparent in the results obtained in silico compare favourably with those observed in biological neurons.