On the Upper Bound of the Information Capacity in Neuronal Synapses

Veletić, Mladen; Floor, Pål Anders; Chahibi, Youssef; Balasingham, Ilangko

doi:10.1109/tcomm.2016.2613970

Cited by 37 publications

(24 citation statements)

References 35 publications

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“…4(b) for different values of W . Since increasing W allows bigger values of λ to be used in (6), we can observe that higher λ op is obtained for bigger values of W for each M . It is also shown in Fig.…”

Section: Maximum Achievable Sum Rate Versus Metabolic Costmentioning

confidence: 87%

“…In [4], the information transfer rate for single-input single-output (SISO) system is calculated using a probabilistic model, which is extended in [5] to find the information rate in a SISO model with multiple synaptic terminals between two neurons. In [6], the upper bound of the capacity for a SISO synaptic communication is derived using Bernoulli distribution to model diffusion process.…”

Section: Introductionmentioning

confidence: 99%

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Sum Rate of MISO Neuro-Spike Communication Channel With Constant Spiking Threshold

Ramezani

Khan

Akan

2018

IEEE Trans.on Nanobioscience

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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.

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Section: Maximum Achievable Sum Rate Versus Metabolic Costmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Sum Rate of MISO Neuro-Spike Communication Channel With Constant Spiking Threshold

Ramezani

Khan

Akan

2018

IEEE Trans.on Nanobioscience

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“…They provide information-theoretical measures (e.g., transmission rate, capacity, and error rate) essential in identifying aberrance in neural encoding and decoding. Several studies addressed through Shannon's information theory quantified the ability of sensory neurons to encode dynamic stimuli [19], [25], [102]- [107], and the ability of post-synaptic neurons to receive the message from pre-synaptic neurons [26], [108]- [110]. These studies are central to monitoring information transfer over synapses and diagnosing synaptopathies reviewed in Section III, as well as ways prospective nano-devices synaptically inter-connect and interface to biological neurons.…”

Section: B Future Cognitive Brain-machine Interfacesmentioning

confidence: 99%

Synaptic Communication Engineering for Future Cognitive Brain–Machine Interfaces

Veletić

Balasingham

2019

Proc. IEEE

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Disease-affected nervous systems exhibit anatomical or physiological impairments that degrade processing, transfer, storage, and retrieval of neural information leading to physical or intellectual disabilities. Brain implants may potentially promote clinical means for detecting and treating neurological symptoms by establishing direct communication between the nervous and artificial systems. Current technology can modify neural function at the supracellular level as in Parkinson's disease, epilepsy, and depression. However, recent advances in nanotechnology, nanomaterials, and molecular communications have the potential to enable brain implants to preserve the neural function at the subcellular level which could increase effectiveness, decrease energy consumption, and make the leadless devices chargeable from outside the body or by utilizing the body's own energy sources. In this study, we focus on understanding the principles of elemental processes in synapses to enable diagnosis and treatment of brain diseases with pathological conditions using biomimetic synaptically interactive brain-machine interfaces. First, we provide an overview of the synaptic communication system, followed by an outline of brain diseases that promote dysfunction in the synaptic communication system. We then discuss technologies for brain implants and propose future directions for the design and fabrication of cognitive brain-machine interfaces. The overarching goal of this paper is to summarize the status of engineering research at the interface between technology and the nervous system and direct the ongoing research towards the point where synaptically interactive brain-machine interfaces can be embedded in the nervous system.

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“…In this communication, information is encoded in spike trains and transmitted through neural pathways by release of vesicles that contain neurotransmitters to the gap between neurons [1]. Several communication channel models are introduced for each of the biological processes involved in this communication [2]- [6] and the characteristics of these communication channels are investigated under different assumptions [3]- [8]. However, the impact of the number of available vesicles for release on its capacity is not evaluated in the existing studies [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Information Capacity of Vesicle Release in Neuro-Spike Communication

Ramezani

Akan

2018

IEEE Commun. Lett.

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Information transmission in the nervous system is performed through propagation of spikes among neurons, which is done by vesicle release to chemical synapses. Understanding the fundamentals of this communication can lead to the implementation of bio-inspired nanoscale communication paradigms. In this paper, we utilize a realistic pool-based model for vesicle release and replenishment in hippocampal pyramidal neurons and evaluate the capacity of information transmission in this process by modeling it as a binary channel with memory. Then, we derive a recurrence relation for the number of available vesicles, which is used to find successful bit transmission probabilities and mutual information between input and output. Finally, we evaluate the spiking probability that maximizes mutual information and derive the capacity of the channel.

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On the Upper Bound of the Information Capacity in Neuronal Synapses

Cited by 37 publications

References 35 publications

Sum Rate of MISO Neuro-Spike Communication Channel With Constant Spiking Threshold

Sum Rate of MISO Neuro-Spike Communication Channel With Constant Spiking Threshold

Synaptic Communication Engineering for Future Cognitive Brain–Machine Interfaces

Information Capacity of Vesicle Release in Neuro-Spike Communication

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