12 research outputs found
Plasticity and Adaptation in Neuromorphic Biohybrid Systems
Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel \u201cbiohybrid\u201d experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering. \ua9 2020 The Author(s
Harnessing function from form: towards bio-inspired artificial intelligence in neuronal substrates
Despite the recent success of deep learning, the mammalian brain is still unrivaled when it comes
to interpreting complex, high-dimensional data streams like visual, auditory and somatosensory stimuli.
However, the underlying computational principles allowing the brain to deal with unreliable, high-dimensional
and often incomplete data while having a power consumption on the order of a few watt are still mostly
unknown.
In this work, we investigate how specific functionalities emerge from simple structures observed in the
mammalian cortex, and how these might be utilized in non-von Neumann devices like “neuromorphic
hardware”. Firstly, we show that an ensemble of deterministic, spiking neural networks can be shaped by
a simple, local learning rule to perform sampling-based Bayesian inference. This suggests a coding scheme
where spikes (or “action potentials”) represent samples of a posterior distribution, constrained by sensory
input, without the need for any source of stochasticity. Secondly, we introduce a top-down framework where
neuronal and synaptic dynamics are derived using a least action principle and gradient-based minimization.
Combined, neurosynaptic dynamics approximate real-time error backpropagation, mappable to mechanistic
components of cortical networks, whose dynamics can again be described within the proposed framework.
The presented models narrow the gap between well-defined, functional algorithms and their biophysical
implementation, improving our understanding of the computational principles the brain might employ.
Furthermore, such models are naturally translated to hardware mimicking the vastly parallel neural
structure of the brain, promising a strongly accelerated and energy-efficient implementation of powerful
learning and inference algorithms, which we demonstrate for the physical model system “BrainScaleS–1”