Temporal Adaptation 
in a 
Silicon Auditory Nerve 
John Lazzaro 
CS Division 
UC Berkeley 
571 Evans Hall 
Berkeley, CA 94720 
Abstract 
Many auditory theorists consider the temporal adaptation of the 
auditory nerve a key aspect of speech coding in the auditory periph- 
ery. Experiments with models of auditory localization and pitch 
perception also suggest temporal adaptation is an important ele- 
ment of practical auditory processing. I have designed, fabricated, 
and successfully tested an analog integrated circuit that models 
many aspects of auditory nerve response, including temporal adap- 
tation. 
1. INTRODUCTION 
We are modeling known and proposed auditory structures in the brain using analog 
VLSI circuits, with the goal of making contributions both to engineering prac- 
tice and biological understanding. Computational neuroscience involves modeling 
biology at many levels of abstraction. The first silicon auditory models were con- 
structed at a fairly high level of abstraction (Lyon and Mead, 1988; Lazzaro and 
Mead, 1989ab; Mead et al., 1991; Lyon, 1991). The functional limitations of these 
silicon systems have prompted a new generation of auditory neural circuits designed 
at a lower level of abstraction (Watts et al., 1991; Liu et al., 1991). 813 
814 Lazzaro 
The silicon model of auditory nerve response models sensory transduction and spike 
generation in the auditory periphery at a high level of abstraction (Lazzaro and 
Mead, 1989c); this circuit is a component in silicon models of auditory localization, 
pitch perception, and spectral shape enhancement (Lazzaro and Mead, 1989ab; 
Lazzaro, 1991a). Among other limitations, this circuit does not model the short- 
term temporal adaptation of the auditory nerve. Many auditory theorists consider 
the temporal adaptation of the auditory nerve a key aspect of speech coding in the 
auditory periphery (Delgutte and Kiang, 1984). From the engineering perspective, 
the pitch perception and auditory localization chips perform well with sustained 
sounds as input; temporal adaptation in the silicon auditory nerve should improve 
performance for transient sounds. 
I have designed, fabricated, and tested an integrated circuit that models the tem- 
poral adaptation of spiral ganglion neurons in the auditory periphery. The circuit 
receives an analog voltage input, corresponding to the signal at an output tap of a 
silicon cochlea, and produces fixed-width, fixed-height pulses that are correlates to 
the action potentials of an auditory nerve fiber. I have also fabricated and tested 
an integrated circuit that coinbines an array of these neurons with a silicon cochlea 
(Lyon and Mead, 1988); this design is a silicon model of auditory nerve response. 
Both circuits were fabricated using the Orbit double polysilicon n-well 2/sin process. 
2. TEMPORAL ADAPTATION 
Figtare 1 shows data from the temporal adaptation circuit; the data in this figure was 
taken by connecting signals directly to the inner hair cell circuit input, bypassing 
silicon cochlea processing. In (a), we apply a I kHz pure tone burst of 20ms in 
duration to the input of the hair cell circuit (top trace), and see an adapting sequence 
of spikes as the output (middle trace). If this tone burst in repeated at 80ms 
intervals, each response in unique; by averaging the responses to 64 consecutive tone 
bursts (bottom trace), we see the envelope of the temporal adaptation superimposed 
on the cycle-by-cycle phase-locking of the spike train. These behaviors qualitatively 
match biological experiments (Kiang et al., 1965). 
In biological auditory nerve fibers, cycle-by-cycle phase locking ceases for auditory 
fibers tuned to sufficiently high frequencies, but the temporal adaptation property 
remains. In the silicon spiral ganglion neuron, a 10kHz pure tone burst fails to elicit 
phase-locking (Figure l(b), trace identities as in (a)). Temporal adaptation remains, 
however, qualitatively matching biological experiments (Kiang et al., 1965). 
To coinpare this data with the previous generation of silicon auditory nerve circuits, 
we set the control parameters of the new spiral ganglion model to eliminate temporal 
adaptation. Figure 1(c) shows the 1 kHz tone burst response (trace identities as 
in (a)). Phase locking occurs without temporal adaptation. The uneven response 
of the averaged spike outputs is due to beat frequencies between the input tone 
frequency and the output spike rate; in practice, the circuit noise of the silicon 
cochleas adds random variation to the auditory input and smooths this response 
(Lazzaro and Mead, 1989c). 
Temporal Adaptation in a Silicon Auditory Nerve 
815 
! ! 
(a) 
! , ! 
(c) 
Figure 1. Responses of test chip to pure tone bursts. Horizontal axis is time 
for all plots, all horizontal rules measure 5 ms. (a) Chip response to a 1 kHz, 
20 ms tone burst. Top trace shows tone burst input, middle trace shows a sample 
response from the chip, bottom trace shows averaged output of 64 responses to tone 
bursts. Averaged response shows both temporal adaptation and phase locking. (b) 
Chip response to a 10 kHz, 20 Ins tone burst. Trace identifications identical to (a). 
Response shows temporal adaptation without phase locking. (c) Chip response to 
a I kHz, 20 ins tone burst, with adaptation circuitry disabled. Trace identifications 
identical to (a). Response shows phase locking without temporal adaptation. 
816 Lazzaro 
3. CIRCUIT DESIGN 
Figure 2 shows a block diagram of the model. The circuits modeling inner hair cell 
transduction remain unchanged from the original model (Lazzaro and Mead, 1989c), 
and are shown as a single box. This box performs time differentiation, nonlinear 
compression and half-wave rectification on the input waveform V/, producing a 
unidirectional current waveform as output. The dependent current source represents 
this processed signal. 
The axon hillock circuit (Mead, 1989), drawn as a box marked with a pulse, con- 
verts this current signal into a series of fixed-width, fixed height spikes; Vo is the 
output of the model. The current signal is connected to the pulse generator using a 
novel current mirror circuit, that serves as the control element to regulate temporal 
adaptation. This current mirror circuit has an additional high impedance input, 
V,, that exponentially scales the current entering the axon hillock circuit (the cur- 
rent mirror operates in the subthreshold region). The adaptation capacitor Ca is 
associated with the control voltage Va. 
IHC 
<> 
\/ 
T Ca 
Figure 2. Circuit schematic of the enhanced silicon model of auditory nerve re- 
sponse. The circuit converts the analog voltage input V/ into the pulse train Vo; 
control voltages  and Vp control the temporal adaptation of state variable V, on 
capacitor Ca. See text for details. 
Temporal Adaptation in a Silicon Auditory Nerve 817 
Ca is constantly charged by the PFET transistor associated with control voltage 
, and is discharged during every pulse output of the axon hillock circuit, by an 
amount set by the control voltage Vp. During periods with no input signal, Va is 
charged to Vdd, and the current mirror is set to deliver maximum current with the 
onset of an input signal. If an input signal occurs and neuron activity begins, the 
capacitor Va is discharged with every spike, degrading the output of the current 
mirror. In this way, temporal adaptation occurs, with characteristics determined 
by Vp and . 
The nonlinear differential equations for this adaptation circuit are similar to the 
equations governing the adaptive baroreceptor circuit (Lazzaro et al., 1991); the 
publication describing this circuit includes an analysis deriving a recurrence relation 
that describes the pulse output of the circuit given a step input. 
Sp/sec 
600 
400 
200 
26 
10 
36 
17 
16 , 
14  _ 
I I I I I I 
0.0 10 20 30 
(ms) 
Figure 3. Instantaneous firing rate of the adaptive neuron, as a function of time; 
tone burst begins at 0 ms. Each curve is marked with the amplitude of presented 
tone burst, in dB. Tone burst frequency is 1Khz. 
818 Lazzaro 
4. DATA ANALYSIS 
The experiment shown in Figure l(a) was repeated for tone bursts of different 
amplitudes; this data set was used to produce several standard measures of adaptive 
response (Hewitt and Meddis, 1991). The integrated auditory nerve circuit was used 
for this set of experiments. Data was taken from an adaptive auditory nerve output 
that had a best frequency of 1 Khz.; the frequency of all tone bursts was also 1 Khz. 
Figure 3 shows the instantaneous firing rate of the auditory nerve output as a 
function of time, for tone bursts of different amplitudes. Adaptation was more 
pronounced for more intense sounds. This difference is also seen in Figure 4. In 
this figure, instantaneous firing rate is plotted as a function of amplitude, both at 
response onset and after full adaptation. 
700 
Spikes/sec 
500 
300 
100 
I I I I I I 
10 20 30 40 
dB 
Figure 4. Instantaneous firing rate of the adaptive neuron, as a function of am- 
plitude (in dB). Top curve is firing rate at onset of response, bottom curve is firing 
rate after adaptation. Tone burst frequency is 1Khz. 
Temporal Adaptation in a Silicon Auditory Nerve 819 
Figure 4 shows tha.t the instantaneous spike rate saturates at moderate intensity 
after full adaptation; at these moderate intensities, however, the onset instantaneous 
spike rate continues to encode intensity. Figure 4 shows a non-monotonicity at high 
intensities in the onset response; this undesired non-monotonicity is a result of the 
undesired saturation of the silicon cochlea circuit (Lazzaro, 1991b). 
CONCLUSION 
This circuit improves the silicon model of auditory response, by adding temporal 
adaptation. We expect this improvement to enhance existing architectures for au- 
ditory localization and pitch perception, and aid the creation of new circuits for 
speech processing. 
Acknowledgement s 
Thanks to K. Johnson of CU Boulder and J. Wawrzynek of UC Berkeley for host- 
ing this research in their laboratories. I also thank the Caltech auditory research 
community, specifically C. Mead, D. Lyon, M. Konishi, L. Watts, M. Godfrey, and 
X. Arreguit. This work was funded by the National Science Foundation. 
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