Qualitative-Modeling-Based Silicon Neurons and Their Networks

Abstract

The ionic conductance models of neuronal cells can finely reproduce a wide variety of complex neuronal activities. However, the complexity of these models has prompted the development of qualitative neuron models. They are described by differential equations with a reduced number of variables and their low-dimensional polynomials, which retain the core mathematical structures. Such simple models form the foundation of a bottom-up approach in computational and theoretical neuroscience. We proposed a qualitative-modeling-based approach for designing silicon neuron circuits, in which the mathematical structures in the polynomial-based qualitative models are reproduced by differential equations with silicon-native expressions. This approach can realize low-power-consuming circuits that can be configured to realize various classes of neuronal cells. In this article, our qualitative-modeling-based silicon neuron circuits for analog and digital implementations are quickly reviewed. One of our CMOS analog silicon neuron circuits can realize a variety of neuronal activities with a power consumption less than 72 nW. The square-wave bursting mode of this circuit is explained. Another circuit can realize Class I and II neuronal activities with about 3 nW. Our digital silicon neuron circuit can also realize these classes. An auto-associative memory realized on an all-to-all connected network of these silicon neurons is also reviewed, in which the neuron class plays important roles in its performance.

Network structure reconstruction using packets of spikes in cultured neuronal networks coupled to microelectrode arrays

“…The proposed method has been shown to be applicable to the network reconstruction of integrateand-fire [11], leaky integrate-and-fire [11], and Izhikevich spiking neural models (herein). In future works, the generality of the proposed method should be verified using other models, such as the DSSN model [25] or Hodgkin-Huxley model [26]. Moreover, we have not taken into account slower synaptic current dynamics, such as the ones induced by slow NMDA channels, nor the effect of short-term synaptic plasticity.…”

Microfluidic Neurons, a New Way in Neuromorphic Engineering?

“…However, few of them [ 19 , 20 , 21 , 22 , 23 ] allow hybrid experiments with living neuron cells. The specifications of such systems, are: working in real-time with a biological time-scale, using complex neuron models that mimic the spike timing, and/or the morphology of action potential.…”

A Metaheuristic Approach for Parameter Fitting in Digital Spiking Silicon Neuron Model

“…4 We have been studying qualitative neuronal models for digital as well as analog circuit implementation that satisfy both the reproducibility of neuronal activities and low computational cost. 5,6,7,8 The core idea of our qualitative-modeling-based approach is to reproduce the core mathematical structures that a wide variety of neuronal activities. In our previous studies, 9,10 we extended the DSSN models 7 to support various neuronal classes; regular spiking (RS), fast spiking (FS), intrinsically bursting (IB), low-threshold spiking (LTS),…”