Pål Anders Floor

Dimension Reducing Mappings in Joint Source-Channel Coding

In this paper we analyze dimension reducing joint source-channel coding (JSCC) systems. First we develop some theory for noise influence in M:N dimension reducing systems. Then practical examples of 2:1, 3:1, 4:1 and 3:2 systems will be given. These will be realized by parametric equations

In-Body to On-Body Ultrawideband Propagation Model Derived From Measurements in Living Animals

Ultrawideband (UWB) radio technology for wireless implants has gained significant attention. UWB enables the fabrication of faster and smaller transceivers with ultralow power consumption, which may be integrated into more sophisticated implantable biomedical sensors and actuators. Nevertheless, the large path loss suffered by UWB signals propagating through inhomogeneous layers of biological tissues is a major hindering factor. For the optimal design of implantable transceivers, the accurate characterization of the UWB radio propagation in living biological tissues is indispensable. Channel measurements in phantoms and numerical simulations with digital anatomical models provide good initial insight into the expected path loss in complex propagation media like the human body, but they often fail to capture the effects of blood circulation, respiration, and temperature gradients of a living subject. Therefore, we performed UWB channel measurements within 1-6 GHz on two living porcine subjects because of the anatomical resemblance with an average human torso. We present for the first time, a path loss model derived from these in vivo measurements, which includes the frequency-dependent attenuation. The use of multiple on-body receiving antennas to combat the high propagation losses in implant radio channels was also investigated.

Peer-to-Peer Communication in Neuronal Nano-Network

Serving as peers in the central nervous system, neurons make use of two communication paradigms, electrochemical, and molecular. Owing to their effective coordination of all the voluntary and involuntary actions of the body, an intriguing neuronal communication nominates as a potential paradigm for nano-networking. In this paper, we propose an alternative representation of the neuron-to-neuron communication process, which should offer a complementary insight into the electrochemical signals propagation. To this end, we apply communication-engineering tools and abstractions, represent information about chemical and ionic behavior with signals, and observe biological systems as input-output systems characterized by a frequency response. In particular, we inspect the neuron-to-neuron communication through the concepts of electrochemical communication, which we refer to as the intra-neuronal communication due to the pulse transmission within the cell, and molecular synaptic transmission, which we refer to as the inter-neuronal communication due to particle transmission between the cells. The inter-neuronal communication is explored by means of the transmitter, the channel, and the receiver, aiming to characterize the spiking propagation between neurons. Reported numerical results illustrate the contribution of each stage along the neuronal communication pathway, and should be useful for the design of a new communication technique for nano-networks and intrabody communications.

From Nano-Scale Neural Excitability to Long Term Synaptic Modification

Neurons within human brain make use of two communication paradigms while pursuing an objective, namely, classical electromagnetic and molecular. Physiological studies revealed that communication performance between neurons, including memory formation and learning processes, highly depends on the concentration of calcium ions, whereas the intracellular calcium concentration hinges on regulation of neurons membrane potential. Hence, the neuronal communication performance can be affected via controlled stimulation of targeted cell. In this paper we analyze the neuronal communication as potential paradigm to be applied for communication between nano-scale devices and define the stochastic spiking model, that is confined to randomness associated with neurons firing, in order to acquire and quantify the neurons response given specified stimulus. We also present synaptic transmission process and modifications related to memory formation and storage using existing simplified theoretical models on calcium dependent behavior and learning. Using modeling, theory, and findings presented in this paper, one can design the stimulus with adequate power spectral density in order to evoke desired synaptic modifications in terms of its strengthening and weakening. Similar approach provides a basement for future technologies and controlling method for nano-scale communication between peers.

On the Upper Bound of the Information Capacity in Neuronal Synapses

Neuronal communication is a biological phenomenon of the central nervous system that influences the activity of all intra-body nano-networks. The implicit biocompatibility and dimensional similarity of neurons with miniature devices make their interaction a promising communication paradigm for nano-networks. To understand the information transfer in neuronal networks, there is a need to characterize the noise sources and unreliability associated with different components of the functional apposition between two cells-the synapse. In this paper, we introduce analogies between the optical communication system and the neuronal communication system to apply results from optical Poisson channels in deriving theoretical upper bounds on the information capacity of both the bipartite and tripartite synapses. The latter refer to the anatomical and functional integration of two communicating neurons and surrounding glia cells. The efficacy of information transfer is analyzed under different synaptic setups with progressive complexity, and is shown to depend on the peak rate of the communicated spiking sequence and neurotransmitter (spontaneous) release, neurotransmitter propagation, and neurotransmitter binding. The results provided serve as a progressive step in the evaluation of the performance of neuronal nano-networks and the development of new artificial nano-networks.

Astrocyte–neuron communication as cascade of equivalent circuits

Abstract The propagation of the neural information in the cerebral cortex relies on the transfer of electrochemical impulses and diffusion of neurotransmitter molecules between neuron cells connected in a network through synaptic junctions. In this scenario, increasing interest is growing on the critical role of glia cells , in particular astrocytes , in supporting the neuronal communication. Neuroglias communicate to each other through calcium signaling and are able to sense the activity of adjacent neurons and release gliotransmitter molecules such as glutamate and D-serine , which bind on receptors located on the synaptic terminal of neurons. In other terms, astrocytes can potentially modulate the neuronal activity of adjacent neurons as well as distant neurons through calcium signaling. In this paper, we describe the neuron–astrocyte communication paradigm, first identifying the molecular processes constituting the communication and then representing each process with equivalent electronic circuits, characterized by frequency response. The aim of this work is to propose an alternative tool for the stimulus–response analysis of the astrocyte–neuron system, in particular to quantify the impact of astrocytic stimulation on the natural activity of spiking neurons. The frequency response of the equivalent circuits shows that certain stimulation patterns evoked through the astrocytes are more effective than others and have the potential of significantly alter the neuronal activity.

Delay-Free Joint Source-Channel Coding for Gaussian Network of Multiple Sensors

We study the communication problem in a sensor network which consists of multiple sensor nodes that observe memoryless Gaussian sources which are inter-correlated. The observations are transmitted over orthogonal additive white Gaussian noise channels, and all source symbols are to be recovered at the receiver. We focus on communication schemes which utilize direct source to channel mappings that operate on a symbol-by-symbol basis to ensure zero coding delay. The distortion lower bound for the network with more than two sensors case is derived. Optimal linear schemes, both distributed and cooperative, are presented. Results show that the gap to the performance upper bound is large when there is high correlation and it increases significantly when the network size is large. We then present nonlinear mappings which can be implemented distributedly and show that they can provide substantial gain when the correlation is close to one. Examples are given for networks with two and three nodes.

Communication theory aspects of synaptic transmission

Biological structures are typically based on molecular communication systems which use a myriad of molecule types to encode messages. Among the cells found in living organisms, interconnected neurons communicate by means of neurotransmitters, particles that serve as physical carriers of information. Owing to information propagation among the nano-scale components, neuronal communication is recently identified as a potential candidate for nano-networking. This paper elaborates on the concept of molecular synaptic transmission between neurons, aiming to give an insight into the performance of physical end-to-end model according to the cell physiology. The synaptic transmission is investigated from several aspects: the transmitter (pre-synaptic terminal), the channel (synaptic cleft), and the receiver (post-synaptic terminal), with a goal to characterize the propagation of the spiking rate function between neurons. Moreover, some ideas on how to incorporate the impact of astrocytic processes to the neuronal communication are presented.

Zero Delay Joint Source Channel Coding for Multivariate Gaussian Sources over Orthogonal Gaussian Channels

Abstract: Communication of a multivariate Gaussian source transmitted over orthogonaladditive white Gaussian noise channels using delay-free joint source channel codes (JSCC)is studied in this paper. Two scenarios are considered: (1) all components of the multivariateGaussian are transmitted by one encoder as a vector or several ideally collaborating nodesin a network; (2) the multivariate Gaussian is transmitted through distributed nodes in asensor network. In both scenarios, the goal is to recover all components of the multivariateGaussian at the receiver. The paper investigates a subset of JSCC consisting of directsource-to-channelmappingsthatoperateonasymbol-by-symbolbasistoensurezerocodingdelay. A theoretical analysis that helps explain and quantify distortion behavior for suchJSCC is given. Relevant performance bounds for the network are also derived withno constraints on complexity and delay. Optimal linear schemes for both scenarios arepresented. Results for Scenario 1 show that linear mappings perform well, except whencorrelation is high. In Scenario 2, linear mappings provide no gain from correlation whenthe channel signal-to-noise ratio (SNR) gets large. The gap to the performance upper boundis large for both scenarios, regardless of SNR, when the correlation is high. The maincontribution of this paper is the investigation of nonlinear mappings for both scenarios. Itis shown that nonlinear mappings can provide substantial gain compared to optimal linearschemes when correlation is high. Contrary to linear mappings for Scenario 2, carefully

On Joint Source-Channel Coding for a Multivariate Gaussian on a Gaussian MAC

In this paper, nonlinear distributed joint source-channel coding (JSCC) schemes for transmission of multivariate Gaussian sources over a Gaussian multiple access channel are proposed and analyzed. The main contribution is a zero-delay JSCC named Distributed Quantizer Linear Coder (DQLC), which performs relatively close the information theoretical bounds, improves when the correlation among the sources increases, and does not level off as the signal-to-noise ratio (SNR) becomes large. Therefore it outperforms any linear solution for sufficiently large SNR. Further an extension of DQLC to an arbitrary code length named Vector Quantizer Linear Coder (VQLC) is analyzed. The VQLC closes in on the performance upper bound as the code length increases and can potentially achieve the bound for any number of independent sources. The VQLC leaves a gap to the bound whenever the sources are correlated, however. JSCC achieving the bound for arbitrary correlation has been found for the bivariate case, but that solution is significantly outperformed by the DQLC/VQLC when there is a low delay constraint. This indicates that different approaches are needed to perform close to the bounds when the code length is high and low. The VQLC/DQLC also apply for bandwidth compression of a multivariate Gaussian transmitted on point-to-point links.