Relative Pitch Perception and the Detection of Deviant Tone Patterns.

Most people are able to recognise familiar tunes even when played in a different key. It is assumed that this depends on a general capacity for relative pitch perception; the ability to recognise the pattern of inter-note intervals that characterises the tune. However, when healthy adults are required to detect rare deviant melodic patterns in a sequence of randomly transposed standard patterns they perform close to chance.

A robust sound perception model suitable for neuromorphic implementation.

We have recently demonstrated the emergence of dynamic feature sensitivity through exposure to formative stimuli in a real-time neuromorphic system implementing a hybrid analog/digital network of spiking neurons. This network, inspired by models of auditory processing in mammals, includes several mutually connected layers with distance-dependent transmission delays and learning in the form of spike timing dependent plasticity, which effects stimulus-driven changes in the network connectivity. Here we present results that demonstrate that the network is robust to a range of variations in the stimulus pattern, such as are found in naturalistic stimuli and neural responses.

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.

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System.

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture.

Characterising stimulus-specific adaptation using a multi-layer field model.

The response of an auditory neuron to a tone is often affected by the context in which the tone appears. For example, when measuring the response to a random sequence of tones, frequencies that appear rarely elicit a greater number of spikes than those that appear often. This phenomenon is called stimulus-specific adaptation (SSA).

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.

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.

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.

Understanding pitch perception as a hierarchical process with top-down modulation.

Pitch is one of the most important features of natural sounds, underlying the perception of melody in music and prosody in speech. However, the temporal dynamics of pitch processing are still poorly understood. Previous studies suggest that the auditory system uses a wide range of time scales to integrate pitch-related information and that the effective integration time is both task- and stimulus-dependent.

A model of perceptual segregation based on clustering the time series of the simulated auditory nerve firing probability.

This paper introduces a model that accounts quantitatively for a phenomenon of perceptual segregation, the simultaneous perception of more than one pitch in a single complex sound. The method is based on a characterization of the time-varying spike probability generated by a model of cochlear responses to sounds. It demonstrates how the autocorrelation theories of pitch perception contain the necessary elements to define a specific measure in the phase space of the simulated auditory nerve probability of firing time series.

The role of transients in auditory processing.

Enhancement of auditory transients is well documented in the auditory periphery and mid-brain, and single unit investigations have identified units with responses which may underlie this sensitivity. It is also known that transients are important in psychophysics in, for example, speech comprehension and object recognition and grouping. In this work we use a simple phenomenological model of auditory transient extraction, based on the skewness of the distribution of energy inside a frequency dependent time window, and show that this view is consistent with electrophysiological measurements of auditory brainstem responses.

Robust sound classification through the representation of similarity using response fields derived from stimuli during early experience.

Models of auditory processing, particularly of speech, face many difficulties. Included in these are variability among speakers, variability in speech rate, and robustness to moderate distortions such as time compression. We constructed a system based on ensembles of feature detectors derived from fragments of an onset-sensitive sound representation.