Artificial Hippocampus Networks (AHNs)

• Artificial Hippocampus Networks (AHNs) are bio-inspired neural systems that emulate hippocampal mechanisms like rapid episodic encoding and pattern separation.
• They integrate spiking, recurrent, and content-addressable architectures with STDP and homeostatic plasticity to enhance memory storage and recall.
• AHNs have demonstrated potential in practical applications through neuromorphic implementations and Transformer-based long-context modeling for efficient, scalable memory use.

Artificial Hippocampus Networks (AHNs) are a class of bio-inspired neural systems designed to replicate key computational and organizational principles of the biological hippocampus. Drawing from empirical neuroscience, statistical physics, and cognitive modeling, AHNs encompass spiking, recurrent, multimodal, and content-addressable architectures for rapid memory encoding, robust recall, pattern separation and completion, context-sensitive modulation, and efficient consolidation. These networks have been proposed as models and practical algorithms for episodic learning, memory storage and retrieval, computational efficiency in long-context modeling, and neuro-inspired systems architectures.

1. Biological Foundations and Memory Principles

AHNs model core hippocampal mechanisms including rapid episodic encoding, auto-associative retrieval, and structural plasticity. The biological hippocampus consists of densely recurrent regions (notably CA3), feedforward circuits (entorhinal cortex [EC], dentate gyrus [DG]), and modular architectures with excitatory and inhibitory populations. Neurogenesis in DG introduces new, more excitable neurons for encoding novel experiences while apoptosis prunes old or overlapping neurons; this maintains distinct memory engrams and supports adaptive forgetting (Chua et al., 2017).

Plasticity models in AHNs capture both local spike-timing–dependent plasticity (STDP) and multi-timescale synaptic/homeostatic corrections:

$$\dot{W}^{ij} = K^{i}A_{+} K_2^{j}(t-\epsilon) S^{j}(t) - K_1^{j} A_{-} S^{i}(t) - \beta(W^{ij}-\tilde{W}^{ij}) (K_{1}^{j}(t-\epsilon))^3 S^{j}(t) + \delta S^{i}(t)$$

This formulation reflects the need for both rapid Hebbian learning and stabilizing global feedback, essential for robust associative memory when storing many patterns (Chua et al., 2017).

2. Architectures: Spiking, Recurrent, and Compositional

AHNs exploit several computational architectures:

• Spiking Neural Networks (SNNs): These implement event-driven dynamics with spiking communication and local plasticity. Models combine DG for sparse coding, CA3 for attractor-based autoassociative memory, and CA1 for output mapping. Learning, recall, and forgetting are governed by STDP with rapid saturation and decay, as realized on neuromorphic platforms (e.g., SpiNNaker) (Casanueva-Morato et al., 2022, Casanueva-Morato et al., 2023). For example:

  $$\Delta w = \begin{cases} A_{+} e^{-\Delta t/\tau_{+}} & \text{if}\ \Delta t > 0 \ -A_{-} e^{\Delta t/\tau_{-}} & \text{if}\ \Delta t < 0 \end{cases}$$

• Continuous Attractor Networks: Extended Hopfield models allow storage and retrieval of spatial maps with localized activity "bumps", reflecting place cell dynamics (Cocco et al., 2017). These systems have phase diagrams characterized by clump, paramagnetic, and glassy phases, and their retrieval properties can be decoded using effective Ising models:

  $$P(\mathbf{s}|M) = \frac{1}{Z^M} \exp\left\{\sum_{i} h_i^{(M)} s_i + \sum_{i<j} J_{ij}^{(M)} s_i s_j\right\}$$

  with maximum-likelihood decoding:

  $M^{(t)} = \arg\max_M \log P(\mathbf{s}^{(t)}|M)$

• Compositional/Multimodal Models: AHNs equip latent spaces to represent both spatial ("where") and object-centric ("what") information. Factorized latent spaces (via averaging over specific dimensions) support scene recognition, view synthesis, and object segmentation in architectures mimicking EC and hippocampal subfields (Frey et al., 2023).
• Content-Addressable Memory (CAM): Bio-inspired models implement distributed learning via all-to-all STDP from cue-to-content and content-to-cue populations, facilitating bidirectional recall (by partial cue or content) and implicit forgetting through weight decay (Casanueva-Morato et al., 2023).

3. Episodic Learning, Pattern Separation, and Completion

A key role of AHNs is rapid episodic learning—memorizing unique experiences in one or a few exposures. Pattern separation is achieved through DG-like winner-take-all or top-$k$ selection mechanisms, yielding distinct, decorrelated engram codes even for similar inputs (Kowadlo et al., 2019, Kowadlo et al., 2021). Pattern completion exploits recurrent CA3 networks with attractor dynamics (Hopfield model), enabling robust retrieval of complete memories from partial cues.

Module operations may be formalized as:

• Pattern separation: $p = \text{PS}(x)$
• Storage in recurrent auto-associator: store $p$ in PC
• Cue-based retrieval: $\hat{p} = \text{PR}(x)$
• Reconstruction: $(\hat{x}, \hat{y}) = \text{PM}(\hat{p})$

These collectively ensure one-shot learning, noise robustness, and memory reinstatement.

4. Memory Consolidation and Interplay with Long-Term Systems

Complementary Learning Systems (CLS) frameworks combine fast AHN-based short-term memory (STM) with slower, statistical long-term memory (LTM) modules (Kowadlo et al., 2021). AHNs rapidly encode and replay episodes, which are then consolidated into cortical representations via offline rehearsal (e.g., replay buffer sampling, interleaved training). Such consolidation avoids catastrophic forgetting of previous concepts when integrating new experiences.

Experimental results demonstrate that coupling AHNs with LTM achieves superior few-shot and continual learning, elevating one-shot accuracy and restoring long-term performance to near baseline after consolidation (Kowadlo et al., 2021).

5. Context Modulation and Multimodal Representation

Biological hippocampal circuits feature parallel processing pathways for contextual gating. In AHNs, context-sensitive biasing (analogous to EC direct input) enables dynamic shifting of processing regimes (Aimone et al., 2017):

$o(x) = f(Ax + B\hat{x})$

where $B$ encodes context-dependent biases.

Multimodal networks (e.g., CLIP) which jointly learn invariant features across visual, textual, and auditory input, better recapitulate hippocampal concept cells and multivoxel activity compared to unimodal counterparts (Choksi et al., 2021). Representational Similarity Analysis (RSA) demonstrates that multimodal architectures reach the noise ceiling in explaining fMRI hippocampal response, supporting their use in advanced AHNs.

6. Efficient Long-Context Modeling and Practical Implementations

Synthetic AHNs have been deployed for efficient long-sequence modeling in neural LLMs (Fang et al., 8 Oct 2025). By maintaining a lossless sliding window (short-term memory) and compressing out-of-window information into a fixed-size recurrent summary (long-term memory), architectures such as AHN-GatedDeltaNet enable scaling to much longer contexts while drastically reducing memory cache and FLOPs.

For instance:

$$h_{t-W} = \alpha(x_{t-W})(I - \beta(x_{t-W})k_{t-W}^\top k_{t-W})h_{t-W-1} + \beta(x_{t-W}) k_{t-W}^\top v_{t-W}$$

Models augmented with AHNs outperform sliding window baselines and achieve competitive scores with full-attention Transformers, with up to 74% memory reduction and 40% inference FLOPs savings on long-context LV-Eval benchmarks (Fang et al., 8 Oct 2025).

On neuromorphic hardware (SpiNNaker), spike-based AHNs achieve energy-efficient, real-time memory operations, learning in a few milliseconds per memory and recalling in six time steps (Casanueva-Morato et al., 2022, Casanueva-Morato et al., 2023).

7. Structural Organization, Temporal Dynamics, and Heterogeneity

AHNs may take inspiration from dynamic organizational features of the biological hippocampus and entorhinal cortex, namely liquid core–periphery structures and temporally heterogeneous connectivity (Pedreschi et al., 2020). Adaptive centrality, network state transitions, and switching “connectivity styles” (e.g., persistent core streamers versus bursty peripheral callers) promote robustness and flexible routing of information. Temporal measures (e.g., cosine similarity of weighted connections) serve as plausible feedback signals for self-organizing artificial systems.

Summary Table: AHN Features and Corresponding Biological Principles

AHN Attribute	Biological Analogue	Mathematical/Algorithmic Principles
Neurogenesis, Apoptosis	DG cell turnover	Structural plasticity, memory capacity regulation
Unified STDP + Homeostasis	Synaptic plasticity + inhibition	Multi-timescale learning, $\dot{W}^{ij}$ equations
Content-Addressability	CA3 attractor dynamics	All-to-all STDP, bidirectional cue/content recall
Pattern Separation & Completion	DG (separation), CA3 (completion)	Top-$k$ selection, Hopfield network
Episodic Encoding & Rapid Recall	Theta/gamma-modulated learning	Local credit assignment, one-shot training
Context Modulation	EC direct/indirect input	Context-sensitive bias, $o(x) = f(Ax + B\hat{x})$
Multimodal Representation	Concept cells, multimodal coding	Contrastive loss, RSA, multimodal network training
Efficient Compression (for LTM)	Memory consolidation, MSM	Recurrent summarization, AHN-GDN formulas
Temporal Heterogeneity & States	Core–periphery, network states	Coreness, liquidity, $\Theta^i(t)$ coefficients

Conclusion and Future Directions

Artificial Hippocampus Networks (AHNs) integrate structural plasticity, recurrent and spiking dynamics, pattern separation/completion, content-addressability, and multimodal/contextual coding to emulate functional principles of the biological hippocampus. Effective implementations span standard neural hardware, neuromorphic SNN platforms, and modern Transformer frameworks for long-context tasks. Ongoing research focuses on dynamic reconfiguration, continual learning, cross-modal generalization, and biologically faithful adaptation mechanisms for robust, energy-efficient and scalable memory systems.