13 papers · ranked by Valyu relevance
Maryada, Chiara De Luca, Arianna Rubino, Chenxi Wen + 4 more
Cortical microcircuits play a fundamental role in natural intelligence. While they inspired a wide range neural computation models and artificial intelligence algorithms, few attempts have been made to directly emulate them with an electronic computational substrate that uses the same physics of computation. Here we…
Michael L. Helde, Alexander G. Dimitrov
We adapted an olfactory neuromorphic algorithm to image and sound recognition. To achieve this, we carried out specific preprocessing procedures that were tailored to each modality. For images, we used the NIST digits dataset directly. For sound, we used samples from the Google Speech Command dataset. A gammatone…
Dmitrii Zendrikov, Sergio Solinas, Giacomo Indiveri
Neuromorphic processing systems implementing spiking neural networks with mixed signal analog/digital electronic circuits and/or memristive devices represent a promising technology for edge computing applications that require low power, low latency, and that cannot connect to the cloud for off-line processing, either…
Margot Wagner, Thomas M. Bartol, Terrence J. Sejnowski, Gert Cauwenberghs
Progress in computational neuroscience towards understanding brain function is challenged both by the complexity of molecular-scale electrochemical interactions at the level of individual neurons and synapses, and the dimensionality of network dynamics across the brain covering a vast range of spatial and temporal…
Maryada, Saray Soldado-Magraner, Martino Sorbaro, Rodrigo Laje + 2 more
Many neural computations emerge from self-sustained patterns of activity in recurrent neural circuits, which rely on balanced excitation and inhibition. Neuromorphic electronic circuits that use the physics of silicon to emulate neuronal dynamics represent a promising approach for implementing the brain’s computational…
Anna-Maria Jürgensen, Afshin Khalili, Elisabetta Chicca, Giacomo Indiveri + 1 more
Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing paradigm aims at the hardware implementation of neural network computations to support novel solutions for building brain-inspired computing systems. Here, we take inspiration from sensory processing in the nervous…
Ben Varkey Benjamin, Nicholas A Steinmetz, Nick N Oza, Jose J Aguayo + 1 more
A central challenge for systems neuroscience and artificial intelligence is to understand how cognitive behaviors arise from large, highly interconnected networks of neurons. Digital simulation is linking cognitive behavior to neural activity to bridge this gap in our understanding at great expense in time and…
Hongwei Cai, Zheng Ao, Chunhui Tian, Zhuhao Wu + 5 more
Brain-inspired hardware emulates the structure and working principles of a biological brain and may address the hardware bottleneck for fast-growing artificial intelligence (AI). Current brain-inspired silicon chips are promising but still limit their power to fully mimic brain function for AI computing. Here, we…
James C Knight, Thomas Nowotny
Large-scale simulations of spiking neural network models are an important tool for improving our understanding of the dynamics and ultimately the function of brains. However, even small mammals such as mice have on the order of 1 × 10^12^ synaptic connections which, in simulations, are each typically charaterized by at…
Alexander James White, Belle Liu, Ming-Ju Hsieh, Meng-Fan Chang + 2 more
Biological neural circuits at various levels exhibit rapid adaptability to diverse environmental stimuli. Such fast response times imply that adaptation cannot rely solely on synaptic plasticity, which operates on a much slower timescale. Instead, circuits must be inherently hyper-flexible and receptive to switches in…
Romain Cazé, Marcel Stimberg
In theory, neurons can compute all threshold functions, but in practice, synaptic weight resolution limits their computing capacity. Here, we study how dendrites alleviate this practical limitation by demonstrating a computation where dendrites considerably decrease the necessary synaptic weights resolution. We show…
Frithjof Gressmann, Ivan Georgiev Raikov, Hau Ngoc Pham, Evan Coats + 2 more
The investigation of cultured biological neural networks is a critical frontier in neuroscience with profound implications for the advancement of brain-machine interfaces, treatments of neurological diseases, and fundamental insights into neural computation and cognition. Advances in induced pluripotent stem cell…
Xiaotian Zhang, Zhi Dou, Seung-Hyun Kim, Gaurav Upadhyay + 16 more
Motivated by the unexplored potential of in vitro neural systems for computing, and by the corresponding need of versatile, scalable interfaces for multimodal interaction, we present an accurate, modular, fully customizable and portable recording/stimulation solution that can be easily fabricated, robustly operated…