Venue: Centre Broca
Hayder Amin, PhD
Biohybrid Neuroelectronics (BIONICS) Group, German Center for Neurodegenerative Diseases (DZNE)
Invited by Muriel Koehl (Magendie)
Title
Multiscale Computation and the Logic of Neural Ensembles Across States
Abstract
The computational power of the brain emerges from the coordinated activity of neuronal ensembles
embedded in complex, multilayered circuits. While single-neuron approaches have provided
foundational insights, they fail to capture the distributed, dynamic structure of computation that
unfolds across spatial and temporal scales. This is particularly true in circuits such as the hippocampus
and olfactory bulb, where plasticity, sensory integration, and experience-dependent reconfiguration
occur on a systems level.
In this talk, I will present our strategy to decode circuit-level computation using custom-engineered
CMOS-based neuroelectronic platforms. These systems enable large-scale, high-resolution recordings
that resolve cell assembly dynamics, track long-term potentiation across layers, and capture
experience-induced network-level plasticity. This multiscale approach allows us to go beyond the
single-cell paradigm and examine the logic of computation embedded in functional ensembles and
circuit topology.
Importantly, we apply this framework to dissect how experience, stress, aging, and disease reshape
neural complexity — revealing distinct, circuit-specific alterations in spatiotemporal dynamics. These
changes expose candidate biomarkers that reflect disruptions in information flow, plasticity, or
ensemble coordination. By linking computation, architecture, and dysfunction, our work provides a
scalable foundation for understanding how neural systems adapt — or fail — across physiological and
pathological states.
Beyond fundamental neuroscience, our research establishes a scalable foundation for advancing
neurotechnology and brain-inspired AI. By combining high-resolution experimental access to circuit
dynamics with computational modeling of multiscale activity patterns, our approach enables more
biologically grounded theories of learning, memory, and adaptive computation. These insights inform
the design of next-generation interfaces, synthetic neural systems, and data-driven frameworks that
move toward functional emulation of brain-like complexity in both health and engineered systems.