A Logic Gate Model based on Neuronal Molecular Communication Engineering

The field of Neuroengineering aims to investigate ways to proposed synthetic and controllable Boolean computing inside the brain using neuronal cells based on the existing neuronal computation abilities of the Brain. In this work, we propose the design of AND and OR logic gates using a multicellular Boolean logic operation by engineering the molecular communications of neurons and we evaluate their performance when passing data along as isolated units. The results show higher accuracy values of gate operation for mid-level inter-spike intervals when stimulated with spike trains revealing the role of the frequency of firing and how this impacts on neuronal logic gatin

Broadband Macroscopic Cortical Oscillations Emerge from Intrinsic Neuronal Response Failures

Broadband spontaneous macroscopic neural oscillations are rhythmic cortical firing which were extensively examined during the last century, however, their possible origination is still controversial. In this work we show how macroscopic oscillations emerge in solely excitatory random networks and without topological constraints. We experimentally and theoretically show that these oscillations stem from the counterintuitive underlying mechanism - the intrinsic stochastic neuronal response failures. These neuronal response failures, which are characterized by short-term memory, lead to cooperation among neurons, resulting in sub- or several- Hertz macroscopic oscillations which coexist with high frequency gamma oscillations. A quantitative interplay between the statistical network properties and the emerging oscillations is supported by simulations of large networks based on single-neuron in-vitro experiments and a Langevin equation describing the network dynamics. Results call for the examination of these oscillations in the presence of inhibition and external drives.Comment: 21 pages, 5 figure

Computational paradigm for dynamic logic-gates in neuronal activity

In 1943 McCulloch and Pitts suggested that the brain is composed of reliable logic-gates similar to the logic at the core of today's computers. This framework had a limited impact on neuroscience, since neurons exhibit far richer dynamics. Here we propose a new experimentally corroborated paradigm in which the truth tables of the brain's logic-gates are time dependent, i.e. dynamic logicgates (DLGs). The truth tables of the DLGs depend on the history of their activity and the stimulation frequencies of their input neurons. Our experimental results are based on a procedure where conditioned stimulations were enforced on circuits of neurons embedded within a large-scale network of cortical cells in-vitro. We demonstrate that the underlying biological mechanism is the unavoidable increase of neuronal response latencies to ongoing stimulations, which imposes a nonuniform gradual stretching of network delays. The limited experimental results are confirmed and extended by simulations and theoretical arguments based on identical neurons with a fixed increase of the neuronal response latency per evoked spike. We anticipate our results to lead to better understanding of the suitability of this computational paradigm to account for the brain's functionalities and will require the development of new systematic mathematical methods beyond the methods developed for traditional Boolean algebra.Comment: 32 pages, 14 figures, 1 tabl

Neuronal Response Impedance Mechanism Implementing Cooperative Networks with Low Firing Rates and Microseconds Precision

Realizations of low firing rates in neural networks usually require globally balanced distributions among excitatory and inhibitory links, while feasibility of temporal coding is limited by neuronal millisecond precision. We show that cooperation, governing global network features, emerges through nodal properties, as opposed to link distributions. Using in vitro and in vivo experiments we demonstrate microsecond precision of neuronal response timings under low stimulation frequencies, whereas moderate frequencies result in a chaotic neuronal phase characterized by degraded precision. Above a critical stimulation frequency, which varies among neurons, response failures were found to emerge stochastically such that the neuron functions as a low pass filter, saturating the average inter-spike-interval. This intrinsic neuronal response impedance mechanism leads to cooperation on a network level, such that firing rates are suppressed towards the lowest neuronal critical frequency simultaneously with neuronal microsecond precision. Our findings open up opportunities of controlling global features of network dynamics through few nodes with extreme properties.Comment: 35 pages, 13 figure

Utilizing Neurons for Digital Logic Circuits: A Molecular Communications Analysis

With the advancement of synthetic biology, several new tools have been conceptualized over the years as alternative treatments for current medical procedures. As part of this work, we investigate how synthetically engineered neurons can operate as digital logic gates that can be used towards bio-computing inside the brain and its impact on epileptic seizure-like behaviour. We quantify the accuracy of logic gates under high firing rates amid a network of neurons and by how much it can smooth out uncontrolled neuronal firings. To test the efficacy of our method, simulations composed of computational models of neurons connected in a structure that represents a logic gate are performed. Our simulations demonstrate the accuracy of performing the correct logic operation, and how specific properties such as the firing rate can play an important role in the accuracy. As part of the analysis, the mean squared error is used to quantify the quality of our proposed model and predict the accurate operation of a gate based on different sampling frequencies. As an application, the logic gates were used to smooth out epileptic seizure-like activity in a biological neuronal network, where the results demonstrated the effectiveness of reducing its mean firing rate. Our proposed system has the potential to be used in future approaches to treating neurological conditions in the brain

Applied Advanced Error Control Coding for General Purpose Representation and Association Machine Systems

General-Purpose Representation and Association Machine (GPRAM) is proposed to be focusing on computations in terms of variation and flexibility, rather than precision and speed. GPRAM system has a vague representation and has no predefined tasks. With several important lessons learned from error control coding, neuroscience and human visual system, we investigate several types of error control codes, including Hamming code and Low-Density Parity Check (LDPC) codes, and extend them to different directions. While in error control codes, solely XOR logic gate is used to connect different nodes. Inspired by bio-systems and Turbo codes, we suggest and study non-linear codes with expanded operations, such as codes including AND and OR gates which raises the problem of prior-probabilities mismatching. Prior discussions about critical challenges in designing codes and iterative decoding for non-equiprobable symbols may pave the way for a more comprehensive understanding of bio-signal processing. The limitation of XOR operation in iterative decoding with non-equiprobable symbols is described and can be potentially resolved by applying quasi-XOR operation and intermediate transformation layer. Constructing codes for non-equiprobable symbols with the former approach cannot satisfyingly perform with regarding to error correction capability. Probabilistic messages for sum-product algorithm using XOR, AND, and OR operations with non-equiprobable symbols are further computed. The primary motivation for the constructing codes is to establish the GPRAM system rather than to conduct error control coding per se. The GPRAM system is fundamentally developed by applying various operations with substantial over-complete basis. This system is capable of continuously achieving better and simpler approximations for complex tasks. The approaches of decoding LDPC codes with non-equiprobable binary symbols are discussed due to the aforementioned prior-probabilities mismatching problem. The traditional Tanner graph should be modified because of the distinction of message passing to information bits and to parity check bits from check nodes. In other words, the message passing along two directions are identical in conventional Tanner graph, while the message along the forward direction and backward direction are different in our case. A method of optimizing signal constellation is described, which is able to maximize the channel mutual information. A simple Image Processing Unit (IPU) structure is proposed for GPRAM system, to which images are inputted. The IPU consists of a randomly constructed LDPC code, an iterative decoder, a switch, and scaling and decision device. The quality of input images has been severely deteriorated for the purpose of mimicking visual information variability (VIV) experienced in human visual systems. The IPU is capable of (a) reliably recognizing digits from images of which quality is extremely inadequate; (b) achieving similar hyper-acuity performance comparing to human visual system; and (c) significantly improving the recognition rate with applying randomly constructed LDPC code, which is not specifically optimized for the tasks