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QUÉ HAN MOSTRADO LOS RESULTADOS?
When considering spatial hearing within the framework provided by David Marr, the duplex theory lies somewhere between computational and algorithmic levels of analysis. In this section, I provide a crude overview of the implementation level i.e. of known physiology and anatomy of the binaural hearing system. For a more detailed information one can refer to the recent review article [49] or the book [124] on which this section is based.
Figure 3.3: A schematic view of the ascending auditory pathway. Input from only single cochlea is depicted.
3.3. The inner ear transforms air-borne sounds into the motion of the cochlear fluid. By vibrating together with the fluid waves the cochlear membrane excites hair cells which convert mechanical energy into electric signal - action potentials. Since parts of the basilar membrane have different resonance frequencies this organ performs ”spectral decomposition” of the sound. The underlying hair cells are aligned in a tonotopic map following a precise frequency ordering. Separate auditory nerve fibres projecting from the cochlea to the cochlear nucleus encode sound energy in different frequency channels. Importantly for binaural hearing mammalian auditory fibers are capable of representing the fine structure of sounds lower than 4000 Hz by phase-locking to the stimulus (i.e. eliciting spikes precisely aligned to waveform peaks). This provides a physiological constraint on the representation of fine-structure phase and IPDs.
Spatial information is first time processed in the dorsal cochlear nucleus (DCN). The principal neurons of the DCN are capable of determining notch frequencies with a high accuracy. It has been concluded that it makes them well suited for processing monaural, spectral localization cues.
cues are extracted. ILDs are predominantly computed in the lateral superior olive (LSO). Neurons in this structure are excited by projections from the ipsilateral ear and inhibited from the contralateral side. For this reason they are typically referred to as ”IE” neurons. It seems that their computational task can be understood as a subtraction of signal power in a narrow frequency channel at one side of the head from the power perceived by the opposite ear. An interesting fact about ILD sensitive neurons in the LSO is that in contrast to the majority of sensory neurons they prefer ipsilateral stimuli not contralateral ones.
Neuronal mechanisms of ILD computation seem to be well understood. In contrast means by which brainstem neurons extract ITDs are still a matter of debate. Smallest detectable ITDs are temporal intervals at the order of microsec- onds - almost three orders of magnitude shorter than the duration of action po- tentials. Despite that the mammalian nervous system is capable of extracting and representing them. Even though a solution to this paradox has been pro- posed [43], mechanisms of submilisecond coding are still a subject of an ongoing research. According to the traditional view the majority of ITD sensitive neu- rons is located in the medial superior olive (MSO) (current evidence points to the existence of ITD detectors also in the LSO [49]). Those cells receive a converg- ing excitatory binaural input in corresponding frequency channels (EE neurons). Due to the narrow frequency selectivity they can be characterized in terms of IPD tuning. A prominent physiological model of ITD computation has been proposed by Jeffress [65]. He suggested that monaural neurons converge in arrays of delay lines - each corresponding to a particular ITD value. Such array would form a labelled line code or a place code where high activity of a single unit represents a specific ITD and effectively a location of a sound source. Neurons would therefore be arranged along a spatial gradient into a spatiotopic map. The Jeffreys model has dominated thinking about sound localization in mammals for a long time. Recent evidence however, points to the fact that ITDs in the mammalian audi- tory system are encoded in a different way - by the joint activity of two broadly tuned channels [88].
Outputs of many brainstem nuclei - LSO, MSO and DCN converge in the inferior colliculus (IC). Neurons in the IC are sensitive to multiple binaural cues. Interestingly many IC cells can be characterized with binaural, spectro-temporal receptive fields [112]. Identified sensitivity to the spectral-temporal composition of the binaural signal suggests that binaural hearing mechanisms expand beyond the cue extraction already in the brainstem.
Processed further by the auditory thalamus - medial genniculate nucleus (MGN), auditory information reaches the auditory cortex - the primary audi- tory field (A1). The functional role played by this structure in audition, and in spatial hearing in particular, remains a mystery. Stimulus transformations per- formed by subcortical structures can apparently account for a localization of a
point source of sound. However, lesions or silencing of neuronal activity in the auditory cortex lead to decreased sound localization performance in human and animal subjects. This apparent contradiction constitutes one of major challenges in understanding the function of this region.
In a manner similar to SOC spatial tuning of mammalian cortical neurons does not match the spatiotopic model. Tuning curves are very broad, and single neuron activity is modulated by sounds located at numerous positions surrounding the animal. They are characterized by steep slopes close to the midline area [137, 138]. Within each hemisphere, neurons seem to prefer positions close to the contralateral ear. These observations suggest that the position of a sound source could be encoded by the joint activity of two ”opponent channels”. Steep slopes at the midline would according to the theory serve the purpose of precisely encoding the position of the sound in this behaviorally important region. Another puzzling finding was that a linear function of the binaural spectrum suffices to predict with a high accuracy spatial selectivity of auditory cortical neurons [125], even though sound localization is a nonlinear operation. Taken together, the role of A1 in (spatial) audition is far from being understood [101].
The monaural ascending auditory pathway has been investigated in experi- ments guided by theoretical principles of efficient coding. It has been demon- strated that redundancy between neuronal responses to natural sounds (bird chirps) decreases between the auditory cortex and IC [26] in the cat. In this way a direct experimental evidence for the efficient coding hypothesis has been provided. Studies of auditory cortical responses have shown that cortical neurons are very sparsely active - i.e. firing rates remain below 1 Hz and less than 5% of neurons in a population are activated by a typical stimulus [57,33]. Based on these results one may risk the statement that notions of sparse and efficient cod- ing provide an appropriate theoretical framework to understand the functioning of the auditory system.