PMID- 18851076
OWN - NLM
STAT- MEDLINE
DCOM- 20081204
LR  - 20081014
IS  - 1539-3755 (Print)
IS  - 1539-3755 (Linking)
VI  - 78
IP  - 3 Pt 1
DP  - 2008 Sep
TI  - Temporal spike pattern learning.
PG  - 031918
AB  - Sensory systems pass information about an animal's environment to higher nervous 
      system units through sequences of action potentials. When these action potentials 
      have essentially equivalent wave forms, all information is contained in the 
      interspike intervals (ISIs) of the spike sequence. How do neural circuits 
      recognize and read these ISI sequences? We address this issue of temporal 
      sequence learning by a neuronal system utilizing spike timing dependent 
      plasticity (STDP). We present a general architecture of neural circuitry that can 
      perform the task of ISI recognition. The essential ingredients of this neural 
      circuit, which we refer to as "interspike interval recognition unit" (IRU) are 
      (i) a spike selection unit, the function of which is to selectively distribute 
      input spikes to downstream IRU circuitry; (ii) a time-delay unit that can be 
      tuned by STDP; and (iii) a detection unit, which is the output of the IRU and a 
      spike from which indicates successful ISI recognition by the IRU. We present two 
      distinct configurations for the time-delay circuit within the IRU using 
      excitatory and inhibitory synapses, respectively, to produce a delayed output 
      spike at time t_0+tau(R) in response to the input spike received at time t_0 
      . R is the tunable parameter of the time-delay circuit that controls the timing 
      of the delayed output spike. We discuss the forms of STDP rules for excitatory 
      and inhibitory synapses, respectively, that allow for modulation of R for the IRU 
      to perform its task of ISI recognition. We then present two specific 
      implementations for the IRU circuitry, derived from the general architecture that 
      can both learn the ISIs of a training sequence and then recognize the same ISI 
      sequence when it is presented on subsequent occasions.
FAU - Talathi, Sachin S
AU  - Talathi SS
AD  - J. Crayton Pruitt Family Department of Biomedical Engineering, University of 
      Florida, Florida 32611, USA. stalathi@bme.ufl.edu
FAU - Abarbanel, Henry D I
AU  - Abarbanel HD
FAU - Ditto, William L
AU  - Ditto WL
LA  - eng
GR  - 1R01EB004752/EB/NIBIB NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20080923
PL  - United States
TA  - Phys Rev E Stat Nonlin Soft Matter Phys
JT  - Physical review. E, Statistical, nonlinear, and soft matter physics
JID - 101136452
SB  - IM
MH  - *Action Potentials
MH  - Algorithms
MH  - Animals
MH  - Biophysics/*methods
MH  - Humans
MH  - *Learning
MH  - Models, Biological
MH  - Models, Neurological
MH  - Nerve Net/physiology
MH  - Neuronal Plasticity/physiology
MH  - Neurons/*metabolism
MH  - Synapses/physiology
MH  - Synaptic Transmission
MH  - Time Factors
EDAT- 2008/10/15 09:00
MHDA- 2008/12/17 09:00
CRDT- 2008/10/15 09:00
PHST- 2008/06/16 00:00 [received]
PHST- 2008/08/11 00:00 [revised]
PHST- 2008/10/15 09:00 [pubmed]
PHST- 2008/12/17 09:00 [medline]
PHST- 2008/10/15 09:00 [entrez]
AID - 10.1103/PhysRevE.78.031918 [doi]
PST - ppublish
SO  - Phys Rev E Stat Nonlin Soft Matter Phys. 2008 Sep;78(3 Pt 1):031918. doi: 
      10.1103/PhysRevE.78.031918. Epub 2008 Sep 23.