Three Levels of Biological Inspiration: Bridging NeuroAI and Neuromorphic Engineering

A neuromorphic engineer’s perspective from Cosyne 2026

I had the fantastic opportunity to speak at Cosyne 2026, in the workshop on *Biologically-Inspired Artificial Intelligence*. Cosyne is one of the leading conferences in (computational) neuroscience, and it was my first time attending it. The community is creative, welcoming, and genuinely curious about the deep questions at the intersection of biology and computation. Being an engineer surrounded by computational neuroscientists for a few days was truly inspiring.

What Role Does Neuroscience Play in the Age of AI?

It is hard to ignore that AI is advancing at a pace that would have seemed implausible just a few years ago. Large language models are reshaping the concept of intelligence and, notably, do not look much like brains. Backpropagation through deep networks, attention mechanisms, and massive parallelism on GPU clusters are a long way from local plasticity, spike-timing-dependent learning, and the roughly 20 watts of power consumed by the human brain.

So what role does neuroscience play in this context? Is biological inspiration still a useful guide for building intelligent systems, or has it become more of an aesthetic preference?

As a neuromorphic engineer, I feel this growing tension. Neuromorphic computing has historically drawn deep inspiration from biology, often at the level of mimicking individual neurons and synapses. But as the gap between neuromorphic hardware and state-of-the-art AI grows wider, it becomes worth asking: Are we drawing the right kind of inspiration?

Three Levels of Biological Inspiration

I find it useful to distinguish between three levels, which map onto different parts of the stack of engineering intelligence — from devices and circuits all the way up to applications and algorithms.

Courtesy of Melika Payvand

1. Mechanical Level

The mechanical level is about faithfully mimicking the biophysics of neural computation: the dynamics of individual neurons, synapses, axons, and dendrites. This is where traditional neuromorphic engineering has lived, designing circuits that reproduce the biophysical dynamics of real neurons, memristive devices that implement synaptic plasticity, or architectures that implement dendritic compartments.

The mechanical level asks: what of the complex neuronal biophysics is useful for computation and how?

2. System Level

The system level takes a step up, adopting the “mechanisms” of machine learning — artificial neural networks and backpropagation — but inject biological system-level features: heterogeneity across neurons, functional specialization of different brain areas, sparse connectivity, modularity, or principles like neurogenesis. It’s not trying to replicate the biophysics, but rather making the hypothesis that the organisational principles of biological neural systems can make artificial ones better.

The system level asks: can biological organizational principles improve the function of artificial networks?

3. Behavioral Level

The behavioral level draws inspiration from how brains act, rather than how they are built. This is closest to classical AI: designing systems that exhibit intelligent behaviors such as perception, decision-making, and creativity, observed in animals, without necessarily caring about the underlying substrate.

Notably, this level is as much about sensing as it is about computation: behavior, after all, emerges from the interaction with an environment. This makes behavioral inspiration particularly relevant for embodied AI, though not exclusively: systems like large language models draw on behavioral inspiration while operating purely in the symbolic domain.

Where Neuromorphic and NeuroAI Currently Live

Neuromorphic engineering has traditionally operated at the mechanical level, while NeuroAI has worked mainly between the system and behavioral levels. But I believe both fields have much to gain from moving across this hierarchy, forming a synergy.

This is also the direction my lab — the Emergent Intelligent Substrates (EIS) Lab, led by Prof. Melika Payvand — has been pursuing.

What We Work On

Let me show you what this looks like in practice across two of these levels.

Mechanical Level: Dendritic Delays

Dendrites exhibit a rich repertoire of computational primitives, one of which is the propagation delay of pre-synaptic stimuli as they travel toward the soma. This delay is a form of temporal memory built directly into the neuron's morphology.

In DenRAM (D’Agostino, Moro et al., Nature Communications 2024), we built a neuromorphic hardware architecture that uses Resistive RAM (RRAM) to implement dendritic delays with ultra-low power consumption. Delay-based spiking networks turn out to be significantly more efficient than recurrent models: we demonstrated a 5× reduction in power and up to a 35× reduction in memory footprint on ECG anomaly-detection and keyword-spotting benchmarks.

DenRAM mimics dendritic arbors with synaptic elements that delay and weigh input spikes, converging to an output Leaky-Integrate-and-Fire neuron

One limitation of DenRAM is that it does not train the delays explicitly. This motivated DelGrad (Göltz, Weber, Kriener et al., Nature Communications 2025), an algorithm that derives exact, event-based gradients for both synaptic weights and delays in spiking networks. DelGrad is compatible with LIF neurons and has been validated on neuromorphic hardware (BrainScaleS), where training axonal delays reduces test error meaningfully even under the noise of analog hardware.

Together, DenRAM and DelGrad make the case that the mechanical richness of dendrites — specifically, their temporal structure — is a powerful computational resource.

System Level: Heterogeneous Memory, Temporal Hierarchy, and Neurogenesis

Moving up the hierarchy, we have been working on how biological system-level principles can improve artificial networks trained with backpropagation.

In the system-level of biological inspiration, we leverage machine-learning computational mechanisms in conjunction with high-level features of biological computations

mGRADE (Torchet et al., arXiv 2025) combines two ideas: the minimal gated recurrent unit (minGRU) for processing slow temporal dynamics, and dilated convolutions with learnable spacings (DCLS) for capturing fast temporal features. The result is a deep recurrent network with heterogeneous memory— inspired by the fact that brains exhibit heterogeneous strategies for memory formation. mGRADE achieves state-of-the-art performance on the Long Range Arena benchmark and on raw-audio keyword spotting, while being the only architecture in its class small enough to fit on common microcontrollers.

Temporal Hierarchy in SNNs (Moro et al., arXiv 2024) investigates whether the hierarchy of intrinsic timescales observed in the mammalian cortex — where deeper cortical areas process information over longer timescales — is also useful as an inductive bias in artificial spiking networks. The answer is yes: imposing a structured gradient of time constants across hidden layers consistently improves performance on temporal tasks, and interestingly, this hierarchy also emerges spontaneously from optimization when networks are trained on sequential data.

GroHess takes inspiration from adult hippocampal neurogenesis — the brain’s ability to grow new neurons as a mechanism for continual learning — and implements a biologically-motivated artificial counterpart. Using information-geometry-based triggers (effective dimensionality and Fisher saturation metrics), the algorithm decides when and where to grow new neurons, and initialises them orthogonally to the existing representations to minimise interference. On both Split MNIST and Permuted MNIST, GroHess achieves better performance with smaller final models than static baselines.

Looking Ahead: A Synergy Worth Pursuing

I believe the future of both fields lies in their convergence.

NeuroAI would benefit from engaging more seriously with neuromorphic hardware. Energy-efficient substrates impose structure that may turn out to be a feature rather than a limitation: sparse connectivity, modular networks, and local learning rules are all biologically motivated and increasingly attractive from an engineering standpoint.

Neuromorphic engineering should break free from its focus on the mechanical level. The most impactful near-term opportunities may lie at the system level: using the organizational principles of biological brains — functional modularity, sparsity, hierarchy of time-scales, heterogeneous dynamics— to build systems that are simultaneously more efficient and more functional.

The next step is to build systems that are neuromorphic in substrate, biologically-organized at the system-level, while leveraging the powerful computational tools of machine learning. I think we are closer to that than it might seem.

Filippo Moro is a Postdoc in the EIS Lab at UZH and ETH Zurich, led by Prof. Melika Payvand. His research sits at the intersection of neuromorphic hardware, spiking neural networks, and biologically-inspired machine learning.