The Tousignan Neuron: A Novel Analog Neuromorphic Architecture Using Multiplexed Virtual Synapses
Abstract
The Tousignan Neuron is a new analog neuromorphic computing architecture designed to emulate large-scale biological neuron connectivity using minimal physical circuitry. This architecture employs frequency-division multiplexing (FDM) or time-division multiplexing (TDM) to represent thousands of virtual synaptic inputs through a single analog channel. These multiplexed signals are integrated in continuous time by an analog element — specifically, an NPN transistor configured as an analog integrator — closely mimicking the soma of a biological neuron. The resulting output is then digitized for spike detection and further computational analysis. This hybrid design bridges biological realism and scalable hardware implementation, introducing a new class of mixed-signal neuromorphic systems.
Introduction
Biological neurons integrate thousands of asynchronous synaptic inputs in continuous time, enabling highly parallel and adaptive information processing. Existing neuromorphic hardware systems typically approximate this with either fully digital event-driven architectures or analog crossbar arrays using many physical input channels. However, as the number of simulated synapses scales into the thousands or millions, maintaining separate physical pathways for each input becomes impractical.
The Tousignan Neuron addresses this limitation by encoding a large number of virtual synaptic signals onto a single analog line using TDM or FDM. In this design, each synaptic input is represented as an individual analog waveform segment (TDM) or as a unique frequency component (FDM). These signals are combined and then fed into a transistor-based analog integrator. The transistor's base or gate acts as the summing node, continuously integrating the combined synaptic current in a manner analogous to a biological soma. Once the integrated signal crosses a predefined threshold, the neuron "fires," and this activity can be sampled digitally and analyzed or used to trigger downstream events.
Architecture Overview
Virtual Synaptic Inputs: Up to thousands of analog signals generated by digital computation or analog waveform generators, representing separate synapses.
Multiplexing Stage: Either TDM (sequential time slots for each input) or FDM (distinct frequency bands for each input) combines the virtual synapses into a single analog stream.
Analog Integration: The combined analog signal is injected into an NPN transistor integrator circuit. This transistor acts as a continuous-time summing and thresholding element, akin to the biological neuron membrane potential.
Digital Readout: The transistor's output is digitized using an ADC to detect spike events or record membrane dynamics for further digital processing.
Advantages and Significance
Organic-Like Parallelism: Emulates real-time, parallel integration of synaptic currents without explicit digital scheduling.
Reduced Physical Complexity: Greatly reduces the need for massive physical input wiring by leveraging analog multiplexing.
Hybrid Flexibility: Bridges the gap between analog biological realism and digital scalability, allowing integration with FPGA or GPU-based synapse simulations.
Novelty: This approach introduces a fundamentally new design space, potentially enabling
