TY - GEN
T1 - A Real-Time Retinomorphic Simulator Using a Conductance-Based Discrete Neuronal Network
AU - Baek, Seungbum
AU - Eshraghian, Jason K.
AU - Thio, Wesley
AU - Sandamirskaya, Yulia
AU - Iu, Herbert H.C.
AU - Lu, Wei D.
PY - 2020/8
Y1 - 2020/8
N2 - We present an optimized conductance-based retina microcircuit simulator which transforms light stimuli into a series of graded and spiking action potentials through photo transduction. We use discrete retinal neuron blocks based on a collation of single-compartment models and morphologically realistic formulations, and successfully achieve a biologically real-time simulator. This is done by optimizing the numerical methods employed to solve the system of over 270 nonlinear ordinary differential equations and parameters. Our simulator includes some of the most recent advances in compartmental modeling to include five intrinsic ion currents of each cell whilst ensuring real-time performance, in attaining the ion-current and membrane responses of the photoreceptor rod and cone cells, the bipolar and amacrine cells, their laterally connected electrical and chemical synapses, and the output ganglion cell. It exhibits dynamical retinal behavior such as spike-frequency adaptation, rebound activation, fast-spiking, and subthreshold responsivity. Light stimuli incident at the photoreceptor rod and cone cells is modulated through the system of differential equations, enabling the user to probe the neuronal response at any point in the network. This is in contrast to many other retina encoding schemes which prefer to 'black-box' the preceding stages to the spike train output. Our simulator is made available open source, with the hope that it will benefit neuroscientists and machine learning practitioners in better understanding the retina subcircuitries, how retina cells optimize the representation of visual information, and in generating large datasets of biologically accurate graded and spiking responses.
AB - We present an optimized conductance-based retina microcircuit simulator which transforms light stimuli into a series of graded and spiking action potentials through photo transduction. We use discrete retinal neuron blocks based on a collation of single-compartment models and morphologically realistic formulations, and successfully achieve a biologically real-time simulator. This is done by optimizing the numerical methods employed to solve the system of over 270 nonlinear ordinary differential equations and parameters. Our simulator includes some of the most recent advances in compartmental modeling to include five intrinsic ion currents of each cell whilst ensuring real-time performance, in attaining the ion-current and membrane responses of the photoreceptor rod and cone cells, the bipolar and amacrine cells, their laterally connected electrical and chemical synapses, and the output ganglion cell. It exhibits dynamical retinal behavior such as spike-frequency adaptation, rebound activation, fast-spiking, and subthreshold responsivity. Light stimuli incident at the photoreceptor rod and cone cells is modulated through the system of differential equations, enabling the user to probe the neuronal response at any point in the network. This is in contrast to many other retina encoding schemes which prefer to 'black-box' the preceding stages to the spike train output. Our simulator is made available open source, with the hope that it will benefit neuroscientists and machine learning practitioners in better understanding the retina subcircuitries, how retina cells optimize the representation of visual information, and in generating large datasets of biologically accurate graded and spiking responses.
KW - biological
KW - photoreceptors
KW - retina
KW - simulator
KW - spiking neural network
UR - http://www.scopus.com/inward/record.url?scp=85085057357&partnerID=8YFLogxK
U2 - 10.1109/AICAS48895.2020.9073963
DO - 10.1109/AICAS48895.2020.9073963
M3 - Conference paper
AN - SCOPUS:85085057357
T3 - Proceedings - 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020
SP - 79
EP - 83
BT - Proceedings - 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - Italy
T2 - 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020
Y2 - 31 August 2020 through 2 September 2020
ER -