Researcher:
Khan, Tooba

Profile Picture
ORCID

Job Title

PhD Student

First Name

Tooba

Last Name

Khan

Name

Name Variants

Khan, Tooba

Email Address

Birth Date

Filters

2017 - 201972020 - 20223

Advanced Search

Filter by

Settings

Researcher Of Publications: search.filters.isAuthorOfPublication.9e12b034-7886-4cc5-95a9-059d9e65a085

Search Results

Now showing 1 - 10 of 10
  • Placeholder
    PublicationMetadata only
    Sum rate of MISO neuro-spike communication channel with constant spiking threshold
    (IEEE-Inst Electrical Electronics Engineers Inc, 2018) N/A; N/A; N/A; Department of Electrical and Electronics Engineering; Ramezani, Hamideh; Khan, Tooba; Akan, Özgür Barış; PhD Student; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 6647
    Communication among neurons, known as neuro-spike communication, is the most promising technique for realization of a bio-inspired nanoscale communication paradigm to achieve biocompatible nanonetworks. In neuro-spike communication, the information, encoded into spike trains, is communicated to various brain regions through neuronal network. An output neuron needs to receive signal from multiple input neurons to generate a spike. Hence, in this paper, we aim to quantify the information transmitted through the multiple-input single-output (MISO) neuro-spike communication channel by considering models for axonal propagation, synaptic transmission, and spike generation. Moreover, the spike generation and propagation in each neuron requires opening and closing of numerous ionic channels on the cell membrane, which consumes considerable amount of ATP molecules called metabolic energy. Thus, we evaluate how applying a constraint on available metabolic energy affects the maximum achievable mutual information of this system. To this aim, we derive a closed form equation for the sum rate of the MISO neuro-spike communication channel and analyze it under the metabolic cost constraints. Finally, we discuss the impacts of changes in number of pre-synaptic neurons on the achievable rate and quantify the tradeoff between maximum achievable sum rate and the consumed metabolic energy.
  • Placeholder
    PublicationMetadata only
    Communication theoretical modeling and analysis of tripartite synapses with astrocytes in synaptic molecular communication
    (IEEE-Inst Electrical Electronics Engineers Inc, 2022) N/A; N/A; Department of Electrical and Electronics Engineering; Khan, Tooba; Akan, Özgür Barış; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 6647
    Astrocytes, the most abundant glial cells in brain, being in physical proximity of pre- and postsynaptic terminals of the chemical synapse, form tripartite synapses. The feedback from astrocytes introduces synaptic plasticity, which modulates information transmission through neurons. Various other synaptic events also cause plasticity, hence combining them in a single model is quite challenging. In this paper, we study the combined effect of short-term depression (STD) and long-term potentiation (LTP) on vesicle release process in a tripartite synapse. STD decreases the release probability due to slower replenishment rates of releasable vesicles, whereas LTP is due to the positive feedback from astrocytes that increases release probability. Thus, we evaluate vesicle release probability and mutual information between input spikes and vesicle release to quantify the effects of STD and LTP on information transmission. Moreover, the effect of different synaptic parameters such as number of releasable vesicles, input spike rate and replenishment rate of the vesicles, is analyzed on information transmission. It is observed that release probability is predominantly affected by LTP, however, presence of STD decreases the achievable average mutual information over time. Furthermore, the synapses with higher number of releasable vesicles are observed to become stronger with time.
  • Placeholder
    PublicationMetadata only
    Analysis of information flow in miso neuro-spike communication channel with synaptic plasticity
    (Institute of Electrical and Electronics Engineers (IEEE), 2018) Ramezani H.; Muzio G.; N/A; Department of Electrical and Electronics Engineering; Khan, Tooba; Akan, Özgür Barış; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 6647
    Communication among neurons is the most promising technique for biocompatible nanonetworks. This necessitates the thorough communication theoretical analysis of information transmission among neurons. The information flow in neuro-spike communication channel is regulated by the ability of neurons to change their synaptic strengths over time, i.e. synaptic plasticity. Thus, the performance evaluation of the nervous nanonetwork is incomplete without considering the influence of synaptic plasticity. Hence, in this paper, we provide a comprehensive model for multiple-input single-output (MISO) neuro-spike communication by integrating the spike timing dependent plasticity (STDP) into existing channel model. We simulate this model for a realistic scenario with correlated inputs and varying spiking threshold. We show that plasticity is strengthening the correlated input synapses at the expense of weakening the synapses with uncorrelated inputs. Moreover, a nonlinear behavior in signal transmission is observed with changing spiking threshold.
  • Placeholder
    PublicationMetadata only
    Information theoretical analysis of synaptic communication for nanonetworks
    (Institute of Electrical and Electronics Engineers (IEEE), 2018) N/A; N/A; N/A; Department of Electrical and Electronics Engineering; Ramezani, Hamideh; Khan, Tooba; Akan, Özgür Barış; PhD Student; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 6647
    Communication among neurons is the highly evolved and efficient nanoscale communication paradigm, hence the most promising technique for biocompatible nanonetworks. This necessitates the understanding of neuro-spike communication from information theoretical perspective to reach a reference model for nanonetworks. This would also contribute towards developing ICT-based diagnostics techniques for neuro-degenerative diseases. Thus, in this paper, we focus on the fundamental building block of neuro-spike communication, i.e., signal transmission over a synapse, to evaluate its information transfer rate. We aim to analyze a realistic synaptic communication model, which for the first time, encompasses the variation in vesicle release probability with time, synaptic geometry and the re-uptake of neurotransmitters by pre-synaptic terminal. To achieve this objective, we formulate the mutual information between input and output of the synapse. Then, since this communication paradigm has memory, we evaluate the average mutual information over multiple transmissions to find its overall capacity. We derive a closed-form expression for the capacity of the synaptic communication as well as calculate the capacity-achieving input probability distribution. Finally, we find the effects of variation in different synaptic parameters on the information capacity and prove that the diffusion process does not decrease the information a neural response carries about the stimulus in real scenario.
  • Placeholder
    PublicationMetadata only
    Impact of long term plasticity on information transmission over neuronal networks
    (Institute of Electrical and Electronics Engineers (IEEE), 2020) Ramezani, Hamideh; Abbasi, Naveed A.; N/A; Department of Electrical and Electronics Engineering; Khan, Tooba; Akan, Özgür Barış; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 6647
    The realization of bio-compatible nanomachines would pave the way for developing novel diagnosis and treatment techniques for the dysfunctions of intra-body nanonetworks and revolutionize the traditional healthcare methodologies making them less invasive and more efficient. The network of these nanomachines is aimed to be used for treating neuronal diseases such as developing an implant that bridges over the injured spinal cord to regain its normal functionality. Thus, nanoscale communication paradigms are needed to be investigated to facilitate communication between nanomachines. Communication among neurons is one of the most promising nanoscale communication paradigm, which necessitates the thorough communication theoretical analysis of information transmission among neurons. The information flow in neuro-spike communication channel is regulated by the ability of neurons to change synaptic strengths over time, i.e. synaptic plasticity. Thus, the performance evaluation of the nervous nanonetwork is incomplete without considering the influence of synaptic plasticity. In this paper, we focus on information transmission among hippocampal pyramidal neurons and provide a comprehensive channel model for MISO neuro-spike communication, which includes axonal transmission, vesicle release process, synaptic communication and spike generation. In this channel, the spike timing dependent plasticity (STDP) model is used to cover both synaptic depressiofan and potentiation depending on the temporal correlation between spikes generated by input and output neurons. Since synaptic strength changes depending on different physiological factors such as spiking rate of presynaptic neurons, number of correlated presynaptic neurons and the correlation factor among them, we simulate this model with correlated inputs and analyze the evolution of synaptic weights over time. Moreover, we calculate average mutual information between input and output of the channel and find the impact of plasticity and correlation among inputs on the information transmission. The simulation results reveal the impact of different physiological factors related to either presynaptic or postsynaptic neurons on the performance of MISO neuro-spike communication. Moreover, they provide guidelines for selecting the system parameters in a bio-inspired neuronal network according to the requirements of different applications.
  • Placeholder
    PublicationMetadata only
    Nanosensor networks for smart health care
    (Elsevier, 2020) Abbasi, Naveed A.; Department of Electrical and Electronics Engineering; N/A; N/A; Department of Electrical and Electronics Engineering; Akan, Özgür Barış; Khan, Tooba; Civaş, Meltem; Çetinkaya, Oktay; Faculty Member; PhD Student; PhD Student; Other; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; 6647; N/A; N/A; N/A
    Advent of nanoscale sensors has paved the way for countless applications envisioned in the concept of a Smart City. In this chapter, we are focusing on one of the most fundamental requirements of the smart city, that is, smart health care. Great advancements in personal health care are expected with the emergence of nanosensing devices; however, single nanosensor is limited in its processing power and storage; thus we need to form network of nanosensors for any health-care application. In this chapter, we first elaborate the communication paradigms for nanosensor network. Moreover, we discuss various smart health-care applications such as smart drug delivery, body area network, implantable devices to treat injuries or malfunctions, and Internet of Nano Things. In the end, we highlight the implementation challenges for the nanosensor network for biomedical applications.
  • Placeholder
    PublicationMetadata only
    Diffusion-based model for synaptic molecular communication channel
    (IEEE-Inst Electrical Electronics Engineers Inc, 2017) N/A; N/A; N/A; Department of Electrical and Electronics Engineering; Khan, Tooba; Bilgin, Bilgesu Arif; Akan, Özgür Barış; PhD Student; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 6647
    Computational methods have been extensively used to understand the underlying dynamics of molecular communication methods employed by nature. One very effective and popular approach is to utilize a Monte Carlo simulation. Although it is very reliable, this method can have a very high computational cost, which in some cases renders the simulation impractical. Therefore, in this paper, for the special case of an excitatory synaptic molecular communication channel, we present a novel mathematical model for the diffusion and binding of neurotransmitters that takes into account the effects of synaptic geometry in 3-D space and re-absorption of neurotransmitters by the transmitting neuron. Based on this model we develop a fast deterministic algorithm, which calculates expected value of the output of this channel, namely, the amplitude of excitatory postsynaptic potential (EPSP), for given synaptic parameters. We validate our algorithm by a Monte Carlo simulation, which shows total agreement between the results of the two methods. Finally, we utilize our model to quantify the effects of variation in synaptic parameters, such as position of release site, receptor density, size of postsynaptic density, diffusion coefficient, uptake probability, and number of neurotransmitters in a vesicle, on maximum number of bound receptors that directly affect the peak amplitude of EPSP.
  • Thumbnail Image
    PublicationOpen Access
    Information theoretical analysis of synaptic communication for nanonetworks
    (Institute of Electrical and Electronics Engineers (IEEE), 2018) Department of Electrical and Electronics Engineering; Ramezani, Hamideh; Khan, Tooba; Akan, Özgür Barış; PhD Student; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering
    Communication among neurons is the highly evolved and efficient nanoscale communication paradigm, hence the most promising technique for biocompatible nanonetworks. This necessitates the understanding of neuro-spike communication from information theoretical perspective to reach a reference model for nanonetworks. This would also contribute towards developing ICT-based diagnostics techniques for neuro-degenerative diseases. Thus, in this paper, we focus on the fundamental building block of neuro-spike communication, i.e., signal transmission over a synapse, to evaluate its information transfer rate. We aim to analyze a realistic synaptic communication model, which for the first time, encompasses the variation in vesicle release probability with time, synaptic geometry and the re-uptake of neurotransmitters by pre-synaptic terminal. To achieve this objective, we formulate the mutual information between input and output of the synapse. Then, since this communication paradigm has memory, we evaluate the average mutual information over multiple transmissions to find its overall capacity. We derive a closed-form expression for the capacity of the synaptic communication as well as calculate the capacity-achieving input probability distribution. Finally, we find the effects of variation in different synaptic parameters on the information capacity and prove that the diffusion process does not decrease the information a neural response carries about the stimulus in real scenario.
  • Thumbnail Image
    PublicationOpen Access
    Fundamentals of molecular information and communication science
    (Institute of Electrical and Electronics Engineers (IEEE), 2017) Department of Electrical and Electronics Engineering; Ramezani, Hamideh; Khan, Tooba; Abbasi, Naveed Ahmed; Kuşcu, Murat; Akan, Özgür Barış; PhD Student; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering
    Molecular communication (MC) is the most promising communication paradigm for nanonetwork realization since it is a natural phenomenon observed among living entities with nanoscale components. Since MC significantly differs from classical communication systems, it mandates re-investigation of information and communication theoretical fundamentals. The closest examples of MC architectures are present inside our own body. Therefore, in this paper, we investigate the existing literature on intrabody nanonetworks and different MC paradigms to establish and introduce the fundamentals of molecular information and communication science. We highlight future research directions and open issues that need to be addressed for revealing the fundamental limits of this science. Although the scope of this development encompasses wide range of applications, we particularly emphasize its significance for life sciences by introducing potential diagnosis and treatment techniques for diseases caused by dysfunction of intrabody nanonetworks.
  • Thumbnail Image
    PublicationOpen Access
    Sum rate analysis of multiple-access neuro-spike communication channel with dynamic spiking threshold
    (Elsevier, 2019) Department of Electrical and Electronics Engineering; Akan, Özgür Barış; Khan, Tooba; Faculty Member; PhD Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; 6647; N/A
    The information from outside world is encoded into spikes by the sensory neurons. These spikes are further propagated to different brain regions through various neural pathways. In the cortical region, each neuron receives inputs from multiple neurons that change its membrane potential. If the accumulated change in the membrane potential is more than a threshold value, a spike is generated. According to various studies in neuroscience, this spiking threshold adapts with time depending on the previous spike. This causes short-term changes in the neural responses giving rise to short-term plasticity. Therefore, in this paper, we analyze a multiple-input single-output (MISO) neuro-spike communication channel and study the effects of dynamic spiking threshold on mutual information and maximum achievable sum rate of the channel. Since spike generation consumes a generous portion of the metabolic energy provided to the brain, we further put metabolic constraint in calculating the mutual information and find a trade-off between maximum achievable sum rate and metabolic energy consumed. Moreover, we analyze three types of neurons present in the cortical region, i.e., Regular spiking, Intrinsic bursting and Fast spiking neurons. We aim to characterize these neurons in terms of encoding/transmission rates and energy expenditure. It will provide a guideline for the practical implementation of bio-inspired nanonetworks as well as for the development of ICT-based diagnosis and treatment techniques for neural diseases.