Tunable neuromimetic integrated system for emulating cortical neuron models (original) (raw)
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Integrated circuit implementation of a cortical neuron
This paper presents an analogue integrated circuit implementation of a cortical neuron model. The VLSI chip prototype has been implemented in a 0.35 µm CMOS technology. The single neuron cell has a compact layout and very low energy consumption, in the range of 9 pJ per spike. Experimental results demonstrate the capability of the circuit to generate a realistic spike shape and a variety of spiking and bursting firing patterns. The models of various cortical neuron types are obtained in a single circuit, through the adjustment of two biasing voltages, making the circuit suitable for applications in reconfigurable neuromorphic devices that implement biologically plausible spiking neural networks.
This paper proposes a silicon neuron circuit which uses a slow-variable controlled leakage term to extend the repertoire of spiking patterns achievable in an integrate and fire model. The simulations reveal the potential of the circuit to provide a wide variety of neuron firing patterns observed in neocortex, including adapting and non-adapting, regular spiking, fast spiking, bursting, chattering, etc. The firing patterns of basic cell classes are obtained with a simple adjustment of four biasing voltages. The circuit operates in the sub-threshold regime, with time constants similar to biological neurons, and hence is suitable for use in systems requiring such operating speeds. Envisaged applications of the proposed circuit are in large-scale analogue VLSI systems for spiking neural network simulations, brain-inspired circuits for robotics and hybrid silicon/biology systems.
IEEE Transactions on Biomedical Circuits and Systems, 2011
In this paper, we present a library of analog operators used for the analog real-time computation of the Hodgkin-Huxley formalism. These operators make it possible to design a silicon (Si) neuron that is dynamically tunable, and that reproduces different kinds of neurons. We used an original method in neuromorphic engineering to characterize this Si neuron. In electrophysiology, this method is well known as the "voltage-clamp" technique. We also compare the features of an application-specific integrated circuit built with this library with results obtained from software simulations. We then present the complex behavior of neural membrane voltages and the potential applications of this Si neuron.
Neuromorphic Silicon Neuron Circuits
Frontiers in Neuroscience, 2011
Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin-Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.
Neuromimetic ICs and system for parameters extraction in biological neuron models
2006 IEEE International Symposium on Circuits and Systems
This paper presents an analog neuromimetic integrated circuit and an associated system dedicated for experiments of parameters extraction in biological neuron models. The IC based on Hodgkin-Huxley (HH) formalism computes in real-time and continuous mode. The dedicated system is a PCI board that is able to program dynamically the neuron model parameters in the IC. The full system, which includes the IC and the PCI board, is used to build a new hardware/software technique to extract biophysics parameters from biological neuron. This technique could be helpful for the neuroscientists proposing an alternative to voltage-clamp technique. For that, the new technique will use optimization algorithms to be efficient.
Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI
IEEE Transactions on Biomedical Circuits and Systems, 2000
We study a range of neural dynamics under variations in biophysical parameters underlying extended Morris-Lecar and Hodgkin-Huxley models in three gating variables. The extended models are implemented in NeuroDyn, a four neuron, twelve synapse continuous-time analog VLSI programmable neural emulation platform with generalized channel kinetics and biophysical membrane dynamics. The dynamics exhibit a wide range of time scales extending beyond 100 ms neglected in typical silicon models of tonic spiking neurons. Circuit simulations and measurements show transition from tonic spiking to tonic bursting dynamics through variation of a single conductance parameter governing calcium recovery. We similarly demonstrate transition from graded to all-or-none neural excitability in the onset of spiking dynamics through the variation of channel kinetic parameters governing the speed of potassium activation. Other combinations of variations in conductance and channel kinetic parameters give rise to phasic spiking and spike frequency adaptation dynamics. The NeuroDyn chip consumes 1.29 mW and occupies 3 mm 3 mm in 0.5 m CMOS, supporting emerging developments in neuromorphic silicon-neuron interfaces.
Spiking and Bursting Firing Patterns of a Compact VLSI Cortical Neuron Circuit
2007 International Joint Conference on Neural Networks, 2007
The paper presents a silicon neuron circuit that mimics the behaviour of known classes of biological neurons. The circuit has been designed in a 0.35µm CMOS technology. The firing patterns of basic cell classes: regular spiking (RS), fast spiking (FS), chattering (CH) and intrinsic bursting (IB) are obtained with a simple adjustment of two biasing voltages. The simulations reveal the potential of the circuit to provide a wide variety of cell behaviours with required accommodation and firing frequency of a given cell type. The neuron consumes only 14 MOSFETs enabling the integration of many neurons in a small silicon area. Hence, the circuit provides a foundation for designing massively parallel analogue neuromorphic networks that closely resemble the circuits of the cortex.
A Multiconductance Silicon Neuron With Biologically Matched Dynamics
IEEE Transactions on Biomedical Engineering, 2004
We have designed, fabricated, and tested an analog integrated-circuit architecture to implement the conductance-based dynamics that model the electrical activity of neurons. The dynamics of this architecture are in accordance with the Hodgkin-Huxley formalism, a widely exploited, biophysically plausible model of the dynamics of living neurons . Furthermore the architecture is modular and compact in size so that we can implement networks of silicon neurons, each of desired complexity, on a single integrated circuit. We present in this paper a six-conductance silicon-neuron implementation, and characterize it in relation to the Hodgkin-Huxley formalism. This silicon neuron incorporates both fast and slow ionic conductances, which are required to model complex oscillatory behaviors (spiking, bursting, subthreshold oscillations).
We introduce and test a system for simulating networks of conductance-based neuron models using analog circuits. At the single-cell level, we use custom-designed analog circuits (ASICs) that simulate two types of spiking neurons based on Hodgkin-Huxley like dynamics: "regular spiking" excitatory neurons with spike-frequency adaptation, and "fast spiking" inhibitory neurons. Synaptic interactions are mediated by conductance-based synaptic currents described by kinetic models. Connectivity and plasticity rules are implemented digitally through a real time interface between a computer and a PCI board containing the ASICs. We show a prototype system of a few neurons interconnected with synapses undergoing spike-timing dependent plasticity (STDP), and compare this system with numerical simulations. We use this system to evaluate the effect of parameter dispersion on the behavior of small circuits of neurons. It is shown that, although the exact spike timings are not precisely emulated by the ASIC neurons, the behavior of small networks with STDP matches that of numerical simulations. Thus, this mixed analog-digital architecture provides a valuable tool for real-time simulations of networks of neurons with STDP. They should be useful for any real-time application, such as hybrid systems interfacing network models with biological neurons.
Hardware computation of conductance-based neuron models
Neurocomputing, 2004
We review di erent applications of silicon conductance-based neuron models implemented on analog circuits. At the single-cell level, we describe a circuit in which conductances are programmed to simulate various Hodgkin-Huxley type models; integrated in a hardware/software system, they provide a simulation tool; an illustrative example is the simulation of bursting neurons of the thalamus. At the network level, we present a mixed analog-digital architecture, where the connectivity and the plasticity rules are implemented digitally and are therefore very exible. These circuits provide valuable tools for real-time simulations, including hybrid applications where single-neuron or network models are interfaced with biological cells.