Spiking Memristor Multimodal

---
name: spiking-memristor-multimodal
description: Memristive neurons supporting multiple spiking functionalities (TTFS, spike count, firing rate) via annealing optimization for neuromorphic hardware.
version: 1.0.0
author: Research Synthesis
license: MIT
metadata:
  hermes:
    tags: [memristive, spiking, neuromorphic, hardware, annealing, multimodal]
    source_paper: "Multiple spiking functionalities in annealing-optimized Ag/HfZrO-based memristive neurons (arXiv:2604.15366)"
    citations: 0
    related_skills: [circuit-level-spiking-neuron-robustness, snn-low-level-vision]
---

# Memristive Multimodal Spiking Neurons

## Overview
Annealing-optimized Ag/Hf₀.₅Zr₀.₅O₂-based memristive neurons support multiple spiking functionalities in a single hardware device: Time-To-First-Spike (TTFS), spike count coding, and firing rate coding. This multimodal capability enables flexible neural encoding strategies on neuromorphic hardware without requiring different circuit designs for each coding scheme.

## Key Concepts

### Memristive Neuron Dynamics
- Memristor acts as a synapse with history-dependent resistance
- Ag/HfZrO₂ material system provides reliable switching characteristics
- Annealing optimization tunes device parameters for desired spiking behavior
- Single device supports multiple encoding modes

### Multiple Spiking Modes
1. **TTFS (Time-To-First-Spike)**: Information in the timing of the first spike
   - Faster response to stronger stimuli
   - Ultra-low latency for classification tasks
2. **Spike Count**: Information in the total number of spikes
   - Robust to timing jitter
   - Suitable for integration tasks
3. **Firing Rate**: Information in the average spike frequency
   - Compatible with traditional rate-based models
   - Easy to interface with conventional ML

### Implementation Pattern
```python
class MemristiveNeuron:
    def __init__(self, mode='ttfs', annealing_params=None):
        self.mode = mode
        self.memristance = R_initial
        self.membrane_potential = 0
        self.threshold = V_th
        self.refractory = 0
        
        # Annealing-optimized parameters
        if annealing_params:
            self.threshold = annealing_params['threshold']
            self.membrane_time_constant = annealing_params['tau']
        
    def encode(self, stimulus):
        if self.mode == 'ttfs':
            # Time-to-first-spike encoding
            return self._compute_first_spike_time(stimulus)
        elif self.mode == 'count':
            # Spike count encoding
            return self._compute_spike_count(stimulus, window=100)
        elif self.mode == 'rate':
            # Firing rate encoding
            return self._compute_firing_rate(stimulus, window=100)
    
    def _compute_first_spike_time(self, stimulus):
        """Stronger stimuli produce earlier spikes."""
        t_spike = tau / np.log(stimulus / threshold)
        return max(t_spike, 0)
```

## Activation Keywords
memristive neuron, TTFS, spike count, firing rate, annealing optimization, Ag/HfZrO2, neuromorphic hardware, multimodal coding

## Applications
- Reconfigurable neuromorphic chips
- Adaptive encoding in sensory processing
- Multi-task neuromorphic systems
- Brain-inspired hardware accelerators

## Limitations
- Device variability requires calibration
- Annealing optimization is compute-intensive
- Limited number of switching cycles in some memristive materials