Coding of Naturalistic Stimuli by Auditory Midbrain Neurons
Authors: Christoph E. Schreiner Hagai Attias
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: It is  known  that  humans  can  make finer  discriminations  between  familiar sounds  (e.g.  syllables)  than between unfamiliar ones  (e.g.  different  noise  segments).  Here we  show  that a  corresponding en(cid:173) hancement is  present in early auditory processing stages.  Based on  previous work which demonstrated that natural sounds had robust  statistical properties that could be quantified, we hypothesize that  the auditory system exploits those properties to construct efficient  neural  codes.  To  test  this  hypothesis,  we  measure  the  informa(cid:173) tion rate carried by  auditory  spike trains on narrow-band stimuli  whose  amplitude  modulation  has  naturalistic  characteristics,  and  compare it to the information rate on stimuli with non-naturalistic  modulation.  We find  that naturalistic inputs significantly enhance  the rate of transmitted information, indicating that auditiory neu(cid:173) ral  responses  are  matched  to  characteristics  of  natural  auditory  scenes. 
1  Natural Scene Statistics and the Neural Code 
A primary goal of hearing research is to understand how complex sounds that occur  in  natural scenes  are  processed  by  the auditory  system.  However,  natural sounds  are  difficult  to  describe  quantitatively  and  the  complexity  of auditory  responses  they evoke makes it hard to gain insight into their processing.  Hence,  most studies  of auditory  physiology  are restricted to pure tones  and  noise  stimuli,  resulting  in  a  limited  understanding  of  auditory  encoding.  In  this  paper  we  pursue  a  novel  approach  to  the  study  of  natural  sound  encoding  in  auditory  spike  trains.  Our 
•  Corresponding author.  E-mail:  hagai@phy.ucsf.edu.  t  E-mail:  chris@phy.ucsf.edu . 
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H.  Attias and C.  E.  Schreiner 
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Figure  1:  Left:  amplitude modulation stimulus drawn from  a  naturalistic stimulus  set,  and the evoked  spike  train of an inferior  colliculus  neuron.  Right:  amplitude  modulation  from  a  non-naturalistic  set  and  the  evoked  spike  train  of  the  same  neuron. 
method consists of measuring statistical characteristics of natural auditory scenes,  and incorporating them into simple  stimuli  in a  systematic manner,  thus  creating  'naturalistic' stimuli  which  enable us  to study the encoding of natural sounds in a  controlled  fashion.  The  first  stage of this  program  has  been  described  in  (Attias  and Schreiner  1997); the second is  reported below.  Fig.  1 shows  two  segments  of long  stimuli  and  the  corresponding  spike  trains  of  the  same  neuron,  elicited  by  pure  tones  that  were  amplitude-modulated  by  these  stimuli.  While  both stimuli appear to be random and to have the same  mean and  both  spike  trains  have  the  same  firing  rate,  one  may  observe  that  high  and  low  amplitudes are more likely to occur in the stimulus on the left; indeed, these stimuli  are drawn from  two stimulus  sets with different  statistical properties.  Our present  study  of auditory  coding  focuses  on  assessing  the  efficiency  of  this  neural  code:  for  a  given  stimulus set,  how  well  can the animal  reconstruct the input  sound and  discriminate between similar sound segments, based on the evoked spike train, and  how  those  abilities  are  affected  by  changing  the  stimulus  statistics.  We  quantify  the discrimination capability of auditory neurons in the inferior colliculus of the cat  using concepts from  information theory  (Bialek et al.  1991;  Rieke et al.  1997). 
This  leads  to  the  issue  of  optimal  coding  (Atick  1992).  Theoretically,  given  an  auditory scene  with particular statistical properties,  it is  possible to design an en(cid:173) coding scheme that would exploit  those properties,  resulting in  a  neural code that  is  optimal for  that scene but is  consequently less efficient for  other scenes.  Here we  investigate the hypothesis that the auditory system uses a  code that is  adapted to  natural auditory scenes.  This question is addressed by comparing the discrimination  capability of auditory  neurons  between sound  segments  drawn  from  a  naturalistic  stimulus set, to the one for  a  non-naturalistic set. 
2  Statistics of Natural Sounds 
As  a  first  step in  investigating the relation between neural responses  and auditory  inputs, we studied and quantified temporal statistics of natural auditory scenes {At(cid:173) tias and Schreiner 1997}.  It is well-known that different locations on the basal mem(cid:173) brane respond selectively to different frequency  components of the incoming sound  x{t)  (e.g.,  Pickles 1988), hence the frequency  v  corresponds to a spatial coordinate,  in  analogy with  retinal location in  vision.  We  therefore analyzed a  large database  of sounds,  including speech,  music,  animal vocalizations,  and  background  sounds,  using various filter  banks comprising 0 -10kHz.  In each frequency band v, the am(cid:173) plitude a{t)  ~ 0 and phase r/>{t)  ofthe band-limited signal xv(t) =  a{t) cos{vt+r/>{t))  were extracted, and the amplitude probability distribution p(a)  and auto-correlation 
Coding of Naturalistic Stimuli by Auditory Midbrain Neurons 
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