ICNCE 2026

Abstract: While artificial neural networks represent a highly successful mapping from neuroscience AI, and clearly capture important aspects neuronal information processing, data from experimental neuroscience strongly suggests that the ANN abstraction omits important biological computational principles. This includes such features as the pulsed nature of neural communication, the...

Abstract: Deep neural networks (DNNs) outperform cortical network models in behavioural performance. DNNs also outperform cortical neuron models in terms of their statistical response properties. Yet, the alignment of DNNs with cortical circuits, cortical structures, and synaptic plasticity is rather limited. I present a functional bottom-up model of cortical pyramidal neurons and cortical...

Abstract: The demand for accurate perception of the physical world leads to a dramatic increase in sensory nodes. However, the transmission of massive and unstructured sensory data from sensors to computing units poses great challenges in terms of power‐efficiency, transmission bandwidth, data storage, time latency, and security. To efficiently process massive sensory data, it is crucial...

Abstract: Biological synapses do more than change their strength: they also regulate how changeable they should remain. This “plasticity of plasticity,” or metaplasticity, is thought to help neural systems balance memory retention with flexibility. While the underlying biological mechanisms remain only partly understood, this idea offers a powerful inspiration for neuromorphic AI: a useful...

Abstract: Neuromorphic devices are transforming healthcare by enabling innovative, low- power, and efficient solutions for biomedical applications. Inspired by the neural architecture and computational principles of the human brain, these systems are particularly suited for wearable and implantable technologies capable of real-time, closed-loop interaction with biological tissue while...