The overall goal of this project is to understand the general principles underlying the processing of sensory information in the brain and in particular to explore odor coding, processing, and learning in the invertebrate olfactory system in collaboration with Gilles Laurent at Caltech. The responses of insect and mammalian olfactory systems to an odor are oscillatory and temporally structured. Recordings from single neurons in the antennal lobe (AL) and olfactory bulb show slow, complex temporal patterns of excitation and inhibition during the stimulus-evoked oscillations. The main questions that will be addressed are: (1) How is an olfactory stimulus encoded in the olfactory system? (2) How are different odor concentrations represented in the olfactory system? (3) How do the AL intrinsic dynamics optimize odor representations? (4) How is the olfactory information decoded in the brain? These questions will be addressed using computer simulations of detailed Hodgkin-Huxley type models of the locust olfactory system. Data from other insects such as honeybee and Drosophila will be used to generalize model predictions. Network models with numbers of neurons close to that in the biological systems will be simulated. The ability of the olfactory system to discriminate between odors will be explored in the models including several levels of processing (receptor cells, AL and mushroom body). The role of AL intrinsic dynamics in amplifying differences between similar odors and improving signal/noise ratio will be studied. The goal of this work will be to examine questions of cellular and network function that are very difficult to explore experimentally. The intrinsic and synaptic properties of the olfactory neurons in the model will be based on experimental data and the results of the model will be compared with recordings from the locust and other insects. The vertebrate olfactory bulb and insect AL are organized according to similar anatomical principles; the responses of neurons in the AL are similar to those in olfactory bulb. Thus, this study will lead to principles of olfactory processing that may also apply to vertebrates. Since neuronal oscillations and synchrony are observed in other sensory systems, the conclusions of this study might also provide insights into information processing in other brain areas. ? ? ?
| Moldakarimov, Samat; Bazhenov, Maxim; Sejnowski, Terrence J (2015) Feedback stabilizes propagation of synchronous spiking in cortical neural networks. Proc Natl Acad Sci U S A 112:2545-50 |
| Moldakarimov, Samat; Bazhenov, Maxim; Sejnowski, Terrence J (2014) Top-down inputs enhance orientation selectivity in neurons of the primary visual cortex during perceptual learning. PLoS Comput Biol 10:e1003770 |
| Assisi, Collins; Stopfer, Mark; Bazhenov, Maxim (2012) Excitatory local interneurons enhance tuning of sensory information. PLoS Comput Biol 8:e1002563 |
| Assisi, Collins; Stopfer, Mark; Bazhenov, Maxim (2011) Using the structure of inhibitory networks to unravel mechanisms of spatiotemporal patterning. Neuron 69:373-86 |
| Moldakarimov, Samat; Bazhenov, Maxim; Sejnowski, Terrence J (2010) Representation sharpening can explain perceptual priming. Neural Comput 22:1312-32 |
| Frohlich, Flavio; Bazhenov, Maxim; Iragui-Madoz, Vicente et al. (2008) Potassium dynamics in the epileptic cortex: new insights on an old topic. Neuroscientist 14:422-33 |
| Bazhenov, M; Rulkov, N F; Timofeev, I (2008) Effect of synaptic connectivity on long-range synchronization of fast cortical oscillations. J Neurophysiol 100:1562-75 |
| Boucetta, Sofiane; Chauvette, Sylvain; Bazhenov, Maxim et al. (2008) Focal generation of paroxysmal fast runs during electrographic seizures. Epilepsia : |
| Frohlich, Flavio; Bazhenov, Maxim (2006) Coexistence of tonic firing and bursting in cortical neurons. Phys Rev E Stat Nonlin Soft Matter Phys 74:031922 |
