AIMC Topic: Neurons

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Desynchronous learning in a physics-driven learning network.

The Journal of chemical physics
In a neuron network, synapses update individually using local information, allowing for entirely decentralized learning. In contrast, elements in an artificial neural network are typically updated simultaneously using a central processor. Here, we in...

Learning to represent continuous variables in heterogeneous neural networks.

Cell reports
Animals must monitor continuous variables such as position or head direction. Manifold attractor networks-which enable a continuum of persistent neuronal states-provide a key framework to explain this monitoring ability. Neural networks with symmetri...

Channel response-aware photonic neural network accelerators for high-speed inference through bandwidth-limited optics.

Optics express
Photonic neural network accelerators (PNNAs) have been lately brought into the spotlight as a new class of custom hardware that can leverage the maturity of photonic integration towards addressing the low-energy and computational power requirements o...

Adaptive Learning Neural Network Method for Solving Time-Fractional Diffusion Equations.

Neural computation
A neural network method for solving fractional diffusion equations is presented in this letter. An adaptive gradient descent method is proposed to minimize energy functions. Due to the memory effects of the fractional calculus, the gradient of energy...

Leveraging Spiking Deep Neural Networks to Understand the Neural Mechanisms Underlying Selective Attention.

Journal of cognitive neuroscience
Spatial attention enhances sensory processing of goal-relevant information and improves perceptual sensitivity. Yet, the specific neural mechanisms underlying the effects of spatial attention on performance are still contested. Here, we examine diffe...

Photonic reservoir computer based on frequency multiplexing.

Optics letters
Reservoir computing is a brain-inspired approach for information processing, well suited to analog implementations. We report a photonic implementation of a reservoir computer that exploits frequency domain multiplexing to encode neuron states. The s...

Coherent oscillations in balanced neural networks driven by endogenous fluctuations.

Chaos (Woodbury, N.Y.)
We present a detailed analysis of the dynamical regimes observed in a balanced network of identical quadratic integrate-and-fire neurons with sparse connectivity for homogeneous and heterogeneous in-degree distributions. Depending on the parameter va...

Surrogate gradients for analog neuromorphic computing.

Proceedings of the National Academy of Sciences of the United States of America
To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum, but communicate with spikes, binary events in time. Analog neuromorphic hardware uses the same principles to emulate spiking neural...

A connectivity-constrained computational account of topographic organization in primate high-level visual cortex.

Proceedings of the National Academy of Sciences of the United States of America
Inferotemporal (IT) cortex in humans and other primates is topographically organized, containing multiple hierarchically organized areas selective for particular domains, such as faces and scenes. This organization is commonly viewed in terms of evol...

Scan-less machine-learning-enabled incoherent microscopy for minimally-invasive deep-brain imaging.

Optics express
Deep-brain microscopy is strongly limited by the size of the imaging probe, both in terms of achievable resolution and potential trauma due to surgery. Here, we show that a segment of an ultra-thin multi-mode fiber (cannula) can replace the bulky mic...