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Manual Page - gennap(1)


Manual Reference Pages  - GENNAP (1)

NAME

gennap - generate neural activity pattern


CONTENTS

Synopsis
Description
Options
Neural Transduction
Examples
References
Files
See Also
Copyright
Acknowledgements

SYNOPSIS

gennap [ option=value | -option ] [ filename ]

DESCRIPTION

The gennap module of the AIM software converts an input wave into a
neural activity pattern (NAP), which is AIM’s representation of the
firing pattern in the auditory nerve at about the level of the
cochlear nucleus. Gennap begins by calculating the basilar membrane
motion (BMM) associated with the input wave using the genbmm module,
and then it applies several additional transforms that we know occur
in some form during the neural transduction process. AIM provides two
alternative methods for generating the NAP, a two-dimensional adaptive
thresholding mechanism (Holdsworth and Patterson, 1993), and an array
of inner haircell simulators based on (Meddis et al., 1990; Giguere
and Woodland, 1994). In the functional version of AIM, the adaptive
thresholding mechanism applies rectification, log compression,
adaptation in time, and suppression across frequency; its purpose is
to stabilise the level of the membrane activity with compression and
then sharpen the features that appear in the compressed membrane
motion. Together, the gammatone filterbank and adaptive thresholding
form a ’functional’ cochlea simulation. In the physiological version
of AIM, the transmission-line filterbank applies level-dependant
compression and the Meddis haircell bank applies compression and
adaptation. The haircells are not coupled and so there is no
frequency sharpening in this module. Together, the transmission-line
filterbank and the Meddis module form a ’physiological’ cochlea
simulation.

OPTIONS

The options for gennap are grouped according to the functions they
control. The adaptive thresholding options are identified by the
common suffix _at; the Meddis module options are identified by the
common suffix _med. These two groups of options are the subject of
this manual entry, together with two additional options that specify
whether rectification and compression are required before
transduction. There is also an option to specify the choice of
transduction module.

I. RECTIFICATION AND COMPRESSION

The adaptive thresholding process in the functional version of AIM
begins with rectification and compression of the BMM. The default
form of compression is logarithmic; it has the advantage of
transforming the exponential envelope of the ringing response of the
gammatone filter into a linear decay with time. There is evidence,
however, that auditory compression may be better represented by power
compression with an exponent in the range of 0.5. It is also
advisable in some cases to insert power compression before the Meddis
haircell when driving it with a gammatone filter. For a discussion of
these issues, see docs/aimMeddisHewitt. To accommodate the assembly
of different configurations of AIM, the rectification and compression
options are presented separately in the options list before the neural
transduction section.

rectify Apply half-wave rectification to filtered waves

Switch. Default value: off.

If rectify is on, the BMM is half-wave rectified. The log compressor
also performs half-wave rectification to avoid negative logs. Since
the compressor default is ’log’, the rectify default is ’off’.

Note: Full wave rectification is produced if rectify is set to 2.
This is useful when calculating envelopes with genasa.

compress Apply compression to filtered waves. The form of the compression can
be either logarithmic (log), or a power function (with a value between
0 and 1).

Switch: Choices log, 0-1, off. Default value: log

NOTE: When using the physiological version of AIM with the
transmission-line filterbank and the Meddis haircell bank, set
compress=off, as compression is an integral part of the feedback loop
in the transmission-line filterbank module.

Neural Transduction

The neural transduction is performed either by two-dimensional
adaptive thresholding or an array of Meddis haircells. The choice is
controlled by the option ’transduction’.

transduction The transduction function

Switch. Choices: at, med, off. Default value: at

If adaptive thresholding is specified (at), the options with suffix
_at below apply; if the Meddis module is specified (med), the options
with suffix _med below apply. If off is specified, no transduction
function is applied. The default is at.

    II TWO-DIMENSIONAL ADAPTIVE THRESHOLDING: _at

The adaptive thresholding mechanism is a functional model of neural
encoding (Holdsworth, 1990; Patterson and Holdsworth, 1996). Its
purpose is to enhance the contrast of the larger features that appear
in the surface of the BMM and reduce those aspects of the
representation which are just a direct consequence of the filtering
and compression processes. The process begins with rectification and
compression of the BMM. The tail of the envelope of the impulse
response of the gammatone filter is exponential. As a result,
logarithmic compression is used, since this makes the filter decay
function linear in NAP coordinates. Following compression, adaptation
is applied in time and suppression is applied across frequency
(Holdsworth and Patterson, 1993, Patterson, 1994a).

Briefly, an adaptive threshold value is maintained for each channel
and updated at the sampling rate. The new value is the largest of a)
the previous value reduced by a fast-acting temporal decay factor
(t1recovery_at), b) the previous value reduced by a longer-term
temporal decay factor (t2recovery_at), c) the adapted level in the
channel immediately above, reduced by a frequency spread factor
(frecovery_at), d) the adapted level in the channel immediately below,
reduced by the same frequency spread factor, or e) a floor level that
precludes the mechanism listening to its own internal noise
(reclimit_at). The mechanism produces output whenever the input
exceeds the adaptive threshold, and the output level is the difference
between the input and the adaptive threshold. The adaptation and
suppression are coupled, and they jointly sharpen features like vowel
formants which appear smeared in compressed BMM.

trise_at Threshold rise rate

Default value: 10000.

Upward Adaptation: This option specifies the rate at which the
adaptive threshold will rise in response to a rise in signal
level. The default value, 10000, means that the adaptive threshold
responds very quickly to increases in the input wave; essentially, it
follows the envelope of any rise in signal amplitude.

Downward Adaptation: Following the cessation of sound, or a rapid drop
in input level, temporal adaptation occurs in two stages as determined
by t1recovery_at, t2recovery_at and propt2t1_at: If the default values
are used, the mechanism initially adapts at a rate slightly slower
than the decay rate of the gammatone filter in the given channel, and
this represses much of the ringing of the impulse response of the
filter. Later the adaptation switches to a slower rate more in line
with data on auditory forward masking. The option propt2t1_at
determines the point at which the initial fast rate of decay gives way
to the slower limiting decay rate.

t1recovery_at The initial rate of threshold recovery relative to filter decay rate

Default value: 0.6.

This option determines the initial rate of decay of the adaptive
threshold relative to the rate of decay of the auditory filter,
provided propt2t1_at is less than unity. Values of t1recovery_at less
than unity cause the adaptive threshold to decay more slowly than the
auditory filter and thereby to remove the filter response from the
representation when it is the sole reason for BMM activity. The rate
of decay is linear with respect to the log-compressed BMM, so it is
like an exponential decay with respect to the BMM.

t2recovery_at The secondary threshold recovery rate

Default value: 0.2.

This option determines the limiting rate of decay of the adaptive
threshold when the sound ceases provided propt2t1_at is less than
unity. The default value causes the adaptive threshold to decay more
slowly than the initial rate as observed in auditory forward masking.
Note, however, that the system to this point is level independent,
whereas auditory forward masking is level dependent.

propt2t1_at The point at which t1recovery_at gives way to t2_recovery_at

Default value: 0.5.

This option determines the point at which the initial fast rate of
decay (t1recovery_at) gives way to the slower limiting decay rate
(t2recovery_at). The nomanclature assumes that propt2t1_at is a value
less than unity. Otherwise the the roles of the initial and limiting
decays are reversed.

frecovery_at Recovery rate across frequency

Default value: 20.

This parameter specifies the rate at which a threshold value in one channel
propagates to influence threshold in neighbouring channels.

reclimit_at Limitation on recovery level

Default units: mB. Default value: 500 mB. (mB=milliBells)

In order to prevent the mechanism from encountering system noise,
or alternately, to reduce sensitivity to stimulus noise, there is a
limit placed on the recovery that the adaptive threshold can achieve.
The limit, reclimit_at, is the limit of the sensitivity of the system.

gain_at Output gain

Default units: scalar. Default value: 1.

    III MEDDIS HAIRCELL TRANSDUCTION: _med

The purpose of the Meddis module is to simulate neural transduction of
BMM as performed by the inner haircells of the cochlea (Meddis, 1986,
1988). There is one haircell simulation unit for each output channel
of the filterbank. The haircell equations (Meddis et al., 1990) are
solved using the wave digital filter algorithm described in Giguere
and Woodland (1994). The characteristics of the haircell are
controlled by options: fiber_med, thresh_med, and gain_med.

fiber_med The spontaneous-rate of the simulated fiber

Default value: medium. Choices: medium, high.

If medium is specified, a medium spontaneous-rate haircell fiber is
simulated. If high is specified, a high spontaneous-rate
fiber is simulated. The properties of these two types of fibers
are listed in Table II in Meddis et al. (1990).
The default value is medium.

thresh_med The threshold shift of the fiber

Default Units: dB. Default value: 0.

This option shifts the entire rate-intensity function of the haircell
fiber horizontally to a higher or lower level, to accomodate changes
in the scaling of the input wave. A positive (negative) value
increases (decreases) the rate- and saturation-thresholds of the fiber
by that amount. This operation does not change the dynamic range, the
spontaneous and saturation rates, or the adaptation time constants or
synchronization index of the fiber.

gain_med Output gain

Default units: scalar. Default value: 1.

Note: There is an internal gain of 20.0 within the software of
the Meddis haircell model itself. The total gain is therefore
20.0 times the value for gain_med.

EXAMPLES

The following command generates the neural activity pattern using the
gammatone auditory filterbank (the default) and adaptive
thresholding (the default) for an input file named cegc:

> gennap cegc

The following command generates the neural activity pattern using the
transmission line filterbank and Meddis haircell transduction for cegc:

> gennap filter=tlf compress=off transduction=meddis cegc

The following command generates the neural activity pattern using the
gammatone filterbank and Meddis haircell
transduction for input cegc:

> gennap compress=off transduction=meddis cegc

NOTE: docs/aimMeddisHewitt shows how to produce a Meddis and Hewitt
(1991) model of pitch perception using the AIM software package, and
how to insert power compression between the gammatone filterbank and
the Meddis haircell bank.

REFERENCES

Giguere, C. and Woodland, P.C. (1994).
"A computational model of
the auditory periphery for speech and hearing research," I. Ascending
path. J.Acoust. Soc. Am. 95: 331-342.
Holdsworth, J. (1990).
"Two dimensional adaptive thresholding."
Annex 4 of AAM-HAP Report 1, APU contract Report.
Holdsworth, J. and Patterson, R.D. (1993).
Analysis of waveforms. UK Patent GB 2234078B.
Meddis, R. (1986).
"Simulation of mechanical to neural transduction in
the auditory receptor," J. Acoust. Soc. Am. 79, 702-711.
Meddis, R. (1988).
"Simulation of auditory neural transduction: Further studies,"
J. Acoust. Soc. Am. 83, 1056-1063.
Meddis, R., Hewitt, M. and Shackleton, T. (1990).
"Implementation details of a computational model of the
inner-haircell/auditory-nerve synapse,"
J.Acoust. Soc. Am. 87: 1813-1816.
Patterson, R.D. and Holdsworth, J. (1996).
"A functional model of neural activity patterns and auditory images,"
In: Advances in Speech, Hearing and Language Processing Vol. 3,
W.A. Ainsworth (ed.), JAI Press, Greenwich, Connecticut, 551-567. (in
press since 1991)
Patterson, R.D. (1994a).
"The sound of a sinusoid: Spectral models,"
J. Acoust. Soc. Am. 96, 1409-1418.

FILES

.gennaprc The options file for gennap.

SEE ALSO

genepn, gencgm, genbmm

COPYRIGHT

Copyright (c) Applied Psychology Unit, Medical Research Council, 1995

Permission to use, copy, modify, and distribute this software without fee
is hereby granted for research purposes, provided that this copyright
notice appears in all copies and in all supporting documentation, and that
the software is not redistributed for any fee (except for a nominal
shipping charge). Anyone wanting to incorporate all or part of this
software in a commercial product must obtain a license from the Medical
Research Council.

The MRC makes no representations about the suitability of this
software for any purpose. It is provided "as is" without express or
implied warranty.

THE MRC DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING
ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL
THE A.P.U. BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES
OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION,
ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS
SOFTWARE.

ACKNOWLEDGEMENTS

The AIM software was developed for Unix workstations by John
Holdsworth and Mike Allerhand of the MRC APU, under the direction of
Roy Patterson. The physiological version of AIM was developed by
Christian Giguere. The options handler is by Paul Manson. The revised
SAI module is by Jay Datta. Michael Akeroyd extended the postscript
facilites and developed the xreview routine for auditory image
cartoons.

The project was supported by the MRC and grants from the U.K. Defense
Research Agency, Farnborough (Research Contract 2239); the EEC Esprit
BR Porgramme, Project ACTS (3207); and the U.K. Hearing Research Trust.


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