Information Representation and Computation of Spike Trains in Reservoir Computing Systems with Spiking Neurons and Analog Neurons

Real-time processing of space-and-time-variant signals is imperative for perception and real-world problem-solving. In the brain, spatio-temporal stimuli are converted into spike trains by sensory neurons and projected to the neurons in subcortical and cortical layers for further processing. Reservoir Computing (RC) is a neural computation paradigm that is inspired by cortical Neural Networks (NN). It is promising for real-time, on-line computation of spatio-temporal signals. An RC system incorporates a Recurrent Neural Network (RNN) called reservoir, the state of which is changed by a trajectory of perturbations caused by a spatio-temporal input sequence. A trained, non- recurrent, linear readout-layer interprets the dynamics of the reservoir over time. Echo-State Network (ESN) [1] and Liquid-State Machine (LSM) [2] are two popular and canonical types of RC system. The former uses non-spiking analog sigmoidal neurons – and, more recently, Leaky Integrator (LI) neurons – and a normalized random connectivity matrix in the reservoir. Whereas, the reservoir in the latter is composed of Leaky Integrate-and-Fire (LIF) neurons, distributed in a 3-D space, which are connected with dynamic synapses through a probability function. The major difference between analog neurons and spiking neurons is in their neuron model dynamics and their inter-neuron communication mechanism. However, RC systems share a mysterious common property: they exhibit the best performance when reservoir dynamics undergo a criticality [1–6] – governed by the reservoirs’ connectivity parameters, |λmax| ≈ 1 in ESN, λ ≈ 2 and w in LSM – which is referred to as the edge of chaos in [3–5]. In this study, we are interested in exploring the possible reasons for this commonality, despite the differences imposed by different neuron types in the reservoir dynamics. We address this concern from the perspective of the information representation in both spiking and non-spiking reservoirs. We measure the Mutual Information (MI) between the state of the reservoir and a spatio-temporal spike-trains input, as well as that, between the reservoir and a linearly inseparable function of the input, temporal parity. In addition, we derive Mean Cumulative Mutual Information (MCMI) quantity from MI to measure the amount of stable memory in the reservoir and its correlation with the temporal parity task performance. We complement our investigation by conducting isolated spoken-digit recognition and spoken-digit sequence-recognition tasks. We hypothesize that a performance analysis of these two tasks will agree with our MI and MCMI results with regard to the impact of stable memory in task performance. It turns out that, in all reservoir types and in all the tasks conducted, reservoir performance peaks when the amount of stable memory in the reservoir is maxi-mized. Likewise, in the chaotic regime (when the network connectivity parameter is greater than a critical value), the absence of stable memory in the reservoir seems to be an evident cause for performance decrease in all conducted tasks. Our results also show that the reservoir with LIF neurons possess a higher stable memory of the input (quantified by input-reservoir MCMI) and outperforms the reservoirs with analog sigmoidal and LI neurons in processing the temporal parity and spoken-digit recognition tasks. From an efficiency stand point, the reservoir with 100 LIF neurons outperforms the reservoir with 500 LI neurons in spoken- digit recognition tasks. The sigmoidal reservoir falls short of solving this task. The optimum input-reservoir MCMI’s and output-reservoir MCMI’s we obtained for the reservoirs with LIF, LI, and sigmoidal neurons are 4.21, 3.79, 3.71, and 2.92, 2.51, and 2.47 respectively. In our isolated spoken-digits recognition experiments, the maximum achieved mean-performance by the reservoirs with N = 500 LIF, LI, and sigmoidal neurons are 97%, 79% and 2% respectively. The reservoirs with N = 100 neurons could solve the task with 80%, 68%, and 0.9% respectively. Our study sheds light on the impact of the information representation and memory of the reservoir on the performance of RC systems. The results of our experiments reveal the advantage of using LIF neurons in RC systems for computing spike-trains to solve memory demanding, real-world, spatio-temporal problems. Our findings have applications in engineering nano-electronic RC systems that can be used to solve real-world spatio-temporal problems

Advances in Microfluidics and Lab-on-a-Chip Technologies

Advances in molecular biology are enabling rapid and efficient analyses for effective intervention in domains such as biology research, infectious disease management, food safety, and biodefense. The emergence of microfluidics and nanotechnologies has enabled both new capabilities and instrument sizes practical for point-of-care. It has also introduced new functionality, enhanced sensitivity, and reduced the time and cost involved in conventional molecular diagnostic techniques. This chapter reviews the application of microfluidics for molecular diagnostics methods such as nucleic acid amplification, next-generation sequencing, high resolution melting analysis, cytogenetics, protein detection and analysis, and cell sorting. We also review microfluidic sample preparation platforms applied to molecular diagnostics and targeted to sample-in, answer-out capabilities

Computational Capabilities of Leaky Integrate-and-Fire Neural Networks for Liquid State Machines

We analyze the computational capability of Leaky Integrate-and-Fire (LIF) Neural Networks used as a reservoir (liquid) in the framework of Liquid State Machines (LSM). Maass et. al. investigated LIF neurons in LSM and their results showed that they are capable of noise-robust, parallel, and real-time computation. However, it still remains an open question how the network topology affects the computational capability of a reservoir. To address that question, we investigate the performance of the reservoir as a function of the average reservoir connectivity. We also show that the dynamics of the LIF reservoir is sensitive to changes in the average network connectivity, which is consistent with the results taken from RBN reservoirs. Our results are relevant for understanding of the computational capabilities of reservoirs made up of biologically-realistic neuron models for real-time processing of time- varying inputs

Use of bakri balloon catheter and pedicled omental flap in combination for pelvic reconstruction after total pelvic exenteration

Our case was a middle-aged woman with advanced cervical cancer that underwent pelvic exenteration (PE) and then pelvic reconstruction (PR) with omental flap and bakri balloon placement.</jats:p

Short-Term Toxicological Evaluation of Triapine (3-Amino-Pyridine-2-Carboxaldehyde Thiosemicarbazone or 3-AP), a Ribonucleotide Reductase Inhibitor with Potential Antitumor Activity, in Dogs and Rats

The objective of these studies was to evaluate the potential toxicity of Triapine (a ribonucleotide reductase inhibitor with potential antitumor activity) in dogs and rats. Various doses (1–20 mg/kg) of Triapine were administered intravenously (IV), once daily for 1, 3, or 5 consecutive days, to Beagle dogs or Albino Sprague-Dawley rats. Control animals were treated with vehicle, with an amount equivalent to a dose of 20 mg/kg of Triapine. Data collected included body weights, clinical signs, clinical pathology (serum chemistry and hematology), and gross and microscopic pathology. In dogs, the no-observed-adverse-effect level (NOAEL) of Triapine, when administered once daily for 5 consecutive days, was 1 mg/kg. For 5 daily doses at 3 mg/kg, or a single dose at 10 mg/kg, Triapine induced only limited toxicity (emesis and diarrhea, causing inappetence and body weight loss). The intensities/frequencies of the clinical signs were greater when Triapine was administered over 15 minutes than 120 minutes. Ondansetron (an antiemetic agent due to its H2 antagonistic properties) delayed, but did not eliminate, emesis induced by 10 mg/kg of Triapine. In rats, the NOAEL of Triapine, when administered once daily for 5 consecutive days, was not determined. However, when administered once daily for 3 consecutive days, 1 mg/kg was considered the NOAEL. Five to 20 mg/kg of Triapine (and its vehicle) induced a wide-range of toxicity. Toxicity related to the vehicle (as reflected by the similar or greater intensity/frequency of these effects in vehicle-treated rats than Triapine-treated rats) were reduced activity, discolored urine, discolored tails, and the accompanying gross and histopathological findings. Triapine-related effects were mortality; reductions in total protein and albumin levels; reductions in red blood cell, white blood cell, and platelet counts; reductions in body and thymic weights; increases in the liver and lung weights (and the corresponding microscopic findings of these organs); and microscopic findings of the adrenal cortex, testes, bone marrow, and kidney. These effects generally increased in a dose-related manner between 5–15 mg/kg, and leveled off at 15 and 20 mg/kg. Essentially all effects induced by Triapine and/or vehicle had returned, or were returning, to normal during a 30-day recovery period. In conclusion, the NOAEL of Triapine was 1 mg/kg, when administered daily for 5 consecutive days, in dogs. Five daily doses at 3 mg/kg or a single dose at 10 mg/kg Triapine-induced emesis and diarrhea. The toxicity was greater when Triapine was administered over 15 minutes than over 120 minutes. Ondansetron had limited efficacy in attenuating the emetic effects of the high dose (10 mg/kg) of Triapine. In rats, the NOAEL of Triapine was 1 mg/kg when administered once daily for 3 consecutive days. At 5–20 mg/kg, Triapine induced a broad spectrum of toxicological effects, which is consistent with its ribonucleotide reductase inhibitory properties. Essentially all effects induced by Triapine and/or vehicle were reversible within 30 days. </jats:p