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. Musically experienced participants perform better than naïve participants, but even they find the task difficult, despite the fact that musical education includes training in interval recognition.To understand the source of this difficulty we designed an experiment to explore the relative influence of the size of within-pattern intervals and between-pattern transpositions on detecting deviant melodic patterns. We found that task difficulty increases when patterns contain large intervals (5-7 semitones) rather than small intervals (1-3 semitones). While task difficulty increases substantially when transpositions are introduced, the effect of transposition size (large vs small) is weaker. Increasing the range of permissible intervals to be used also makes the task more difficult. Furthermore, providing an initial exact repetition followed by subsequent transpositions does not improve performance. Although musical training correlates with task performance, we find no evidence that violations to musical intervals important in Western music (i.e. the perfect fifth or fourth) are more easily detected. In summary, relative pitch perception does not appear to be conducive to simple explanations based exclusively on invariant physical ratios.

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. This robustness is a property critical to the development of realistic, electronic neuromorphic systems. We analyze the variability of the response of the network to "noisy" stimuli which allows us to characterize the acuity in information-theoretic terms. This provides an objective basis for the quantitative comparison of networks, their connectivity patterns, and learning strategies, which can inform future design decisions. We also show, using stimuli derived from speech samples, that the principles are robust to other challenges, such as variable presentation rate, that would have to be met by systems deployed in the real world. Finally we demonstrate the potential applicability of the approach to real sounds.

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.

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. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems.

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). This article presents a neural field model in which SSA arises through selective adaptation to the frequently-occurring inputs. Formulating the network as a field model allows one to obtain an analytical expression for the expected response of a simple two-layer model to tones in a random sequence. The sequences of stimuli used in SSA experiments contain hundreds-and sometimes thousands-of tones, and these experiments routinely measure the response to many such sequences. A conventional neural network model (e.g., integrate-and-fire) would require numerical integration over long time periods to obtain results. Consequently, a field model that offers an immediate, analytical solution for a given input sequence is helpful. Two routes to obtaining this solution are discussed. The first involves the convolution of two closed-form expressions; the second relies on a series of approximations involving Gaussian curves. The purpose of the paper is to describe the model, to develop the approximations that allow an analytical solution, and finally, to comment on the output of the model in light of the SSA results published in the physiology literature. This article is part of a Special Issue entitled "Neural Coding".

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.

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. 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.

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. None of the existing models of pitch processing can account for such task- and stimulus-dependent variations in processing time scales. This study presents an idealized neurocomputational model, which provides a unified account of the multiple time scales observed in pitch perception. The model is evaluated using a range of perceptual studies, which have not previously been accounted for by a single model, and new results from a neurophysiological experiment. In contrast to other approaches, the current model contains a hierarchy of integration stages and uses feedback to adapt the effective time scales of processing at each stage in response to changes in the input stimulus. The model has features in common with a hierarchical generative process and suggests a key role for efferent connections from central to sub-cortical areas in controlling the temporal dynamics of pitch processing.

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. This measure was motivated in the first instance by the correlation dimension of the attractor; however, it has been modified in several ways in order to increase the neurobiological plausibility. This quantity characterizes each of the cochlear frequency channels and gives rise to a channel clustering criterion. The model computes the clusters and the pitch estimates simultaneously using the same processing mechanisms of delay lines; therefore, it respects the biological constraints in a similar way to temporal theories of pitch. The model successfully explains a wide range of perceptual experiments.

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. In addition, we present evidence that this representation may provide a positive biological advantage in processing classes of sound that are behaviourlly relevant.

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. This method is based on the idea of 'spectro-temporal response fields' and uses convolution to measure the degree of similarity through time between the feature detectors and the stimulus. The output from the ensemble was used to derive segmentation cues and patterns of response, which were used to train an artificial neural network (ANN) classifier. This allowed us to estimate a lower bound for the mutual information between the class of the input and the class of the output. Our results suggest that there is significant information in the output of our system, and that this is robust with respect to the exact choice of feature set, time compression in the stimulus, and speaker variation. In addition, the robustness to time compression in the stimulus has features in common with human psychophysics. Similar experiments using feature detectors derived from fragments of non-speech sounds performed less well. This result is interesting in the light of results showing aberrant cortical development in animals exposed to impoverished auditory environments during the developmental phase.