Faramarz Faghihi

Group(s): Neural Computation
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    Faghihi, F. and Kolodziejski, C. and Fiala, A. and Wörgötter, F. and Tetzlaff, C. (2013).
    An Information Theoretic Model of Information Processing in the Drosophila Olfactory System: the Role of Inhibitory Neurons for System Efficiency. Frontiers in Computational Neuroscience, 7, 183. DOI: 10.3389/fncom.2013.00183.
    BibTeX:
    @article{faghihikolodziejskifiala2013,
      author = {Faghihi, F. and Kolodziejski, C. and Fiala, A. and Wörgötter, F. and Tetzlaff, C.},
      title = {An Information Theoretic Model of Information Processing in the Drosophila Olfactory System: the Role of Inhibitory Neurons for System Efficiency},
      journal = {Frontiers in Computational Neuroscience},
      year = {2013},
      volume= {7},
      number = {183},
      url = {http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00183/full},
      doi = {10.3389/fncom.2013.00183},
      abstract = {Fruit flies Drosophila melanogaster rely on their olfactory system to process environmental information. This information has to be transmitted without system-relevant loss by the olfactory system to deeper brain areas for learning. Here we study the role of several parameters of the flys olfactory system and the environment and how they influence olfactory information transmission. We have designed an abstract model of the antennal lobe, the mushroom body and the inhibitory circuitry. Mutual information between the olfactory environment, simulated in terms of different odor concentrations, and a sub-population of intrinsic mushroom body neurons Kenyon cells was calculated to quantify the efficiency of information transmission. With this method we study, on the one hand, the effect of different connectivity rates between olfactory projection neurons and firing thresholds of Kenyon cells. On the other hand, we analyze the influence of inhibition on mutual information between environment and mushroom body. Our simulations show an expected linear relation between the connectivity rate between the antennal lobe and the mushroom body and firing threshold of the Kenyon cells to obtain maximum mutual information for both low and high odor concentrations. However, contradicting all-day experiences, high odor concentrations cause a drastic, and unrealistic, decrease in mutual information for all connectivity rates compared to low concentration. But when inhibition on the mushroom body is included, mutual information remains at high levels independent of other system parameters. This finding points to a pivotal role of inhibition in fly information processing without which the systems efficiency will be substantially reduced}}
    Abstract: Fruit flies Drosophila melanogaster rely on their olfactory system to process environmental information. This information has to be transmitted without system-relevant loss by the olfactory system to deeper brain areas for learning. Here we study the role of several parameters of the flys olfactory system and the environment and how they influence olfactory information transmission. We have designed an abstract model of the antennal lobe, the mushroom body and the inhibitory circuitry. Mutual information between the olfactory environment, simulated in terms of different odor concentrations, and a sub-population of intrinsic mushroom body neurons Kenyon cells was calculated to quantify the efficiency of information transmission. With this method we study, on the one hand, the effect of different connectivity rates between olfactory projection neurons and firing thresholds of Kenyon cells. On the other hand, we analyze the influence of inhibition on mutual information between environment and mushroom body. Our simulations show an expected linear relation between the connectivity rate between the antennal lobe and the mushroom body and firing threshold of the Kenyon cells to obtain maximum mutual information for both low and high odor concentrations. However, contradicting all-day experiences, high odor concentrations cause a drastic, and unrealistic, decrease in mutual information for all connectivity rates compared to low concentration. But when inhibition on the mushroom body is included, mutual information remains at high levels independent of other system parameters. This finding points to a pivotal role of inhibition in fly information processing without which the systems efficiency will be substantially reduced
    Review:
    Faghihi, F. and Moustafa, A. and Heinrich, R. and Wörgötter, F. (2017).
    A computational model of conditioning inspired by Drosophila olfactory system. Neural Networks, 96 - 108, 87. DOI: 10.1016/j.neunet.2016.11.002.
    BibTeX:
    @article{faghihimoustafaheinrich2017,
      author = {Faghihi, F. and Moustafa, A. and Heinrich, R. and Wörgötter, F.},
      title = {A computational model of conditioning inspired by Drosophila olfactory system},
      pages = {96 - 108},
      journal = {Neural Networks},
      year = {2017},
      volume= {87},
      url = {http://www.sciencedirect.com/science/article/pii/S0893608016301666},
      doi = {10.1016/j.neunet.2016.11.002},
      abstract = {Recent studies have demonstrated that Drosophila melanogaster (briefly Drosophila) can successfully perform higher cognitive processes including second order olfactory conditioning. Understanding the neural mechanism of this behavior can help neuroscientists to unravel the principles of information processing in complex neural systems (e.g. the human brain) and to create efficient and robust robotic systems. In this work, we have developed a biologically-inspired spiking neural network which is able to execute both first and second order conditioning. Experimental studies demonstrated that volume signaling (e.g. by the gaseous transmitter nitric oxide) contributes to memory formation in vertebrates and invertebrates including insects. Based on the existing knowledge of odor encoding in Drosophila, the role of retrograde signaling in memory function, and the integration of synaptic and non-synaptic neural signaling, a neural system is implemented as Simulated fly. Simulated fly navigates in a two-dimensional environment in which it receives odors and electric shocks as sensory stimuli. The model suggests some experimental research on retrograde signaling to investigate neural mechanisms of conditioning in insects and other animals. Moreover, it illustrates a simple strategy to implement higher cognitive capabilities in machines including robots.}}
    Abstract: Recent studies have demonstrated that Drosophila melanogaster (briefly Drosophila) can successfully perform higher cognitive processes including second order olfactory conditioning. Understanding the neural mechanism of this behavior can help neuroscientists to unravel the principles of information processing in complex neural systems (e.g. the human brain) and to create efficient and robust robotic systems. In this work, we have developed a biologically-inspired spiking neural network which is able to execute both first and second order conditioning. Experimental studies demonstrated that volume signaling (e.g. by the gaseous transmitter nitric oxide) contributes to memory formation in vertebrates and invertebrates including insects. Based on the existing knowledge of odor encoding in Drosophila, the role of retrograde signaling in memory function, and the integration of synaptic and non-synaptic neural signaling, a neural system is implemented as Simulated fly. Simulated fly navigates in a two-dimensional environment in which it receives odors and electric shocks as sensory stimuli. The model suggests some experimental research on retrograde signaling to investigate neural mechanisms of conditioning in insects and other animals. Moreover, it illustrates a simple strategy to implement higher cognitive capabilities in machines including robots.
    Review:
    Gupta, A. and Faghihi, F. and Moustafa, A. (2017).
    Computational Models of Olfaction in Fruit Flies. Computational Models of Brain and Behavior, 199--213. DOI: 10.1002/9781119159193.ch15.
    BibTeX:
    @inbook{guptafaghihimoustafa2017,
      author = {Gupta, A. and Faghihi, F. and Moustafa, A.},
      title = {Computational Models of Olfaction in Fruit Flies},
      pages = {199--213},
      booktitle = {Computational Models of Brain and Behavior},
      year = {2017},
      publisher = {John Wiley & Sons, Ltd},
      url = {http://dx.doi.org/10.1002/9781119159193.ch15},
      doi = {10.1002/9781119159193.ch15},
      abstract = {Fruit flies (Drosophila melanogaster) rely on their olfactory system to process environmental information. Although an extensive number of studies have revealed the basic molecular and cellular mechanisms underlying information processing as well as neural circuits of learning in the Drosophilas olfactory system, there are still many questions that are awaiting an answer. Specifically, linking behavior to underlying molecular mechanisms and neural circuitry are some of the challenges of modern neuroscience. In this review, we present some models which are based on available data of the Drosophila olfactory system to describe the role of physiological as well as structural parameters in information processing in the Drosophila olfactory system.}}
    Abstract: Fruit flies (Drosophila melanogaster) rely on their olfactory system to process environmental information. Although an extensive number of studies have revealed the basic molecular and cellular mechanisms underlying information processing as well as neural circuits of learning in the Drosophilas olfactory system, there are still many questions that are awaiting an answer. Specifically, linking behavior to underlying molecular mechanisms and neural circuitry are some of the challenges of modern neuroscience. In this review, we present some models which are based on available data of the Drosophila olfactory system to describe the role of physiological as well as structural parameters in information processing in the Drosophila olfactory system.
    Review:
    Faghihi, F. and Moustafa, A. (2017).
    Sparse and burst spiking in artificial neural networks inspired by synaptic retrograde signaling. Information Sciences, 30 - 42, 421, Supplement C. DOI: https://doi.org/10.1016/j.ins.2017.08.073.
    BibTeX:
    @article{faghihimoustafa2017,
      author = {Faghihi, F. and Moustafa, A.},
      title = {Sparse and burst spiking in artificial neural networks inspired by synaptic retrograde signaling},
      pages = {30 - 42},
      journal = {Information Sciences},
      year = {2017},
      volume= {421},
      number = {Supplement C},
      url = {http://www.sciencedirect.com/science/article/pii/S0020025517303833},
      doi = {https://doi.org/10.1016/j.ins.2017.08.073},
      abstract = {Abstract The bursting of action potential and sparse activity are ubiquitously observed in the brain. Although the functions of these activity modes remain to be understood, it is expected that they play a critical role in information processing. In addition, the functional role of retrograde signalling in neural systems is under intensive research. Therefore, we propose a bio-inspired neural network that is capable of demonstrating these activity modes as well as shifting themselves from normal to bursting or sparse modes by changing model parameter values. Accordingly, we model diffused retrograde signalling with different activity patterns in dendrites and presynaptic neurons. Using in a three-layered spiking neural network, simulation studies are conducted using different conditions and parameter values to find factors underlying the change in firing rate of output neurons. Our findings propose the application of retrograde signalling as a known synaptic mechanism for the development of artificial neural systems to encode environmental information by different spiking modes.}}
    Abstract: Abstract The bursting of action potential and sparse activity are ubiquitously observed in the brain. Although the functions of these activity modes remain to be understood, it is expected that they play a critical role in information processing. In addition, the functional role of retrograde signalling in neural systems is under intensive research. Therefore, we propose a bio-inspired neural network that is capable of demonstrating these activity modes as well as shifting themselves from normal to bursting or sparse modes by changing model parameter values. Accordingly, we model diffused retrograde signalling with different activity patterns in dendrites and presynaptic neurons. Using in a three-layered spiking neural network, simulation studies are conducted using different conditions and parameter values to find factors underlying the change in firing rate of output neurons. Our findings propose the application of retrograde signalling as a known synaptic mechanism for the development of artificial neural systems to encode environmental information by different spiking modes.
    Review:

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