Ramin M. Hasani

c302: a multiscale framework for modelling the nervous system of Caenorhabditis elegans

The OpenWorm project has the ambitious goal of producing a highly detailed in silico model of the nematode Caenorhabditis elegans. A crucial part of this work will be a model of the nervous system encompassing all known cell types and connections. The appropriate level of biophysical detail required in the neuronal model to reproduce observed high-level behaviours in the worm has yet to be determined. For this reason, we have developed a framework, c302, that allows different instances of neuronal networks to be generated incorporating varying levels of anatomical and physiological detail, which can be investigated and refined independently or linked to other tools developed in the OpenWorm modelling toolchain. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.

Efficient modeling of complex Analog integrated circuits using neural networks

This paper introduces a black-box method for automatically learning an approximate but simulation-time efficient high-level abstraction of given analog integrated circuit (IC). The learned abstraction consists of a non-linear auto-regressive neural network with exogenous input (NARX), which is trained and validated from the input-output traces of the IC stimulated with particular inputs. We show the effectiveness of our approach on the power-up behavior and supply dependency of a CMOS band-gap reference (BGR) circuit. We discuss in detail the precision of the NARX abstraction, and show how this model can be used and implemented in testing of Analog ICs within the Cadence environment. By using our method one can automatically learn high-level abstractions of all the components of an Analog IC. This dramatically speeds up the transient simulation time of the Analog ICs.

OpenWorm: overview and recent advances in integrative biological simulation of Caenorhabditis elegans

The adoption of powerful software tools and computational methods from the software industry by the scientific research community has resulted in a renewed interest in integrative, large-scale biological simulations. These typically involve the development of computational platforms to combine diverse, process-specific models into a coherent whole. The OpenWorm Foundation is an independent research organization working towards an integrative simulation of the nematode Caenorhabditis elegans, with the aim of providing a powerful new tool to understand how the organisms behaviour arises from its fundamental biology. In this perspective, we give an overview of the history and philosophy of OpenWorm, descriptions of the constituent sub-projects and corresponding open-science management practices, and discuss current achievements of the project and future directions. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.

Probabilistic reachability analysis of the tap withdrawal circuit in caenorhabditis elegans

We present a probabilistic reachability analysis of a (nonlinear ODE) model of a neural circuit in Caeorhabditis elegans (C. elegans), the common roundworm. In particular, we consider Tap Withdrawal (TW), a reflexive behavior exhibited by a C. elegans worm in response to vibrating the surface on which it is moving. The neural circuit underlying this response is the subject of this investigation. Specially, we perform bounded-time reachability analysis on the TW circuit model of Wicks et al. (1996) to estimate the probability of various TW responses. The Wicks et al. model has a number of parameters, and we demonstrate that the various TW responses and their probability of occurrence in a population of worms can be viewed as a problem of parameter uncertainty. Our approach to this problem rests on encoding each TW response as a hybrid automaton with parametric uncertainty. We then perform probabilistic reachability analysis on these automata using a technique that combines a δ-decision procedure with statistical tests. The results we obtain are a significant extension of those of Wicks et al. (1996), who equip their model with fixed parameter values that reproduce a single TW response. In contrast, our technique allow us to more thoroughly explore the models parameter space using statistical sampling theory, identifying in the process the distribution of TW responses. Wicks et al. conducted a number of ablation experiments on a population of worms in which one or more of the neurons in the TW circuit are surgically ablated (removed). We show that our technique can be used to correctly estimate TW response-probabilities for four of these ablation groups. We also use our technique to predict TW response behavior for two ablation groups not previously considered by Wicks et al.

Computing with Biophysical and Hardware-Efficient Neural Models

In this paper we evaluate how seminal biophysical Hodgkin Huxley model and hardware-efficient TrueNorth model of spiking neurons can be used to perform computations on spike rates in frequency domain. This side-by-side evaluation allows us to draw connections how fundamental arithmetic operations can be realized by means of spiking neurons and what assumptions should be made on input to guarantee the correctness of the computed result. We validated our approach in simulation and consider this work as a first step towards FPGA hardware implementation of neuromorphic accelerators based on spiking models.

Towards Deterministic and Stochastic Computations with the Izhikevich Spiking-Neuron Model.

In this paper we analyze simple computations with spiking neural networks (SNN), laying the foundation for more sophisticated calculations. We consider both a deterministic and a stochastic computation framework with SNNs, by utilizing the Izhikevich neuron model in various simulated experiments. Within the deterministic-computation framework, we design and implement fundamental mathematical operators such as addition, subtraction, multiplexing and multiplication. We show that cross-inhibition of groups of neurons in a winner-takes-all (WTA) network-configuration produces considerable computation power and results in the generation of selective behavior that can be exploited in various robotic control tasks. In the stochastic-computation framework, we discuss an alternative computation paradigm to the classic von Neumann architecture, which supports information storage and decision making. This paradigm uses the experimentally-verified property of networks of randomly connected spiking neurons, of storing information as a stationary probability distribution in each of the sub-network of the SNNs. We reproduce this property by simulating the behavior of a toy-network of randomly-connected stochastic Izhikevich neurons.

Compositional neural-network modeling of complex analog circuits

We introduce CompNN, a compositional method for the construction of a neural-network (NN) capturing the dynamic behavior of a complex analog multiple-input multiple-output (MIMO) system. CompNN first learns for each input/output pair (i, j), a small-sized nonlinear auto-regressive neural network with exogenous input (NARX) representing the transfer-function hij. The training dataset is generated by varying input i of the MIMO, only. Then, for each output j, the transfer functions hij are combined by a time-delayed neural network (TDNN) layer, fj. The training dataset for fj is generated by varying all MIMO inputs. The final output is f = (f1, …, fn). The NNs parameters are learned using Levenberg-Marquardt back-propagation algorithm. We apply CompNN to learn an NN abstraction of a CMOS band-gap voltage-reference circuit (BGR). First, we learn the NARX NNs corresponding to trimming, load-jump and linejump responses of the circuit. Then, we recompose the outputs by training the second layer TDNN structure. We demonstrate the performance of our learned NN in the transient simulation of the BGR by reducing the simulation-time by a factor of 17 compared to the transistor-level simulations. CompNN allows us to map particular parts of the NN to specific behavioral features of the BGR. To the best of our knowledge, CompNN is the first method to learn the NN of an analog integrated circuit (MIMO system) in a compositional fashion.

A novel Bayesian network-based fault prognostic method for semiconductor manufacturing process

Fault prognostic in various levels of production of semiconductor chips is considered to be a great challenge. To reduce yield loss during the manufacturing process, tool abnormalities should be detected as early as possible during process monitoring. In this paper, we propose a novel fault prognostic method based on Bayesian networks. The network is designed such that it can process both discrete and continuous variables, to represent the correlations between critical deviations and to process quality control data based on divide-and-conquer strategy. Such a network enables us to perform high-precision multi-step prognostic on the status of the fabrication process given the current state of the sensory info. Additionally, we introduce a layer-wise approach for efficient learning of the Bayesian-network parameters. We evaluate the accuracy of our prognostic model on a wafer fabrication dataset where our model performs precise next-step fault prognostic by using the control sensory data.