We are seeking a highly skilled researcher with a PhD in electrical or electronics engineer/computer science engineer/physicist with experience in areas such as analog or digital circuit design, embedded systems, systems on chip, FPGA programming or artificial intelligence. The candidate will join the staff of the Institute of Microelectronics of Seville (IMSE), an academic research center belonging to the University of Seville and the Spanish Research Council (CSIC). IMSE is equipped with state-of-the-art infrastructures hosting 1.000m^2 of laboratories for the design and test of electronic circuits and opto-electronic sensors. A new clean room facility for advanced integrated circuits packaging and additive manufacturing is currently being set up. It is located in a technological park at 15 minutes walking from Sevilla city center. The candidate is sought to join the Neuromorphic Systems Group, which has over 30 years of experience in the field of neuromorphic hardware systems and applications, including the development of spatial contrast retinas, dynamic vision sensors, convolutional neural processors, spiking convolutional neural networks, spiking learning circuits and algorithms, and spiking neural processors combining conventional CMOS circuits with nanodevices.

A Postdoctoral Research Associates in Neuromorphic Systems and/or Computational Neuroscience for robotics is required for a period of 3.5 years to work on the Horizon/InnovateUK project “PRIMI: Performance in Robots Interaction via Mental Imagery. This is a collaborative project of the University of Manchester’s Cognitive Robotics Lab with various academic and industry partners in the UK and Europe. PRIMI will synergistically combine research and development in neurophysiology, psychology, machine intelligence, cognitive mechatronics, neuromorphic engineering, and humanoid robotics to build developmental models of higher-cognition abilities – mental imagery, abstract reasoning, and theory of mind – boosted by energy-efficient event-driven computing and sensing. You will carry out research on the design of neuromorphic systems models for robotics. The postdoc will work collaboratively with the other postdocs and PhD students in the PRIMI project. This post requires expertise in computational neuroscience (e.g. spiking neural networks) for robotics and/or neuromorphic systems.

Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plasticity is believed to play a pivotal role in biological memory, it has so far been analyzed mostly in the context of pattern completion and unsupervised learning. Here, we propose that Hebbian plasticity is fundamental for computations in biological neural systems. We introduce a novel spiking neural network (SNN) architecture that is enriched by Hebbian synaptic plasticity. We experimentally show that our memory-equipped SNN model outperforms state-of-the-art deep learning mechanisms in a sequential pattern-memorization task, as well as demonstrate superior out-of-distribution generalization capabilities compared to these models. We further show that our model can be successfully applied to one-shot learning and classification of handwritten characters, improving over the state-of-the-art SNN model. We also demonstrate the capability of our model to learn associations for audio to image synthesis from spoken and handwritten digits. Our SNN model further presents a novel solution to a variety of cognitive question answering tasks from a standard benchmark, achieving comparable performance to both memory-augmented ANN and SNN-based state-of-the-art solutions to this problem. Finally we demonstrate that our model is able to learn from rewards on an episodic reinforcement learning task and attain near-optimal strategy on a memory-based card game. Hence, our results show that Hebbian enrichment renders spiking neural networks surprisingly versatile in terms of their computational as well as learning capabilities. Since local Hebbian plasticity can easily be implemented in neuromorphic hardware, this also suggests that powerful cognitive neuromorphic systems can be build based on this principle.

Current SNNs studies frequently ignore dendrites, the thin membranous extensions of biological neurons that receive and preprocess nearly all synaptic inputs in the brain. However, decades of experimental and theoretical research suggest that dendrites possess compelling computational capabilities that greatly influence neuronal and circuit functions. Notably, standard point-neuron networks cannot adequately capture most hallmark dendritic properties. Meanwhile, biophysically detailed neuron models are impractical for large-network simulations due to their complexity, and high computational cost. For this reason, we introduce Dendrify, a new theoretical framework combined with an open-source Python package (compatible with Brian2) that facilitates the development of bioinspired SNNs. Dendrify, through simple commands, can generate reduced compartmental neuron models with simplified yet biologically relevant dendritic and synaptic integrative properties. Such models strike a good balance between flexibility, performance, and biological accuracy, allowing us to explore dendritic contributions to network-level functions while paving the way for developing more realistic neuromorphic systems.