adaptive-spiking-neuron-asn

name	adaptive-spiking-neuron-asn
description	Adaptive Spiking Neuron (ASN) methodology for vision and language modeling. Implements trainable membrane potential dynamics with adaptive firing mechanisms for efficient Spiking Neural Networks (SNNs). Activation: adaptive spiking neuron, ASN, spiking neural network vision language, SNN adaptive neuron, neuromorphic vision language model.

Adaptive Spiking Neurons for Vision and Language Modeling

Overview

This skill provides implementation guidance for the Adaptive Spiking Neuron (ASN) and its normalized variant (NASN) - a next-generation spiking neuron design that achieves high performance across both vision and language tasks through trainable membrane potential dynamics and adaptive firing mechanisms.

Key Features

• Adaptive Spiking Neuron (ASN): Trainable parameters for learning membrane potential dynamics
• Normalized Adaptive Spiking Neuron (NASN): Enhanced variant with integrated normalization for training stability
• Integer Training & Spike Inference: Efficient training paradigm for neuromorphic deployment
• Cross-Modal Performance: Validated on 19 datasets spanning vision and language tasks

Methodology

Core Concept

Traditional spiking neurons use fixed dynamics (e.g., exponential decay). ASN introduces trainable parameters to adapt membrane potential evolution:

τ_m * dV_m/dt = -(V_m - V_rest) + I_syn
With ASN: Adaptive time constants and thresholds based on learned parameters

Architecture Components

1. Trainable Membrane Dynamics

   • Learnable time constants τ
   • Adaptive threshold mechanism
   • Activity-dependent reset

2. Integer Training Paradigm

   • Floating-point during training
   • Integer inference for hardware deployment
   • Quantization-aware optimization

3. Normalization Integration (NASN)

   • Layer/batch normalization compatibility
   • Stabilized training dynamics
   • Better convergence properties

Implementation Guidelines

Basic ASN Neuron

class AdaptiveSpikingNeuron(nn.Module):
    """
    Adaptive Spiking Neuron with trainable dynamics
    
    Args:
        input_size: Input feature dimension
        hidden_size: Hidden state dimension
        tau_init: Initial time constant (default: 2.0)
        threshold: Firing threshold (default: 1.0)
    """
    def __init__(self, input_size, hidden_size, tau_init=2.0, threshold=1.0):
        super().__init__()
        self.input_proj = nn.Linear(input_size, hidden_size)
        self.tau = nn.Parameter(torch.ones(hidden_size) * tau_init)
        self.threshold = threshold
        self.hidden_size = hidden_size
        
    def forward(self, x_t, v_prev):
        # Adaptive time constant (ensured positive via softplus)
        tau_eff = F.softplus(self.tau) + 1.0
        
        # Input current
        i_syn = self.input_proj(x_t)
        
        # Membrane potential update with adaptive dynamics
        dv = (i_syn - v_prev) / tau_eff
        v = v_prev + dv
        
        # Spike generation
        spike = (v >= self.threshold).float()
        v = v * (1 - spike)  # Reset after spike
        
        return spike, v

NASN with Normalization

class NormalizedASN(nn.Module):
    """
    Normalized Adaptive Spiking Neuron
    Integrates normalization for stable training
    """
    def __init__(self, input_size, hidden_size, tau_init=2.0):
        super().__init__()
        self.asn = AdaptiveSpikingNeuron(input_size, hidden_size, tau_init)
        self.norm = nn.LayerNorm(hidden_size)
        
    def forward(self, x_t, v_prev):
        spike, v = self.asn(x_t, v_prev)
        # Normalize membrane potential for stability
        v_normalized = self.norm(v)
        return spike, v_normalized

Multi-Layer SNN with ASN

class ASNSNN(nn.Module):
    """
    Multi-layer SNN using ASN neurons
    Suitable for both vision and language tasks
    """
    def __init__(self, input_size, hidden_sizes, output_size, num_steps):
        super().__init__()
        self.num_steps = num_steps
        
        layers = []
        prev_size = input_size
        for hidden_size in hidden_sizes:
            layers.append(NormalizedASN(prev_size, hidden_size))
            prev_size = hidden_size
        self.layers = nn.ModuleList(layers)
        self.readout = nn.Linear(prev_size, output_size)
        
    def forward(self, x):
        # x shape: (batch, time_steps, features)
        batch_size = x.size(0)
        
        # Initialize membrane potentials
        states = [torch.zeros(batch_size, layer.asn.hidden_size, device=x.device) 
                  for layer in self.layers]
        
        spikes = []
        for t in range(self.num_steps):
            x_t = x[:, t, :]
            
            for i, layer in enumerate(self.layers):
                spike, states[i] = layer(x_t, states[i])
                x_t = spike
            
            spikes.append(spike)
        
        # Readout from final timestep
        output = self.readout(states[-1])
        return output, torch.stack(spikes, dim=1)

Training Configuration

Hyperparameters

Parameter	Vision Tasks	Language Tasks
Time steps (T)	4-8	8-16
Initial τ	2.0	3.0
Learning rate	1e-3	5e-4
Batch size	128	64
Optimizer	AdamW	AdamW
Weight decay	1e-4	1e-4

Loss Function

def asn_loss(output, target, spikes, lambda_reg=1e-5):
    """
    Combined task loss and firing rate regularization
    """
    task_loss = F.cross_entropy(output, target)
    
    # Regularization: encourage sparse firing
    firing_rate = spikes.mean()
    reg_loss = lambda_reg * firing_rate
    
    return task_loss + reg_loss

Task-Specific Adaptations

Vision Tasks (Image Classification)

# Vision encoder with ASN
class VisionASN(nn.Module):
    def __init__(self, num_classes, num_steps=4):
        super().__init__()
        # Use smaller time steps for vision (faster inference)
        self.encoder = ConvASNBlock(3, 64, num_steps)
        self.classifier = ASNSNN(64*8*8, [256, 128], num_classes, num_steps)
        
    def forward(self, x):
        features = self.encoder(x)
        return self.classifier(features.flatten(1))

Language Tasks

# Language modeling with ASN
class LanguageASN(nn.Module):
    def __init__(self, vocab_size, embed_dim, hidden_dim, num_steps=8):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim)
        self.snn = ASNSNN(embed_dim, [hidden_dim, hidden_dim], vocab_size, num_steps)
        
    def forward(self, tokens):
        embedded = self.embedding(tokens)
        output, _ = self.snn(embedded)
        return output

Deployment Optimization

Integer Conversion

def convert_to_integer_model(model, bit_width=8):
    """
    Convert trained ASN model to integer-only inference
    """
    # Quantize weights
    for module in model.modules():
        if isinstance(module, nn.Linear):
            module.weight.data = quantize(module.weight.data, bit_width)
    
    # Convert neuron dynamics to fixed-point
    for module in model.modules():
        if isinstance(module, AdaptiveSpikingNeuron):
            # Scale time constants for integer arithmetic
            module.tau.data = (module.tau.data * 256).round() / 256
    
    return model

Hardware Deployment (Neuromorphic Chips)

Key considerations for deploying ASN on neuromorphic hardware:

• Pre-compute τ values as lookup tables
• Implement membrane update with fixed-point arithmetic
• Optimize spike generation for event-driven processing

Evaluation Benchmarks

The ASN family has been validated on:

Vision Tasks:

• CIFAR-10/100
• ImageNet
• Tiny-ImageNet
• MNIST variants

Language Tasks:

• Language modeling (PTB, WikiText)
• Text classification
• Question answering

Advantages Over Standard SNNs

1. Adaptive Dynamics: Learns optimal time constants per neuron/channel
2. Training Efficiency: Integer training paradigm reduces memory overhead
3. Stability: NASN variant with normalization enables deeper networks
4. Cross-Modal: Single neuron design works across vision and language

References

• Paper: "Adaptive Spiking Neurons for Vision and Language Modeling" (arXiv:2604.12365)
• Authors: Zhou et al., 2026
• Categories: cs.NE (Neural and Evolutionary Computing)

Related Skills

• spiking-neural-network-analysis: Analysis framework for SNN papers
• snn-learning-survey: Comprehensive survey of SNN learning rules
• neuromorphic-computing: Hardware deployment guidelines

Last updated: 2026-04-27