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Neural correlates of auditory perception and the underlying computation
The auditory brain is a flexible, highly interconnected system for processing sound. It's the most complex audio signal processing device ever devised, and the perception of sound is a consequence of the computations it performs. Our research is focused on understanding how these neural computations determine our perception of sound.
Staff Programme Leader Chris Sumner Research Staff Chris Scholes Graduate Students Mark Steadman Toby Wells
Recent Publications Coombes S, Thul R, Laudanski J, Palmer AR, Sumner CJ (2012) Neuronal spike-train responses in the presence of threshold noise: First passage times, stochastic mode-locking, and coding. Frontiers in Life Sciences, in press Sollini J (2011) Behavioural and physiological measures of frequency and binaural integration in the mammalian auditory system. PhD thesis awarded by The University of Nottingham Briley (2011) Disentangling the effects of stimulus context on auditory responses using electroencephalography. PhD thesis awarded by The University of Nottingham Paltoglou AE, Sumner CJ, Hall DA (2011) Mapping feature-sensitivity and attentional modulation in human auditory cortex with functional magnetic resonance imaging. European Journal of Neuroscience 33(9), 1733-41 [Open Access Article (UKPMC)] [PubMed]  Scholes C, Palmer AR, Sumner CJ (2011) Forward suppression in the auditory cortex is frequency-specific. European Journal of Neuroscience 33(7), 1240-51 [Open Access Article (UKPMC)] [PubMed]  Irving S, Moore DR, Liberman MC, Sumner CJ (2011) Olivocochlear efferent control in sound localization and experience-dependent learning. Journal of Neuroscience 31(7), 2493-501 [Open Access Article] [PubMed]  Clark N (2011) The role of temporal and spectral cues in the temporal integration of pitch and pitch-based segregation of sound sources. PhD thesis awarded by The University of Nottingham View all publications from this research group

The brain is able to construct a view of the outside world from sound alone: it lets us listen to music and identify the location of each individual musician ; pick out a bassoon in an orchestra or follow the grand themes of a symphony; follow one person speaking amidst the cacophony of a room full of conversations. Yet the brain receives only two one-dimensional inputs: one from each ear. And even with one ear, we still perceive a world of discrete, separate sound sources. The task of the auditory neuroscientist is to understand how the brain processes sound to perform the remarkable feat of hearing.

The response of a neuron in auditory cortex
Figure 1. The response of a neuron in auditory cortex to a sequence of tones of alternating frequency

Many fundamental questions about how the brain allows us to listen remain unanswered. For example, we do not even know much about how our ability to detect a sound (i.e. ‘yes, I hear it!’, vs. ‘no… don’t hear that’) relates to patterns of neurons firing in the brain. Clearly, a detectable sound must produce some activity in the brain, from which we are able to answer ‘yes!’ But how is this signal represented? And at what place in the brain does this representation emerge? How is the representation affected by the presence of other sounds? And what happens to these representations after damage to the inner ear? Why does even mild hearing impairment seriously affect people’s ability to listen in complex environments? These are the ‘big issues’ that drive work in our lab. Probing more deeply, we also ask how the auditory system ‘computes’ the answer for us.

Recent work in our lab has been probing some specific instances of these questions. For example, that our ability (or lack of ability:’masking’) to detect quiet sounds that are preceded by louder sounds is quite well accounted for by the responses of single neurons in the auditory cortex (Alves-Pinto et al. 2010). Other work has been probing how information from different frequencies is integrated and processed by cortical neurons (Scholes et al. 2011), and how they encode sequences of sounds (figure 1). In order to determine exactly how neural responses relate to our perception, we integrate perceptual and neural experiments – so these different measures can be compared quantitatively (figure 2).

Using masking to characterise the frequency resolution
Figure 2. Using masking to characterise the frequency resolution of the auditory system (top), in neurons (bottom left), and perceptually (bottom right).

Another branch of our recent research examines how signals that vary in time are coded in when neurons fire (figure 3), rather than if. It has long been understood that auditory neurons can signal acoustic features in the timing of their spikes – spikes tend to occur relative to the peaks in the acoustic envelope. We have shown that in brainstem neurons this code can be temporally complex, and non-linearly related to the acoustic envelope (figure 3; Laudanski et al. 2010). The behaviour, known as mode-locking, was predicted by simple computational models of these cells, but had not previously been observed in real neurons. It expands the ‘vocabulary’ neurons use to represent sound.

A (theoretical) mode-locked spiking pattern
Figure 3. A (theoretical) mode-locked spiking pattern in response to a modulated stimulus envelope.

We use computational models to ask how it is that the auditory system might be accomplishing a particular processing feat (Sumner et al. 2009). For example – following an injury to a specific part of the cochlea, the auditory system rapidly ‘reorganises’ itself so that neurons that used to represent the damaged region of the cochlea now represent undamaged regions. We have shown that even a simple model of dendritic processing in a single neuron (figure 4) can produce this ‘reorganising’ without any modification of synapses. Understanding how the brain manages to cope so well with damage to our ears not only helps to understand the consequences of hearing loss, but also tells us more generally about how the brain is able to process sound so flexibly.

A schematic of a computational model of a neuron
Figure 4. A schematic of a computational model of a neuron, with inputs from a computer model of the cochlea