Temporal spike pattern learning.
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.
Talathi
Sachin S
SS
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Florida 32611, USA. stalathi@bme.ufl.edu
Abarbanel
Henry D I
HD
Ditto
William L
WL
D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D001703:Biophysics / Q000379:methods*; D006801:Humans; D007858:Learning; D008954:Models, Biological; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000378:metabolism*; D013569:Synapses / Q000502:physiology; D009435:Synaptic Transmission; D013997:Time Factors