Biophysical Modelling

This subtopic is concerned with the development of cellular-resolution computational models of the brain combining biological realism with learning and task performance. A particular aim was to gain insight into visuomotor processing through models of multiple brain areas including the cerebellum, basal ganglia, cerebral cortex, hippocampus, and other intercalated nuclei. Aspects taken into account encompassed spiking communication, cell-type-specific electrophysiology and connectivity, spike-timing-dependent plasticity, and dopamine-dependent reinforcement learning. These models formed a bottom-up complement to the top-down approach taken elsewhere within the focus area, and have suggested mechanisms for cognitive architectures more closely based on known anatomy and physiology. Besides the neuroscientific knowledge gained from these models, they constitute testbeds for various EBRAINS services: simulation, high-performance computing, neuromorphic computing, and neurorobotics.

Activation of a vertical column of the cerebellar cortex (as shown in Schepper et al, 2021)

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Uncovering new learning rules

Intelligence is our ability to learn appropriate responses to new stimuli and situations. During learning, neurons in association cortices become tuned to relevant features and start to represent them with persistent activity during memory delays. Scientists from various groups in the HBP developed biologically plausible learning rules that explain how trial-and-error learning induces neuronal selectivity and working memory representations for task-relevant information. They examine the role of attention, synaptic tags, feedback connections and neuromodulators in learning, examining biologically plausible learning rules endowing neural networks to exhibit the same learning capacity and flexibility as the human brain. These projects addressed how neurons in the association cortex and subcortical structures transform sensory signals into activity patterns in the motor cortex that can guide optimal behaviour. Another hallmark of intelligence is to maintain and organise experiences in episodic memory, and to use these experiences for solving problems and for creating predictions of sensory inputs. Therefore, researchers worked on uncovering learning rules and spiking neural network architectures which can imagine possible consequences of taking a particular action, among other notable work. Intelligence is also characterised by the flexibility and adaptability of the responses to stimuli and situations. In line with this, biologically plausible learning rules and architectures were developed for solving multi-step visual tasks requiring the flexible composition of different subtasks, as well as learning-to-learn tasks requiring the learning of an overarching rule applied independently of the stimuli presented.

Biologically plausible implementation of natural-gradient-based plasticity for spiking neurons which renders learning invariant under dendritic transformations (Kreutzer, E., et al., 2022)

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Neuromorphic Computing

Physical neural networks represent one of the most promising venues for realising massively parallel, energy-efficient information processing with imperfect and noisy components. Our research focused on two aspects of neuromorphic computing. On the one hand, project partners developed applications for neuromorphic devices, with demonstrated ability to push the boundaries of state of the art in terms of combined performance and efficiency. On the other hand, they actively pursued the development of novel neuromorphic hardware and software that incorporate important functional components of our biologically inspired models, including modified neuronal firing mechanisms, particular types of neuronal morphology and various aspects of synaptic dynamics.

The BrainScaleS chip with false-colour overlay of some of its components

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Neuromorphic Sensing

Complementary to neuromorphic computing, scientists at the Royal Institute of Technology in Stockholm worked on event cameras that mimiced the working principle of the human eye allowing low-latency and low-power perception in challenging real-time real-world settings. This research combined such event cameras with neuromorphic computing systems to provide perception for low-power always-on sensing on wearable devices, seamless human-machine interaction, or enable autonomous robots to interact in a world made for humans. Furthermore, researchers at the University of Hertfordshire explored circuits and processes for sensing in the olfactory system and translated them into algorithms running on the HBP Neuromorphic Platform, combined with devices for electronic gas sensing. The aim was to enable gas-based navigation for robots and environmental monitoring at the edge.    

The dynamic vision sensor setup allowing control of a robotic arm

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Cognitive Neurorobotics

Can we develop robots that have human-like perception, action and reasoning? This subtopic investigates the development of digital brains to control robots and artificial systems, and at the same time, it gives tools to validate current computational neuroscience theories. It focuses on areas, such as: perception and adaptive control (e.g., safe human-robot collaboration), robust local learning, and neurosymbolic planning. For instance, we investigate functional cognitive models for model predictive control with spiking neural networks based on predictive coding approaches, and we study forms of representation learning that incorporate inductive biases, such as objects, to allow abstract planning.  

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