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

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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“…4). The molecular MIMO system profits in terms of information transfer with increased synaptic redundancy [25]- [27].…”

Section: Receiving Neuron #1mentioning

confidence: 99%

Synaptic Communication Engineering for Future Cognitive Brain–Machine Interfaces

Veletić

Balasingham

2019

Proc. IEEE

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“…A sum rate analysis of multisynaptic neuronal channel has been presented in the work of Ramezani et al The authors have quantified information transmitted through a neuro‐spike channel and have also provided a framework on how metabolic energy affects information rate.…”

Section: Introductionmentioning

confidence: 99%

A leaky integrate and fire model for spike generation in a neuron with variable threshold and multiple‐input–single‐output configuration

Ganguly

Chakrabarti

2019

Trans Emerging Tel Tech

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Comprehensive understanding of the phenomenon of spike transfer from one neuron to another in human brain is essential for developing spiking neuronal network models. Synapses play pivotal role in transferring information from one neuron to another. A leaky integrate and fire model of a representative neuron in multiple‐input and single‐output configuration with threshold variability for neuronal spike transfer process is proposed in this paper. A simple analytical expression of membrane potential including threshold variability over a small time interval is developed. The model captures several important features of a spiking neuron through a minimal set of model parameters. An activity dependent noise model is also suggested. An expression for the probability of generation of an action potential at axon hillock of the post‐synaptic neuron is derived from the model. Analytical and simulation results are provided to explain various aspects of the proposed model.

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“…Three-dimensional propagation model of neurotransmitters in the synaptic cleft is proposed in [9]. Moreover, multiple-access neuronal communication channel is analyzed in [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…The generation of the spike and changes in membrane potential consume most of the energy reserved for the brain as it involves opening and closing of numerous ionic channels on the cell membrane [24]. Thus, the maximum achievable sum rate of the MISO channel is analyzed in [11] considering a realistic channel model with metabolic energy constraint. This study uses spike generation model for the postsynaptic neuron with constant spiking threshold.…”

Section: Introductionmentioning

confidence: 99%

Sum rate analysis of multiple-access neuro-spike communication channel with dynamic spiking threshold

Khan

Akan

2019

Nano Communication Networks

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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.

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

Cited by 20 publications

References 28 publications

Synaptic Communication Engineering for Future Cognitive Brain–Machine Interfaces

Synaptic Communication Engineering for Future Cognitive Brain–Machine Interfaces

A leaky integrate and fire model for spike generation in a neuron with variable threshold and multiple‐input–single‐output configuration

Sum rate analysis of multiple-access neuro-spike communication channel with dynamic spiking threshold

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