Neural coding of pitch: Temporal and place representations · Neural coding of pitch: Temporal and...

Neural Coding and Perception of Sound

Neural coding of pitch: Temporal and place representations

See Reference List Starting on Slide 40 for full bibliography

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Harvard-MIT Division of Health Sciences and Technology HST.723: Neural Coding and Perception of Sound

Outline • • •

– – –

pitch code •

HST.723 – Neural Coding and Perception of Sound

Rate and temporal codes Central auditory pathway Temporal coding of pitch

Licklider’s autocorrelation model Pitch representation in interspike intervals Neural tuning to temporal envelope as a possible

Rate-place coding of pitch

Rate vs. Temporal Codes


See Rieke et al. (1997)

Classification of Neural Codes

Temporal-place code (all

information)

Rate-place or population-rate code

Neural Population

(Map?)

Temporal codeRate codeSingle Neuron

Spike TimingSpike Counts

Neural Codes


Information in sounds occurs on different time scales

• envelope and a rapidly varying fine time structure.

•

weak consonants. • •

•


Any band-limited signal (including sounds) can be factored into the product of a slowly varying In speech, envelope information and slow envelope fluctuations (2-50 Hz) created principally by the alternation between relatively loud vowels and

Fine structure information in speech is associated with formant frequencies. Pitch information (50-500 Hz) associated with vibration of the vocal chords is present both in the fine structure for low-frequency, resolved harmonics, and in the envelope for unresolved harmonics. These distinctions are important because a neural mechanism that operates on one time scale may not work on another one.

What is temporal coding? • rate code is one that depends only on

duration. Temporal codes

•

window, and examined as a function of time.

•

•

•

code.


See Rieke et al. (1997)

For steady-state stimuli such as pure tones, a the discharge rates (spike counts) or auditory neurons averaged over the stimulus

, which depend of the temporal distribution of spikes within the stimulus, potentially contain much more information.

Most natural sounds (e.g. speech, music, vocalizations) are time-varying. For such stimuli, it makes no sense to average the firing rate over the entire stimulus duration. Rather, rate is averaged over a finite

If the averaging window is made very short (e.g. shorter than neural refractory periods), rate and temporal codes become equivalent. The distinction between rate and temporal coding amounts to a different choice of averaging window.

The window is defined in relation to the frequency content of the stimulus. A rate code is one for which the window is long relative to the periods of the frequency components of interest.

Because the time scales of the envelope and the fine structure are very different, the envelope may be coded temporally even if the fine structure is represented by a rate

Licklider’s autocorrelation model •

x) and lag (delay) τ.

autocorrelation function

period.

• implemented in neural circuits:

•

• icoincidence detector neurons.

• all-order interspike interval distribution.


See Licklider (1951)

In 1951, Licklider proposed a “duplex theory of pitch” stating that the auditory system performs a running autocorrelation analysis is performed in each frequency channel. The model’s output is a 2D function of CF (cochlear place,

• An compares a signal with a delayed version of itself:

It is used to detect periodicities in a signal because it reaches a maximum when the lag equals the

Computing an autocorrelation requires two operations, delay and multiplication, which can be

Delays can be implemented by chains of synapses (as shown) or by long, thin axons.

For signals consisting of discrete events such as spikes, mult plication can be implemented by

The autocorrelation function of a spike train is equivalent to its

Interval histograms: First-order and all-order

See McKinney (1999)


Pooled interspike interval distribution of AN

•

•

•


Single-formant vowel

Waveform (above) and autocorrelation function (top right) of single formant vowel show periodicity at 160 Hz fundamental frequency.

All-order interspike interval histograms of AN fibers arranged in order of CF often show period of single-formant vowel.

Pooled interval distributions, formed by summing interval histograms from all fibers, shows prominent representation of stimulus period.

See Cariani and Delgutte (1996)

Pitch representation in pooled all-order and first-order interspike interval distributions

• interspike intervals (left) gives more stable representation of

first-order intervals.



Pooled interval distribution constructed from all-order

F0 across stimulus levels than pooled distribution based on

Pitch coding in auditory-nerve interspike interval distributions

• : Pitch corresponds to most frequentinterspike interval in entire auditory nerve –

ANFs –

• harmonic and inharmonic complex tones: –

region •

– –

polarities at low F0s


Hypothesis

Sum (“pool”) all-order interspike interval histograms from all

Pick largest mode in pooled interval distribution Accounts for a wide variety of pitch phenomena for

Missing fundamental, pitch shift of inharmonic tones, pitch ambiguity, pitch equivalence, phase invariance, dominance

Some weaknesses: Pitch salience of pure tones underestimated Does not predict rate pitch of click trains with alternating


Pooled interspike interval distribution predicts wide range of pitch phenomena



The inferior colliculus (IC) receives projections from most brainstem auditory nuclei

• l

(lateral lemniscus.

•


Irvine (1992)

Parallel processing pathways originating in the CN converge onto the tonotopically-organized central nucleus of the IC. All these projections reach the IC via the lateralemniscus, a fiber tract. Some of the projections from CN to IC are direct monosynaptic); others are via intervening synapses in the SOC and/or the nuclei of the

The IC probably contains more neurons than all the other brainstem auditory nuclei put together, yet its function remains poorly understood.

Central nucleus of IC is tonotopically organized •

best frequencies is recorded.

•

• l


See Merzenich & Reid (1974)

A: Dorsal-to-ventral electrode penetrations were made through IC. B: After traversing the dorsal region and entering the central nucleus, a regularly-increasing sequence of

C: In parasagittal sections, iso-frequency contours are approximately horizontal, with low frequencies represented dorsally, and high frequencies ventrally.

In transverse (coronal) sections, iso-frequency contours run dorsomedial to ventrolateral, parallel to the incoming afferent fibers from the ateral lemniscus (not shown).

Phase locking to pure tones in AN fibers is limited to 5 kHz


See Javel et al. (1988) and Johnson (1980)

CN unit types differ in their phase locking to pure tones

See Rhode &

• frequency than in AN fibers.

i

•

mechanism.


Smith (1986) and Joris et al. (1994)

Phase locking in all CN unit types (except primary-like) degrades faster with increasing Choppers are particularly poor phase lockers, with an upper

limit near 1 kHz. EPSPs n choppers have long durations and require temporal summation over may AN inputs to produce a postsynaptic spike, resulting in severe lowpass filtering.

Nevertheless, bushy cells show improved phase locking over AN fibers for frequencies below 1 kHz. This temporal sharpening is consistent with a coincidence detection

Phase locking to pure tones degrades with each successive synapse in auditory pathway

• On the average

medial geniculate body.

•

√

•


See Rouiller et al. (1979)

, the upper frequency limit of phase locking in auditory neurons decreases as one ascends the auditory pathway. From 5 kHz in the AN, it falls to 1 kHz or less in the

This degradation in phase locking may be due to random temporal jitter introduced by synapses. If all synapses introduce the same jitter, then the frequency limit of phase locking should drop as 1/ N, where N is the number of synapses.

At each stage in the auditory pathway, neurons differ greatly in their phase locking abilities. Some synapses appear to be specialized to minimize jitter.

IC neurons have diverse tone-burst response patterns

•

)

• )

•


In response to tone-bursts at the CF, IC neurons show a wide variety of temporal discharge patterns: primary-like (a-b), sustained (c-d), onset(e-g), pauser/buildup (h-k), offset (l-m , on-off (n-o).

The last two response patterns (p-q were obtained for AM tones.

No universally-agreed classification scheme for IC neurons has been developed.

See Ehret and Merzenich (1988)

Pitch representation in pooled interval distributions of IC neurons is limited to low frequencies

See McKinney (2001)


Pairs of harmonic complex tones forming consonant musical intervals


Modulation frequency, depth and phase

Time (ms)


Modulation Transfer Function MTF)

•

– fm and fc are the modulation and carrier frequencies, respectively – m is the modulation depth, a number between 0 and 1 – φm is the modulation phase

• If an AM tone is used as input to a system, the modulation gain

in the input. • modulation phase lag, the difference

• by its modulation transfer function (MTF), which expresses the

fm.

( )( ) 1 (2 )m m cs t A m π φ π= + +


An sinusoidally amplitude-modulated (SAM) tone is defined by:

is the ratio of the modulation depth in the system’s output to that

Also important is the between the modulation phase of the output and that of the input. Often, this phase term is ignored in practice. A linear system’s sensitivity to modulation can be characterized

modulation gain as a function of modulation frequency

cos cos 2 f t f t

MTFs of AN fibers are lowpass • lowpass

modulation frequencies (< 10 Hz).

•

•

• fc-fm and fc+fm by the auditory filters centered at CF= fc.


See Joris & Yin (1992)

AN fibers have modulation transfer functions (MTFs) for AM tones at the CF. A small bandpass trend is sometimes seen if MTFs are measured down to very low

The upper cutoff of MTFs depends on two different phenomena:

For fibers with CFs above 2 kHz, it is a temporal limitation (such as synaptic jitter) in the neuron’s ability to phase lock to high modulation frequencies.

For CFs below 2 kHz, it is a spectral attenuation of the AM sidebands at

Some CN unit types have bandpass MTFs •

•

•

frequencies.

•

• P/B).

•


See Rhode & Greenberg (1994)

Some CN unit types (Primary-like, primary-like with notch, Onset L) have lowpass tMTFs like their AN inputs. However, the upper frequency cutoff is always lower in CN units than in the AN for neurons having the same CF.

Neuron types with lowpass MTFs tend to fire irregularly.

Other CN unit types (choppers, onset choppers and pauser/buildup) have bandpass tMTFs, meaning that they preferentially phase lock to a range of modulation

The modulation gain in the passband can be higher than that of AN fibers.

The upper cutoff in some CN cells can be much lower than in AN fibers (e.g. Cs and

Neuron types with bandpass MTFs tend to fire regularly.

Regularly-firing CN units have bandpass MTFs

See Kim et al. (1990)

• 300 Hz.

•

•

•


Left: tMTFs of 2 DCN units, one lowpass, one bandpass with a best modulation frequency (BMF) near

Center: Autocorrelation histograms in response to pure tones (unmodulated) for the same two neurons. The P/B neuron with bandpass MTFs shows pronounced oscillations in its autocorrelation, meaning that it fires regularly. The oscillation frequency is about 300 Hz, matching the BMF.

Right: For a population of neurons from CN and AN showing both intrinsic oscillations and bandpass MTFs, the BMF is highly correlated with the oscillations frequency.

Thus, bandpass MTFs in CN units can be accounted for by the tendency of these cells to fire regularly.

Discharge rates of IC neurons are tuned to modulation frequency

• A rate MTF

• bandpass

neurons are tuned to AM frequency.

• bandstop

• lowpass, as in the CN.


See Krishna & Semple (2000)

(rMTF) expresses the average discharge of a neuron as a function of modulation frequency. Unlike rMTFs of AN fibers and CN neurons, which are near flat, rMTFs in the IC often show a strong frequency dependence.

A majority of IC neurons have rMTFs, with a frequency region where the response to AM increases over the response to unmodulated tones as modulation depth increases. Such

A minority of neurons have rMTFs, with a suppression region where the response decreases with increasing modulation depth. More complex rMTFs with both enhancement and suppression regions are occasionally seen.

Temporal MTFs (tMTFs), based on strength of phase locking to AM, can be either bandpass or

Map of BMF orthogonal to frequency map in IC?

Schreiner (1988)

• units in the IC are below 150 Hz, some multi units

•

iso-frequency bands.

•

IC neurons.

• processing.

Caudal

Lateral Medial


See Langner &

While the BMFs of most single are tuned to modulation frequencies as high as 1000 Hz.

There is some evidence for a map of BMF orthogonal to the pure-tone frequency map in IC, with higher BMFs being represented in the caudal and lateral parts of

The BMF map is only apparent if data from multi units are included. Such data are hard to interpret because multi units could represent the inputs to the IC rather than

It has been suggested that tuning to modulation frequency play a role in pitch

Rostral

Coincidence-based mechanism for tuning to modulation frequency

See Hewitt & Meddis (1994)

• ii ion

frequency.

•

il

•

• i


One neural mechanisms that might account for bandpass rMTFs in the IC is coinc dence of multiple excitatory inputs which all preferent ally phase lock to the same modulat

The model has two stages:

• A first layer of neurons (here identified w th CN chopper cells, although other cell types in CN and SOC would be suitable) have bandpass tempora MTFs by integrating inputs from many AN fibers.

• A second layer of hypothetical coincidence detectors receives inputs from Layer 1 neurons having bandpass tMTFs with the same best frequency,

The model predicts some features of actual IC neurons, including the nonmonotonic level dependence of rMTFs.

The model suggests that bandpass rMTFs can be achieved w thout invoking inhibition.

Most best modulation frequencies (BMFs) in IC are below 100 Hz

• in the anesthetized gerbil.

•

Krishna & Semple (2000)


Best modulation frequencies of IC neurons with bandpass rMTFs range from 1 Hz to about 150 Hz For many neurons, the BMF can vary significantly with sound level.

Upper frequency cutoffs of phase locking to AM range from about 10 Hz to over 300 Hz, showing that IC neurons well represent temporal modulation in the roughness range.

Temporal envelope modulations in roughness range are reflected in IC responses

See McKinney & Delgutte (2001)


Consonant and Dissonant Stimuli


(See Terhardt, 1984)

Dissonance is reflected in average rates and rate fluctuations of IC Onset neuron


Two populations of cortical neurons differ in their ability to synchronize to click trains

See Lu, Liang and Wang (2001)


Auditory filters are more sharply tuned in humans than in experimental animals


No representation of fundamental frequency in AN rate-place profiles for vowels

•

•

•

(100-300 Hz).

See Sachs and Young (1979)


We have already seen that profiles of average discharge rate against CF for vowel stimuli only show a good representation of formant frequencies at low sound levels. Here, only data for high spontaneous fibers are shown.

A steady state vowel is a complex periodic tone whose fundamental frequency (here 128 Hz) determines voice pitch. Even at low sound levels, the rate-place profile shows no obvious peaks at harmonics of the fundamental frequency.

In general, data from the cat auditory nerve show few, if any, rate-place cues to pitch for harmonic complex tones with fundamental frequencies in the range of human voice

Combination-sensitive neurons in mustached bat IC

•

•

•


See Leroy & Wenstrup (1999)

The auditory system of certain bats (here the mustached bat) contain “combination-sensitive” neurons that respond preferentially to harmonically-related pairs of pure tones, even if they poorly respond to single tones.

Combination sensitive neurons were originally discovered in the auditory cortex of the mustached bat. More recently, they have also been found in the auditory thalamus and midbrain (IC) of the mustached bat and in other species as well.

In many combination-sensitive neurons, the best response occurs when the higher frequency tone is delayed relative to the low-frequency tone by several ms. For this reason, these neurons are thought to play a role in echolocation by measuring the distance between the bat and the target.

References

34:275-294.


Delgutte B (1997) Auditory neural processing of speech. In: The Handbook of Phonetic Sciences (Hardcastle WJ, Laver J, eds), pp 507-538. Oxford: Blackwell.

Ehret G, Merzenich MM (1988) Complex sound analysis (frequency resolution, filtering and spectral integration) by single units of the inferior colliculus of the cat. Brain Res Rev 13:139-163.

Frisina RD, Smith RL, Chamberlain SC (1985) Differential encoding of rapid changes in sound amplitude by second-order auditory neurons. Exp Brain Res 60:417-422.

Grothe B, Klump GM (2000) Temporal processing in sensory systems. Curr Opin Neurobiol 10:467-473.

Grothe E (1994) Interaction of excitation and inhibition in processing of pure tone and amplitude-modulated stimuli in the medial superior olive of the mustached bat. J Neurophysiology 71:706-721.

Heil P, Irvine DR (1997) First-spike timing of auditory-nerve fibers and comparison with auditory cortex. J Neurophysiol 78:2438-2454.

Hewitt MJ, Meddis R (1994) A computer model of amplitude-modulation sensitivity of single units in the inferior colliculus. J Acoust Soc Am 95:2145-2159.

Irvine D (1992) Physiology of the auditory brainstem. In: The Mammalian Auditory Pathway: Neurophysiology (Popper AN, Fay RR, eds), pp 153-231. New-York: Springer-Verlag.

Javel E, Mott J (1988) Physiological and psychophysical correlates of temporal processes in hearing. Hear Res

Johnson DH (1980) The relationship between spike rate and synchrony in responses of auditory-nerve fibers to single tones. J Acoust Soc Am 68(4):1115-1122.

Soc Am 91:215-232.

1036.

Hearing Research 45:95-113.

Acoust Soc Am 73:1694-1700.

Houtsma AJM, Kohlrausch A, Prij


Joris PX, Yin TCT (1992) Responses to amplitude-modulated tones in the auditory nerve of the cat. J Acoust

Joris PX, Carney LH, Smith PH, Yin TCT (1994) Enhancement of neural synchronization in the anteroventral cochlear nucleus. I. Responses to tones at the characteristic frequency. J Neurophysiol 71:1022-

Kiang NYS, Peake WT (1988) Physics and physiology of hearing. In: Stevens' Handbook of Experimental Psychology (Atkinson RC, Herrnstein RJ, Lindzey G, Luce RD, eds), pp 277-326. New York: Wiley.

Kim DO, Sirianni, J.G., Chang, S.O. (1990) Responses of DCN-PVCN neurons and auditory nerve fibers in unanesthetized decerebrate cats to AM and pure tones: Analysis with autocorrelation/ power spectrum.

Krishna BS, Semple MN (2000) Auditory temporal processing: responses to sinusoidally amplitude-modulated tones in the inferior colliculus. J Neurophysiol 84:255-273.

Langner G, Schreiner CE (1988) Periodicity coding in the inferior colliculus of the cat. I. Neuronal mechanisms. J Neurophysiol 60:1799-1822.

Licklider JCR (1951) A duplex theory of pitch perception. Experientia 7:128-134.

McKinney MF, Delgutte B (1999) A possible neurophysiological basis of the octave enlargement effect. J

McKinney MF, Tramo MJ, Delgutte B (2001) Neural correlates of the dissonance of musical intervals in the inferior colliculus. In: Physiological and Psychophysical Bases of Auditory Function (D.J. Breebaart,

s VF, Schoonhoven R, eds), pp 83-89. Maastricht: Shaker.

Merzenich MM, Reid MD (1974) Representation of the cochlea within the inferior colliculus of the cat. Brain Res 77:397-415.

Neurophysiology 71:1797-1825.

II


Neuert V, Pressnitzer D, Patterson RD, Winter IM (2001) The responses of single units in the inferior colliculus of the guinea pig to damped and ramped sinusoids. Hear Res 159:36-52.

Phillips DP, Semple MN, Kitzes LM (1995) Factors shaping the tone level sensitivity of single neurons in posterior field of cat auditory cortex. J Neurophysiol 73:674-686.

Portfors CV, Wenstrup JJ (1999) Delay-tuned neurons in the inferior colliculus of the mustached bat: implications for analyses of target distance. J Neurophysiol 82:1326-1338.

Rhode WS, Smith P (1986) Encoding of timing and intensity in the ventral cochlear nucleus of the cat. J Neurophysiol 56:261-286.

Rhode WS, Greenberg S (1992) Physiology of the cochlear nuclei. In: The Mammalian Auditory Pathway: Neurophysiology (Popper AN, Fay RR, eds), pp 94-152. New York: Springer-Verlag.

Rhode WS, Greenberg S (1994) Encoding of amplitude modulation in the cochlear nucleus of the cat. J

Rieke F, Warland D, de Ruyter van Steveninck R, Bialek W (1997) Spikes. Exploring the neural code. Cambridge, MA: MIT Press.

Rouiller EM, de Ribaupierre Y, de Ribaupierre F (1979) Phase-locked responses to low frequency tones in the medial geniculate body. Hearing Res 1:213-226.

Schreiner CE, Urbas JV (1986) Representation of amplitude modulation in the auditory cortex of the cat. I. The anterior auditory field (AAF). Hear Res 21:227-241.

Schreiner CE, Urbas JV (1988) Representation of amplitude modulation in the auditory cortex of the cat: Comparison between cortical fields. Hearing Res 32:49-64.

Extra slides


Key dates in study of temporal coding

• • • • • • • • • 1968: Reverse correlation technique (De Boer) •

• • • • •


1860’s: Helmholtz-Seebeck debate on pitch mechanisms 1896: Rayleigh’s duplex theory of sound localiztion 1930: Cochlear microphonic and volley principle (Wever) 1943: Phase locking in auditory neurons (Galambos & Davis) 1940’s: Schouten’s residue theory of pitch 1948: Jeffress coincidence model of binaural processing 1951: Licklider’s autocorrelation model of pitch 1960’s: Binaural coincidence in brainstem neurons (Rose et al., Goldberg & Brown)

1970: Strong superiority of timing information over rate information for frequency discrimination (Siebert) 1970’s: Neurons tuned to echo delay in echolocating bats 1979: Limitations of rate-place coding; temporal-place scheme (Sachs & Young) 1980+: Cellular specializations for temporal processing in auditory brainstem 1980’s: Tuning to envelope frequencies in auditory neurons 1990’s: Revival of autocorrelation models of pitch

•

Here, all-order interspike intervals of ANFs in response to the vowel [ae] are displayed as a function of CF.

• providing a robust representation of voice pitch.

•

F2=1400Hz, show interval modes at 1/F2 and its multiple. •

the pooled interval distribution, obtained by summing interval distributions from all the fibers in the auditory nerve.

• the periodicity and fine structure of low-frequency sounds.

AN all-order and pitch information


Steady-state vowels are harmonic complex tones having a fundamental F0 corresponding to noise pitch, and maximum energy near the formant frequencies.

For all fibers, the largest mode is at the fundamental period 1/F0 (10 ms),

In addition, fibers with CFs close to the first formant frequency F1=800Hz show modes in their interval distribution at multiples of 1/F1. Similarly, fibers tuned to

This predominance of formant and fundamental-related intervals is reflected in

These results illustrate the potential of Licklider’s autocorrelation model for coding

See Delgutte (1997)

interspike intervals contain formant

Phase locking to modulation frequency degrades at high sound levels in AN

•

• fibers.


See Joris & Yin (1992)

The ability of AN fibers to track low-frequency AM in their phase locked response first improves then severely degrades with increasing intensity. Sensitivity to AM is best in a narrow range of intensity where average discharge rate grows rapidly with level. Below and average this intensity, AM sensitivity is limited by threshold and saturation, respectively.

Degradation in AM sensitivity at high levels is worse for high spontaneous rate fibers than for low-SR

CN onset and choppers better code AM than AN fibers at high SPLs

•

not.

•

frequencies.


See Frisina et al. (1985) and Rhode & Greenberg (1992)

Left: At 60 dB above threshold, responses of CN On-L and Chpper neurons still encode a 100-Hz AM in their temporal discharge patterns, while Primary-like and Pri-N neurons (which behave like AN fibers) do

On the average, the synchronization index to 250 Hz degrades slower with increasing level for Onset and Chopper units than for both low-SR and high-SR AN fibers. Thus, CN Onset an Choppers have both higher gains and wider dynamics range than AN fibers for AM coding, but only over a restricted range of

IC has three major subdivisions •

• lightly-stained ), 2) the darkly-stained (ICP) located

(ICX) located lateral and rostral to ICC.


Nissl-stained coronal sections of the IC show a high density of cells throughout the nucleus except on the lateral side.

Immunostaining for the neuropeptide Substance P reveals three subdivisions of IC: 1) the central nucleus (ICC pericentral nucleus

dorso-medial and caudal to ICC, and 3) the external nucleus

Many IC neurons respond differentially to “damped” and “ramped” sinusoids

•

half lifes of 2-8 ms.

•

ramped sinusoid.

•

• coincidence detection mechanism.


See Neuert et al. (2001)

Psychophysically, “ramped” sinusoids evoke a more tonal percept that “damped” sinusoids, which produced more of a drumming sound. The number following D or R is the half life of the exponential envelope in ms. Psychophysical discrimination is best for

Responses of many neurons in the IC differ sharply for the two stimuli. Here, an onset neuron shows a strong response to the damped sinusoid, but little response to the

Because the envelopes of these two stimuli have the same power spectrum, the difference in response cannot be accounted for by the MTF magnitude. This illustrates the importance of the MTF phase.

The greater response to damped sinusoids than ramped sinusoids is consistent with a

Tuning to modulation frequency based on coincidence of excitation and inhibition

(1994, 2000)

•

•

•

the postsynaptic cell.

•

processing of echolocation signals.


See Grothe

The bat MSO receives excitatory inputs from the contralateral CN as well as inhibitory inputs via the lateral and medial nuclei of the trapezoid body.

Many bat MSO cells have lowpass rMTFs. This lowpass selectivity is greatly reduced by local iontophoresis of strychnine, an antagonist to the inhibitory neurotransmitter glycine. This result suggests that glycinergic inhibition plays a role in AM selectivity.

Lowpass tuning can be accounted for by a model based on coincidence of phase-locked excitation with delayed inhibition. At low frequencies, the inhibition occurs primarily during the stimulus half-cycle when there is no excitation, and has therefore little effect. However, at higher frequencies, excitation coincides with delayed inhibition from the previous cycle, causing a sharp reduction in the response of

Unlike most species, where the medial superior olive (MSO) is involved in binaural processing, the MSO of the mustached bat is primarily a monaural nucleus thought to play a role in temporal

Neurons tuned to echo delay in the bat IC • echolocate, meaning localizing targets (preys,

echo delay

•

•

echo.


Portfors and Wenstrup (1999)

Bats use high-frequency sounds to obstacles, predators) by listening to the acoustic reflections (echoes) they produce. A key measure is the , which is an indicator of the target range.

The IC of the mustached bat contains neurons that are tuned to the delay between the emitted pulse (an FM sound) and simulated reflection. While such delay-tuned neurons were originally discovered in the auditory cortex, the IC is the first site in the auditory pathway where they are found in substantial numbers.

The neural mechanism for delay tuning appears to be coincidence of a long-latency (delayed) response to the emitted pulse and a short-latency response to the echo. The long-latency response to the pulse may be formed in part by long-lasting inhibition followed by a rebound which coincides with enhanced response to the

Cortical neurons are tuned to low modulation frequencies

• their average rate and their phase locking.

• than those in the IC. The average BMF in the primary auditory cortex (A1) is 15-

field (AAF). •

prominent modulations. •

are represented is unknown.

See Schreiner & Urbas (1986, 1988)


As in the IC, most cortical neurons are tuned to modulation frequency in both

Best modulation frequencies frequencies (BMF) of cortical neurons are lower

20 Hz in anesthetized animals, and only somewhat higher in awake animals. BMFs in the other cortical fields are even lower, with the exception of the anterior

BMFs of cortical neurons are in the range where speech and music show

Listeners are sensitive to higher modulation frequencies. How these frequencies

First spike timing of cortical neurons is very precise

•

•

signals ascend the auditory pathway.


See Phillips et al. (1995) and Heil & Irvine (1997)

Despite the inability of cortical neurons to track rapid modulations, the latency of the first spike in response to stimulus onset can be very precise in these neurons: The standard deviation in first spike latency can be less than 1 ms, comparable to that of AN fibers. The precision of first spike latency suggests that the failure of cortical neurons to track rapid AM is not due to accumulation of synaptic jitter as

Neural mechanisms for temporal processing

• •

membrane channels) • • •

mechanism • Long-lasting inhibition followed by rebound may implement a

delay line. •

•

of stimulation.


Synaptic jitter and dendritic filtering: lowpass filter Specialized mechanisms to ensure precise synaptic transmission (giant synapses, rapid receptor adaptation,

Coincidence of excitatory inputs: sharpens temporal pattern Coincidence of excitation and inhibition: precise cancellation Delay lines: powerful in combination with coincidence

Oscillations produced by intrinsic membrane properties or temporal summation of many subthreshold inputs By combining the letters of this basic alphabet, it is possible to achieve precise selectivity for specific spatio-temporal patterns

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