Given these differences, it is an open question whether optimally efficient coding is a substantial constraint on the evolution of neural circuits. Natural selection often produces local maxima, and solutions need not be optimal as long as they are good enough24,25
. In the auditory system, the narrowest information bottleneck presumably occurs in the auditory nerve. Thereafter, diverging connections in the ascending auditory pathway could mean that ever greater numbers of neurons are available to encode finite information. This would create some redundancy and reduce the necessity to make neural coding at subsequent stages optimally efficient. Nevertheless, the ‘optimality’ arguments put forward by Harper et al.21
provide a plausible, even elegant, explanation for recent experimental findings in rodents, wherein the peaks in ITD tuning curves were often found to lie outside the animal’s physiological range and tended to depend systematically on the neuron’s characteristic frequency26,27
(). These observations do not fit the classic Jeffress model, in which coincidence detectors are organized to form a place map. To form such a topographic map requires ITD detectors in each frequency band to be tuned to the full range of physiological ITDs, and their tuning should therefore not depend on characteristic frequency. The new data thus argue for a population rate code rather than a map.
Are the two models for encoding ITDs irreconcilable? Not entirely: both depend on coincidence detection and convey ITDs to the midbrain through the distribution of firing rates across the population of neurons. Barn owls seem to use the information in both the peaks and the slopes of the tuning curves28
, whereas theoretic analyses suggest that the two codes are not mutually exclusive29
. Whether peaks or slopes of the tuning curves are better suited to representing ITDs from an information theoretic perspective may depend on the factors that constrain the shape of the tuning curves21,29
. Animals may also not necessarily adopt the same optimal solution. For example, chickens and gerbils both have similar head sizes and ability to encode temporal information, and both use ITDs at relatively low frequencies (). The two species have similar head sizes and abilities to encode temporal information, and both use ITDs at relatively low frequencies. The constraints on the codes should be similar. But chickens have a place map of ITD in the nucleus laminaris25
(), fitting Jeffress’s model closely, whereas gerbils may not.
The anatomical organization of the MSO is also more complicated than required by the basic Jeffress model. MSO neurons receive excitatory inputs from each ear, but they also receive inhibition that is precisely time-locked to the incoming auditory signals30
. This inhibition is important for shaping ITD tuning curves in the MSO. Brand and colleagues27
, for example, recorded responses in gerbil MSO before and after pharmacological suppression of inhibitory inputs and noted substantial changes in the shape of the ITD tuning curves, including shifts of the peak in the curve by several hundred microseconds. Several modeling studies30–32
have since explored how the interplay of excitation and inhibition might produce these shifts in the ITD tuning curve. Joris and Yin33
have recently argued against such a central role for inhibition in ITD processing. Consideration of the data published by Brand and colleagues27
led them to conclude that inhibition in the MSO might mostly affect the early ‘onset’ but not the sustained response. This conclusion may, however, be premature. Many important sound signals (for example, footsteps) are highly transient in nature and contain little or no sustained sound, meaning that the onset is arguably the most important part of the neural response34
. Moreover, data recently published by Pecka et al.35
indicate that the effect of glycinergic inhibition on the shape of ITD tuning curves can persist during the sustained part of the response in the MSO. Thus, synaptic dynamics based on the interplay of precisely timed excitation and inhibition remain a credible additional or alternative mechanism to either axonal or cochlear delays in the inputs to the mammalian MSO ().
Perhaps we are so attached to the systematic axonal delay line arrangement in Jeffress’s model because it alone automatically leads to a topographic, ‘space-mapped’ representation of best ITDs. In the barn owl, where the evidence for the implementation of a Jeffress model in the nucleus laminaris is strongest, the resulting topographic map of ITDs is passed on and maintained in subsequent processing stations, such as the central and external nuclei of the inferior colliculus and the optic tectum, where auditory and visual information is combined to direct the animal’s direction of gaze22
. Mammals too have a topographic mapping of auditory space in the superior colliculus (the mammalian homolog of the optic tectum), but the evidence for place coding in the mammalian MSO looks increasingly weak, and there is no topographic space map in the central nucleus of the inferior colliculus. Rather, the mammalian auditory space map in the superior colliculus emerges gradually, under visual guidance36
, as the information passes from the central nucleus of the inferior colliculus by way of the nucleus of the brachium to the superior colliculus37
. Furthermore, the mammalian map in the superior colliculus is, as far as we know, based mostly on ILDs and monaural spatial cues, not ITDs38
, and no topographic arrangement of spatial tuning or ITD sensitivity has ever been found in the areas of mammalian cortex thought to be involved in the perception of sound source location. The generation of a topographic map of ITD is considered by some to be a “defining feature” of Jeffress’s model33
, and although it serves a clear function in the barn owl brain (where this topography is brought into register with a retinotopic representation of visual space), it is unclear whether such an ITD map exists in the mammalian brainstem or what purpose it would serve. Why would the mammalian auditory pathway establish a topographic representation of ITD in the MSO, only to abandon it again at the next stage of the ascending pathway?
Perhaps the strategies for encoding ITDs may be somewhat different in birds and mammals because different species combine different binaural cues in different ways. For example, barn owls have highly asymmetric external ears and can use ILDs to detect sound source elevation22
. They then combine ITD and ILD information to create neurons sharply tuned for location in both azimuth (sound source direction in the horizontal plane) and elevation (direction in the vertical plane)39
. This is very different from mammals, which typically use both ITDs and ILDs as cues to sound source azimuth but tend to rely on ITDs mostly for low frequency sound and on ILDs for high frequencies40
. Sounds only generate large ILDs when the wavelength of the sound is small compared to the head diameter, which limits the usefulness of ILDs at low frequencies. ITDs are in principle present at all frequencies, but limitations in the ability of neurons to represent and process very rapid changes in the sound (so-called phase-locking limits and phase ambiguity) make it difficult for the brain to exploit ITDs in high-frequency sound. Therefore, our judgment of the azimuthal location of a sound source is dominated by ITDs at low frequencies and by ILDs at high frequencies. However, there is a considerable ‘overlap’, where information from both cues is combined. Presumably, this cue combination occurs as ITD and ILD information streams converge, and it would be very straight forward if ITD and ILD were both encoded using a similar population rate-coding scheme41