hierarchical-connectome-phc

name	hierarchical-connectome-phc
description	Parallelized Hierarchical Connectome (PHC) framework for spatiotemporal recurrent spiking neural networks. Upgrades State-Space Models (SSMs) into spatiotemporal networks with biological constraints including Dale's Law, short-term plasticity, and reward-modulated STDP. Activation: spiking neural networks, SSM, connectome, spatiotemporal modeling, biological neural networks.

Parallelized Hierarchical Connectome (PHC)

Overview

This work presents the Parallelized Hierarchical Connectome (PHC), a general framework that upgrades temporal-only State-Space Models (SSMs) into spatiotemporal recurrent networks by mapping SSM components to hierarchical neuronal architectures with biological constraints.

Paper Reference

• Title: Parallelized Hierarchical Connectome: A Spatiotemporal Recurrent Framework for Spiking State-Space Models
• arXiv ID: 2604.01295v1
• Authors: Po-Han Chiang
• Published: 2026-04-01
• Category: q-bio.NC (Neurons and Cognition)
• PDF: https://arxiv.org/pdf/2604.01295v1

Core Innovation

PHC maps the diagonal SSM core to a shared Neuron Layer and inter-neuronal communication to a shared Synapse Layer, where neurons are partitioned into hierarchical regions governed by the connectome topology.

Key Features

1. Multi-Transmission Loop: Enables intra-slice spatial recurrence within each temporal window while preserving O(logT) parallelism

2. Biological Constraints: Supports intractable neuro-physical priors including:

   • Adaptive leaky integrate-and-fire (ALIF) dynamics
   • Dale's Law (excitatory/inhibitory neuron separation)
   • Short-term plasticity
   • Reward-modulated spike-timing-dependent plasticity (STDP)

3. Parameter Efficiency: Reduces complexity from Θ(D²L) for L-layer stacked architectures to Θ(D²)

PHCSSM Implementation

PHCSSM is the first model to unify:

• Recurrent spiking neural network dynamics
• Diagonal SSM parallelism
• Five biological constraints
• Learnable lateral connections
• Fully parallelizable training pipeline

Architecture Components

Neuron Layer (NL)

Maps diagonal SSM core to hierarchical neuronal regions

Synapse Layer (SL)

Inter-neuronal communication with connectome topology

Multi-Transmission Loop

• Intra-slice spatial recurrence
• Preserves parallel scan efficiency
• Enables lateral/feedback interactions within single timestep

Biological Constraints Supported

Constraint	Description
ALIF	Adaptive leaky integrate-and-fire dynamics
Dale's Law	Excitatory/inhibitory neuron separation
STP	Short-term plasticity
R-STDP	Reward-modulated spike-timing-dependent plasticity

Empirical Results

Evaluated on physiological benchmarks from the UEA multivariate time-series archive:

• Performance: Competitive with state-of-the-art SSMs
• Parameter complexity: Reduced from Θ(D²L) to Θ(D²)
• Training: Fully parallelizable pipeline

Methodology Applications

Use this framework when:

• Building biologically grounded sequence models
• Implementing spiking neural networks with SSM efficiency
• Researching brain-inspired parameter-efficient architectures
• Studying spatiotemporal dynamics in neural systems

Trigger Keywords

• spiking neural network
• state-space model
• connectome
• spatiotemporal recurrence
• biological neural network
• Dale's Law
• short-term plasticity
• reward-modulated STDP
• parallel scan
• parameter-efficient SSM