Synchronization enhances synaptic efficacy through spike timing-dependent plasticity in the olfactory system Xiaobin Lin à , Philippe De Wilde Computer Science Department, Heriot-Watt University, Edinburgh EH14 4AS, UK article info Article history: Received 2 May 2008 Received in revised form 26 June 2009 Accepted 2 August 2009 Communicated by T. Heskes Available online 19 August 2009 Keywords: Synchronization STDP Learning Synaptic efficacy Olfaction abstract Synaptic modifications are measured in biological experiments with respect to spike timings. Spike timing-dependent plasticity is the latest development in refinements of Hebbian learning. We have applied additive and multiplicative STDP synaptic learning rules to a biologically inspired olfactory network. The olfactory system recognizes odorant patterns by synchronization of mitral cells. Synchronization enhances synaptic connections between mitral cells and cortical cells. Both STDP rules exhibit unimodal weight distributions which is biologically realistic. As a result, cortical cells respond with a wider range of variability and higher firing frequency. This property has potential for the improvement of artificial odor recognition through ongoing selection of mitral cells. & 2009 Elsevier B.V. All rights reserved. 1. Introduction The brain is the center of intelligence, necessary for learning new concepts and dealing with uncertainty. Plasticity among neuronal connections in neural networks is believed to be the major mechanism that underlies intelligence [1]. Information in neural networks is encoded in the form of spike trains. Spike trains spread from one neuron to another through synapses and finally activate specific neurons in the cortex. The spatial position, spiking time and firing frequency of spike trains are studied by researchers to reveal the coding by the brain. However, it is still an open problem how information is represented in the brain using spiking trains. We experience learning new concepts through reenforcement of practice. For instance, we remember a telephone number by repeating it several times. Hebbian learning was introduced to neural networks in this scenario by Hebb in 1949. Hebb proposed that ‘‘when one cell repeatedly assists in firing another, the axon of the first cell develops synaptic knobs (or enlarges them if they already exist) in contact with the soma of the second cell’’ [1]. Hebbian theory was confirmed by experimental discoveries of long-term potentiation (LTP) in the rabbit hippocampus in 1966 [2]. LTP describes long-term enhancement of a synapse between two neurons, which are stimulated at the same time. Although there is no sufficient evidence, long-term depression (LTD) is also postulated in neural networks to balance enhancement caused by LTP. A lot of correlation learning rules inspired by Hebbian theory have proved to be successful in specific neural networks [3]. In 1998, spike timing was found to be critical to synaptic modification [4]. Synaptic changes depending on the timing of both pre- and postsynaptic spike trains are named spike timing- dependent plasticity (STDP). Feedback of postsynaptic neurons can reflect the synaptic weights locally with global knowledge of the network. There are some variations of STDP and details of differences are explained in Section 3. STDP has been discovered to facilitate synchronization in the olfactory neural networks of locusts [5,6]. On the contrary, we have explored the features of STDP in a closed network with spontaneous synchronization [7]. Synchronization is evoked by external inputs in a recurrent network with frequency-dependent synapses [8]. For invertebrate, Finelli et al. have proposed that STDP regulates and selects specific output of projection neurons to form sparsely firing of Kenyon cells in the mushroom body [6]. In the biological olfactory system, odorants are bound to their specific receptors residing in the capillary network around sensory neurons. One neuron can only expresses one kind of receptor and neurons with the same receptors are classified as the same type of sensory neurons. These sensory neurons play an important role in the transition from chemical detection to electrical signals, which initiates olfactory perceptions. The same type of sensory neurons are assembled at the same location in the olfactory bulb, named glomerulus. The number of ARTICLE IN PRESS Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/neucom Neurocomputing 0925-2312/$ - see front matter & 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.neucom.2009.08.003 à Corresponding author. E-mail address: XL61@hw.ac.uk (X. Lin). Neurocomputing 73 (2009) 381–388