2026· IEEE Transactions on Molecular Biological and Multi-Scale Communications· Vol 12, pp. 931-936· 1 citation· 22 references
Abstract
Bioengineered neuronal systems are increasingly explored as living computational substrates, yet their end-to-end communication properties remain poorly characterized beyond global activity statistics. We model a mechanosensitive spiking neuronal network as a noisy multiple-input multiple-output (MIMO) communication system to quantify how topology and substrate mechanics shape information transfer and computation. Using a mechanosensitive Izhikevich network with time-varying stiffness, we compare random, clustered, and disintegrated topologies under matched stimulation and observation noise. Communication is quantified using transfer entropy, achievable mutual information rate from binned spike observations, and information redundancy. Computation is evaluated using a receiver-level separability metric posed as a transmitter-identification task. Across noise levels, clustered networks more consistently maintain higher achievable rates and exhibit more concentrated transmitter–receiver separability than random and disintegrated substrates. These results show that topology and mechanical state jointly regulate communication and computational separability in bioengineered neuronal networks, providing design-relevant metrics for bioengineered intelligence systems.
This work proposes heterogeneous neural networks that combine spiking neural networks (SNNs) and artificial neural networks (ANNs) at bandwidth-limited regions, such as chip boundaries, where spike-based communication reduces data transfer overhead.
Joshua Nardone, Rui-Jie Zhu, Ruhai Lin et al.· International Conference on...· 0 citations
Brains achieve extraordinary efficiency in processing temporal information through dense interconnectivity and recurrent feedback among neurons. Inspired by this principle, we introduce neuromorphic tissues—soft biomolecular networks comprising cell-sized aqueous compartments interconnected by lipid membranes containing voltage-gated ion channels. When a compartment is electrically stimulated by current injection, the membranes separating it from neighboring compartments polarize until channel activation occurs, transiently transforming the interface into a conductive synapse that couples adjacent nodes. These dynamics generate intrinsic physical recurrence, enabling the network to encode, propagate, and reconstruct time-dependent signals without external feedback circuitry. Experiments and modeling demonstrate nonlinear, fading-memory, and recurrent dynamics characteristic of reservoir computing, enabling accurate prediction of nonlinear and chaotic sequences such as NARMA-10 and the Lorenz attractor. This work suggests that spatial interconnectivity can enhance the computational capabilities of physical reservoirs and highlights soft, self-assembled materials as a promising platform for implementing such interconnected systems.
N. Armendarez, A. Mohamed, M. Hasan et al.· Science Advances· 0 citations
Biological neural systems achieve high efficiency and robustness through compartmentalized architectures. In contrast, modern artificial neural networks rely on globally entangled structures, which obscure decision logic and suffer from catastrophic forgetting. Here, we report a Decomposable Spiking Neural Network (D-SNN) that eliminates global synaptic entanglement by structurally isolating classification pathways into independent experts. Optimized via a bio-inspired push-pull loss function, the D-SNN achieves competitive accuracies on MNIST, Fashion-MNIST, and CIFAR-10/100 benchmarks. This modular approach matches the performance of fully dense networks while utilizing an order of magnitude fewer parameters. In addition, our networks operate with up to several orders of magnitude lower firing rates and fewer synaptic operations. Furthermore, physically severing connections between experts provides inherent protection against catastrophic forgetting during sequential learning. Crucially, these isolated pathways generate auditable neural signals, increasing decision transparency. This biomimetic, verifiable architecture establishes an efficient foundation for deploying deterministic neuromorphic intelligence in resource-constrained edge environments.
Maksim Bazhenov, S. Grubas, V. Putkaradze· 0 citations
Neuromorphic computing is closely associated with spiking neuronal networks. However, an alternative class of so-called"rate-based"models arising from computational neuroscience and machine learning forgoes spiking interactions and instead relies on continuous coupling between neurons. Existing neuromorphic implementations designed around spike-based interactions are not well-suited for emulating such models. Here view the distributed simulation of these models as message-passing algorithms on parallel hardware. Leveraging prior art in numerical algorithms and distributed simulation, we outline steps that enable the design of efficient digital neuromorphic accelerators for non-spiking neuronal models. In particular, we show that multi-bit packets, rather than spikes, are the most efficient communication strategy in packet-switched networks and that compared to basic numerical integration methods, higher-order differential equation solvers decrease both computation and communication costs while achieving lower numerical error, but that these benefits are ultimately limited by arithmetic precision. Using our proposed design principles, we convert an existing neuromorphic architecture into a distributed numerical solver - a spikeless neuromorphic system - for continuously-coupled neuronal models. We thereby demonstrate that our theoretical considerations indeed translate into practical advantages, namely reduced energy consumption and delay.
Jakob Jordan, Ole Richter, Cong Li et al.· 0 citations
The brain can rapidly adjust sensory processing according to behavioral context from moment to moment without altering its underlying anatomical wiring. Such flexibility is thought to arise from dynamic reconfiguration of the effective network through which signals propagate, yet the principles governing such reconfiguration and its consequences for coding remain unclear. Here, we exploited the distinct stationary and locomotion states within the same recording sessions and addressed this question by inferring directed, millisecond-scale signal-transmission networks at single-neuron resolution in behaving mice. Locomotion was accompanied by a counterintuitive structured sparsification of the inferred network: interactions became fewer but more temporally precise, while the remaining interactions were more local, modular, feature-specific, and feedforward. We then used theoretical analysis and controlled perturbations of multi-area rate models to systematically determine how each empirically observed component of this reorganization affects population coding. We found that sparsification reduced shared variability; local organization reduced signal–noise alignment; feature-specific interactions sharpened selectivity; and a more feedforward architecture accelerated decoding. These results provide an experimentally grounded mechanism by which behavioral state reorganizes neuronal interactions given the same sensory inputs, and suggest structured sparsification as an underlying principle of network reconfiguration that supports accurate and faster sensory coding.
Brains are often described as cost-efficient communication networks that optimally balance long-connection costs against fast communication. Inspired by the “use it or lose it” principle, we present a game-theoretic model of self-organizing neural units showing the brain is suboptimal in both regards. Regional competition for connectivity under propagative dynamics yields networks resembling the human cortex yet more efficient and economical. In addition, using a reservoir computing framework, we find comparable information processing capacity, but synthetic optimal communication networks show lower computational reliability. Last, virtual lesions reveal why these networks are fragile: To optimize communication, they funnel information through a spatially clustered “oligarchy” of transmodal hubs. The human brain instead uses a distributed “rich-club” backbone that better resists targeted attacks, despite higher wiring costs and less efficient communication. Cortical networks thus trade both cost and efficiency for reliable computation, highlighting computational reliability as an overlooked and perhaps even more prominent driver of brain connectivity than wiring cost or communication efficiency.
Kayson Fakhar, Danyal Akarca, Andrea I. Luppi et al.· Science Advances· 1 citation
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