PMID- 23663144
OWN - NLM
STAT- MEDLINE
DCOM- 20140407
LR  - 20130730
IS  - 1530-888X (Electronic)
IS  - 0899-7667 (Linking)
VI  - 25
IP  - 9
DP  - 2013 Sep
TI  - Spike-based probabilistic inference in analog graphical models using 
      interspike-interval coding.
PG  - 2303-54
LID - 10.1162/NECO_a_00477 [doi]
AB  - Temporal spike codes play a crucial role in neural information processing. In 
      particular, there is strong experimental evidence that interspike intervals 
      (ISIs) are used for stimulus representation in neural systems. However, very few 
      algorithmic principles exploit the benefits of such temporal codes for 
      probabilistic inference of stimuli or decisions. Here, we describe and rigorously 
      prove the functional properties of a spike-based processor that uses ISI 
      distributions to perform probabilistic inference. The abstract processor 
      architecture serves as a building block for more concrete, neural implementations 
      of the belief-propagation (BP) algorithm in arbitrary graphical models (e.g., 
      Bayesian networks and factor graphs). The distributed nature of graphical models 
      matches well with the architectural and functional constraints imposed by 
      biology. In our model, ISI distributions represent the BP messages exchanged 
      between factor nodes, leading to the interpretation of a single spike as a random 
      sample that follows such a distribution. We verify the abstract processor model 
      by numerical simulation in full graphs, and demonstrate that it can be applied 
      even in the presence of analog variables. As a particular example, we also show 
      results of a concrete, neural implementation of the processor, although in 
      principle our approach is more flexible and allows different neurobiological 
      interpretations. Furthermore, electrophysiological data from area LIP during 
      behavioral experiments are assessed in light of ISI coding, leading to concrete 
      testable, quantitative predictions and a more accurate description of these data 
      compared to hitherto existing models.
FAU - Steimer, Andreas
AU  - Steimer A
AD  - Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich 8057, 
      Switzerland. asteimer@ini.phys.ethz.ch
FAU - Douglas, Rodney
AU  - Douglas R
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20130510
PL  - United States
TA  - Neural Comput
JT  - Neural computation
JID - 9426182
SB  - IM
MH  - *Algorithms
MH  - Animals
MH  - Bayes Theorem
MH  - *Computer Simulation
MH  - Humans
MH  - *Models, Neurological
MH  - Neurons/*physiology
EDAT- 2013/05/15 06:00
MHDA- 2014/04/08 06:00
CRDT- 2013/05/14 06:00
PHST- 2013/05/14 06:00 [entrez]
PHST- 2013/05/15 06:00 [pubmed]
PHST- 2014/04/08 06:00 [medline]
AID - 10.1162/NECO_a_00477 [doi]
PST - ppublish
SO  - Neural Comput. 2013 Sep;25(9):2303-54. doi: 10.1162/NECO_a_00477. Epub 2013 May 
      10.