734
Analysis of temporal patterns of communication signals
Gerald S Pollack
Temporal pattern is a crucial feature of communication signals,
and neurons in the brains of many animals respond selectively
to behaviorally relevant temporal features of sensory stimuli.
Many aspects of neural function contribute to this selectivity,
including membrane biophysics, channel properties, synaptic
physiology and network structure.
Addresses
Department of Biology, McGill University, 1205 Avenue Docteur Penfield,
Montreal, Quebec, H3A 1B1, Canada;
e-mail: gerald.pollack@mcgill.ca
Figure 1
(a)
(b)
10 mV
500 ms
Current Opinion in Neurobiology 2001, 11:734–738
0959-4388/01/$ — see front matter
© 2001 Elsevier Science Ltd. All rights reserved.
Abbreviations
CF
constant frequency
EPSP excitatory postsynaptic potential
FM
frequency-modulated
GABA γ-amino butyric acid
IPSP
inhibitory postsynaptic potential
MSO
medial superior olive
Current Opinion in Neurobiology
Electrical filtering shapes synaptic potentials and contributes to
filtering. The recording shown in (a) is from a low-pass neuron. The
synaptic response is smooth and sinusoidal in shape, mirroring the
amplitude-envelope of the stimulus (bottom trace). In the neuron in (b),
synaptic potentials are brief and vary in frequency with stimulus
envelope. The neuron in (a) has spiny dendrites; the neuron in (b) lacks
spines. Reproduced with permission from [13].
Introduction
Rhythm is a crucial feature of communication signals [1].
Bird songs, frog calls, cricket chirps, and speech are all
characterized by sequences of sounds with particular
attack and decay times, durations, repetition rates, and so
on. That these temporal features can encode a signal’s content has been demonstrated in a number of animals [2–7].
Analysis of temporal pattern is thus an important aspect of
the neural processing of communication signals.
Many features of neurons, such as membrane time constants, durations of synaptic potentials, kinetics of ion
currents, and synaptic plasticity, operate over the same
tens-to-hundreds of millisecond time scales that characterize communication signals, and thus are good candidates for
mechanisms underlying the analysis of rhythm. A large
body of work has shown how these sorts of properties,
incorporated into appropriately wired networks, control the
generation of rhythmic activity [8], and it seems reasonable
to expect that the same basic mechanisms underlie rhythm
detection. In this review, I examine a few examples that
illustrate how these neuronal properties are exploited to
analyze the temporal patterns of communication signals.
Passive electrical properties
The rate at which membrane potential can change in
response to synaptic current is limited by biophysical and
morphological parameters, such as membrane resistance
and capacitance, and the dimensions and topology of the
dendritic branches through which current flows. The influence of these factors on responses to temporally patterned
input has long been appreciated [9]. The role of electrical
filtering in the analysis of communication signals has been
explored in the fish Eigenmannia. Eigenmannia produce
alternating electric fields and monitor their surroundings
by analyzing electrical distortions induced by external
objects. This sensory capability is disrupted by the superimposition of a second field (e.g. from a neighboring fish).
This elicits a ‘jamming avoidance response’, in which a
fish adjusts the frequency of its electrical discharge, so as
to increase the frequency difference between itself and its
neighbor, and thus minimize interference. A crucial sensory cue driving this behavior is the amplitude modulation
(beating) that occurs when signals that differ in frequency
are added [10]. The fish, and many neurons in the
torus semicircularis of its midbrain, respond best to beat
frequencies in the range 3–8 Hz, even though the afferents
to the torus respond strongly to much higher frequencies
as well [11]. Low-pass filtering in the torus results, in part,
from electrical filtering of synaptic currents. This is apparent in the waveforms of synaptic potentials. In neurons
that prefer low beat rates, synaptic potentials are smooth
and mirror the amplitude envelope of the stimulus (except
that they decrease in size as beat rate increases). This is as
expected if input from the afferents to this nucleus were
low-pass filtered and summed. By contrast, neurons that
are either nonselective or prefer higher beat rates exhibit
barrages of fast excitatory potentials that vary in frequency
according to stimulus amplitude, as expected for inputs
that undergo little or no low-pass filtering [12] (Figure 1).
The difference in electrical filtering characteristics of
low-pass versus all-pass or high-pass neurons has been
Analysis of temporal patterns of communication signals Pollack
735
Figure 2
(d) 15
(a)
Sum
Delay
Stimulus
Excitation
200 pulse/s
constant amplitude
Inhibition
Spikes / bin
Sensitivity to amplitude modulation in bat brains.
Summary of how excitation and inhibition interact
to shape firing patterns to constant tones, as in
the CF-FM bat Pteronotus. The traces shown in
green represent excitatory synaptic input, those
in blue are inhibitory, and the red trace
represents their sum (i.e. the variations in
membrane potential that might be expected).
The stimulus trace, shown in black, represents
the amplitude envelope of the sound stimulus.
(a) Net excitation occurs only briefly at the onset
of a constant-amplitude stimulus, because of the
longer latency of inhibitory input. Some neurons
respond at tone offset; in these cases, inhibition
arrives earliest, and excitation is unopposed only
at the end of the stimulus (not shown). (b) The
response of an ‘on’ neuron to an amplitudemodulated tone. The delay between excitation
and inhibition is such that, at the modulation rate
shown here, the two are out of phase. In (c), the
modulation rate is increased, but the delay
remains the same. Now, excitation produced by
each cycle of modulation (except the first)
overlaps with inhibition produced by the
preceding cycle, resulting in failure to respond.
From experiments described in [29]. The traces
in (d) illustrate selectivity for amplitudemodulated trains of echoes in the FM bat,
Myotis. The neuron responds only at the onset of
an unmodulated train of brief stimuli repeated at
200 Hz (top), but responds more robustly when
the stimulus train is amplitude-modulated, in this
case at 30 Hz (bottom). In this example, the
response to the modulated pulse train is about
twice that to the unmodulated train. In other
neurons, the difference may be as much as fivefold. Reproduced with permission from [31].
0
15
(b)
200 pulse/s
modulated (30 Hz)
0
0.5 s
(c)
confirmed by measuring voltage responses to injected
sinusoidal current [14]. The filter characteristics have an
intriguing morphological correlate: the dendrites of most
low-pass neurons are decorated with dendritic spines,
whereas the dendrites of all-pass and high-pass neurons
lack spines [12]. The causal relationship between spines
and filtering, however, remains unclear.
Current Opinion in Neurobiology
two stimuli, as a result of the time-dependent interactions
between a facilitating α-amino-3-hydroxy-5-methyl4-isoxazole propionic acid (AMPA)-like excitatory postsynaptic potential (EPSP), a depressing γ-amino butyric
acid (GABA)A-like inhibitory postsynaptic potential
(IPSP), and a slow GABAB-like IPSP. Networks built of
many such neurons can detect more complicated patterns,
such as sequences of dissimilar intervals.
Synaptic plasticity
Synaptic potentials may increase (facilitate) or decrease
(depress) when activated repeatedly, and short-term
depression and facilitation are obvious candidate mechanisms for the analysis of temporal pattern. In Eigenmannia,
synaptic inputs to neurons in the torus semicircularis
show frequency-dependent depression [15,16••], which
can strongly attenuate input at high modulation rates. For
many of these neurons, the combination of synaptic
depression and electrical filtering adequately accounts for
their overall selectivity to rate of amplitude modulation.
Short-term plasticity also contributes to temporal filtering
in a recently investigated simple model circuit [17•]. Here,
a two-neuron circuit is selective for the interval between
Active membrane conductances
Some torus neurons of Eigenmannia exhibit voltage-dependent conductances, distinct from those responsible for the
action potential, which amplify synaptic inputs in a
temporal-pattern-dependent manner. In other words, the
conductances in low-pass neurons selectively amplify
low-frequency input, and those in high-pass neurons
amplify high-frequency input [14]. Thus, the identities
and/or kinetics of the underlying channels (which have not
yet been characterized) appear to be matched to the filter
requirements of the neurons.
Neurons in the inferior colliculus of mammals (the
mammalian homolog of the torus semicircularis) exhibit a
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Neurobiology of behaviour
number of forms of temporal-pattern filtering including
selectivity for sound duration [18,19,20••] and rate of
amplitude modulation [21,22]. Recordings from these
neurons reveal phenemona such as post-inhibitory
rebound and oscillations [23,24•,25••], which suggest that
active conductances may play a role in temporal selectivity
here as well. In the rat, neurons in the inferior colliculus
express a variety of K+ currents that shape the temporal
dynamics of responses to current injections [25••], and
it seems likely that these participate in selectivity to
patterned sensory input as well.
Network mechanisms
Echolocation in bats
Temporal-pattern-selective networks have been studied
extensively in the brains of echolocating bats, which hunt
flying insect prey by emitting high-frequency sounds and
analyzing the returning echoes. Neurons in the bat brain are
selective for a number of temporal features (see [26] for a
recent review). I focus here on their selectivity to amplitude
modulation. Periodic modulations in echo amplitude result
from variations in the effective size of the echo source as a
flying insect flaps its wings. Modulations aid in prey identification [3], and also provide what amounts to a flashing
acoustical target that might be more easily tracked in an
environment cluttered with other reflective surfaces (e.g.
vegetation) [27]. Frequency modulations occur as well, due
to the Doppler effect [27], but these are not considered here.
Pteronotus parnelii is a so-called CF-FM bat, because its
echolocation cry consists of a long (up to tens of milliseconds)
component with constant frequency (CF; amplitude is also
relatively constant), followed by a briefer frequencymodulated (FM) portion. Neurons at several stations in the
brain, including the medial superior olive (MSO, a brainstem
nucleus) respond well to amplitude-modulated CF mimics,
but only transiently to unmodulated tones. An elegant
mechanism — proposed long ago in a theoretical paper
[28] — appears to underlie this selectivity [29]. The neurons
receive both excitatory and inhibitory inputs, but with
different latencies. Transient responses to constant-amplitude
stimuli are the result of the interplay between excitation and
inhibition (Figure 2a); blocking the latter converts these to
sustained responses [29]. Responses to amplitude-modulated stimuli depend on the phase relationship between
excitation and inhibition. This, in turn, depends on both the
latency difference between excitation and inhibition, and the
rate of modulation. At preferred modulation rates (which are
in the same range of the wingbeat frequencies of insect prey,
< 250 Hz), excitation and inhibition are in antiphase (Figure
2b), and the neuron is able to respond to each cycle of amplitude modulation. An increase in modulation rate brings the
inhibition induced by one stimulus cycle in phase with
excitation produced by a neighboring cycle, and responses
are suppressed. The model is supported by the observation
that when inhibition is blocked, the upper limit of modulation rates to which neurons respond increases, approaching
the rates encoded by the afferents to the MSO, ∼800 Hz [29].
Another bat species, Myotis lucifugus, is an FM bat; its
echolocation sounds are brief frequency sweeps that
decrease in duration (to < 2 ms) and increase in repetition
rate (to 200 Hz) as the bat approaches its prey. Many
neurons in the the auditory pathway of Myotis respond only
at the onset of a series of constant-amplitude tones delivered
at high rates (mimicking the final stages of prey capture),
but respond throughout an amplitude-modulated series
(Figure 1d). In this case, the modulation occurs not within
a single sound (each of which is briefer than the period of
an insect’s wingbeat) but across the series. Selectivity for
modulated pulse trains is apparent in the inferior colliculus
[21] and is further enhanced as processing proceeds from
the colliculus to the thalamus and cortex [30]. The underlying mechanism is not yet known in this case, although
modeling demonstrates that excitatory–inhibitory interactions could account for responses observed in the thalamus
[31]. The key factor in the Myotis model is not the phase
relationship between excitation and inhibition, but the
decay rate of synaptic responses. Neurons receive both
excitatory and delayed inhibitory inputs (as suggested by
anatomical data). At the onset of a series of constant-amplitude stimuli, a transient response occurs because of the
delay in inhibition. However, the rapid rate of stimulation
prevents both excitatory and inhibitory conductances from
inactivating between stimuli; instead, they build up to
nearly constant levels that are maintained throughout the
stimulus series, canceling each other out. When the same
stimulus train is modulated in amplitude, conductances
return towards baseline during the low-amplitude portion
of each modulation cycle, and the neuron is thus able to
respond to the increase in amplitude at the next cycle.
In both of the examples just discussed, temporal
selectivity depends on the interactions between excitatory
and inhibitory inputs. This appears to be a widespread
mechanism. Other instances include neurons selective for
particular sound durations [18,23], and for the sequence of
notes within birdsong [32].
Song recognition in frogs and insects
Amplitude modulation is also an important feature of frog
calls, where it underlies selection of mates [2]. Neurons in
the frog’s torus semicircularis are selective for amplitudemodulation rate [33] and here, too, excitation followed by
delayed inhibition has been proposed as a possible mechanism [34•]. An additional requirement of many of these
neurons, beyond a ‘correct’ rate of modulation, is that a sufficient number of modulation cycles occur at that rate,
demonstrating that integration occurs over longer time periods [35]. An upper limit on amplitude-modulation rate is set
by the need for a minimum recovery period between successive stimuli [34•], as in the Myotis model described above.
Like frogs, insects use song temporal patterns to identify
mates [4,5]. Insects offer the advantage that many neurons
can be uniquely identified and assigned clear behavioral
functions, facilitating the study of the neural basis of
Analysis of temporal patterns of communication signals Pollack
behavior (see [36] for a recent review). Little is yet known,
however, about the cellular mechanisms of temporal
pattern selectivity in insects, in part because, until recently,
these mechanisms were believed to be mainly, if not
exclusively, within the purview of small, difficult to study
neurons in the brain, rather than large neurons in lower
ganglia [37]. Nonetheless, recent work shows that two of
the largest, most accessible auditory neurons known in
insects are sensitive to stimulus temporal pattern [38–40],
opening the door to more detailed cellular studies. The
T neuron of the katydid Tettigonia viridissima responds
selectively to the paired-pulse pattern of its conspecific
song, rejecting a series of regularly occurring pulses that
resembles the song of a sympatric species [38]. In the
katydid Neoconocephalus ensiger, temporal selectivity of the
T neuron depends on whether the signal spectrum resembles that of conspecific song or of bat cries. The neuron
responds reliably to 15 kHz tones mimicking song, only up
to pulse repetition rates of about 10 Hz, but responses can
continue up to 100 Hz for ultrasound stimuli, which mimic
bat cries [39]. This difference in temporal selectivity
matches the differing temporal structures of the conspecific
song and bat cries; in song, sound-pulse rates are in the
range 5–15 Hz [39], but, as described above, bats may
produce as many as 200 sound pulses per second.
A similar situation occurs in the omega neuron of the cricket
Teleogryllus oceanicus. This neuron is dually tuned in the
frequency domain, with enhanced sensitivity to both cricketlike (4.5 kHz) and bat-like (ultrasonic) frequencies. As in
Neoconocephalus, the sound-pulse rate in cricket songs
(∼8–30 Hz) is lower than that in bat cries. Several measures of
temporal selectivity, derived using the technique of reverse
correlation [41], show that the omega neuron responds better
to high modulation rates when the sound frequency is bat-like,
than when the frequency is cricket-like [40]. Several candidate
mechanisms for temporal-pattern filtering have been revealed
in intracellular recordings from the omega neuron, including
excitation followed by delayed inhibition [42,43], differences
in the time courses of postsynaptic potentials evoked by the
two frequencies [44], and frequency-specific differences in the
afferent circuitry leading to the omega neuron [45,46].
Whether any of these play a role in pattern selectivity remains
to be determined by further studies.
Conclusions
Neurons and nervous systems are intrinsically dynamic
entities, and it is not surprising that many of their timedependent properties are enlisted in the sensory analysis
of temporal patterns. These include cell-level factors such
as membrane biophysics and channel kinetics, as well as
network-level factors such as the wiring diagrams of neural
circuits. Although it is sometimes convenient to consider
these as separate levels of neural function, it is clear that they
work in a cooperative manner, and that a comprehensive view
of temporal filtering must take both into account (c.f. [47]).
Bridging the gap between cellular and network approaches is
being facilitated by two relatively recent technical advances.
737
First, in vivo whole-cell patch clamping has made it possible
to study the currents and synaptic potentials underlying neural
responses that, previously, could only be observed as spike
trains (e.g. [14–16••,23,25••]). Second, quantitative modeling
based on realistic neurons and circuits (e.g. [17•,31]), which
demands both cellular and network-level data, provides both
tools and concepts that are required to bring these two levels
of organization together.
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