Advanced Memory and Neural Computing

Evolutionary and spike-timing-dependent reinforcement learning train spiking neuronal network motor control

This study compares spike-timing-dependent reinforcement learning and evolutionary strategy for training spiking neuronal networks to solve a reinforcement learning control task.

A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain

This paper develops a neuromorphic model of olfactory processing in Drosophila larvae, showing how sparse coding and efficient encoding can be achieved through feedback inhibition and spike frequency adaptation in a small neural network.

Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks

This paper introduces adaptive spiking recurrent neural networks with a novel surrogate gradient, achieving state-of-the-art performance in time-domain tasks and improved computational efficiency.

Towards on-chip real-time classification of extra-cellular neural recordings

This paper presents a feasibility study for real-time classification of extra-cellular neural recordings using low-power VLSI systems and spike-based neuromorphic circuits.

Spike-based symbolic computations on bit strings and numbers

This paper reviews spike-based symbolic computations in neural networks and shows small spiking networks can perform symbolic computations on bit sequences and numbers using biologically plausible learning rules.

Neurogrid simulates cortical cell-types, active dendrites, and top-down attention

Neurogrid simulates cortical models across multiple levels, revealing active dendrites mediate top-down attention.

A memristor-based neuromodulation device for real-time monitoring and adaptive control of in vitro neuronal populations

This paper introduces a memristor-based neuromodulation device that enables real-time adaptive control of in vitro neuronal populations by detecting specific activity patterns and modulating synaptic-like interactions.

Real-time detection of bursts in neuronal cultures using a Neuromorphic Auditory Sensor and Spiking Neural Networks

This paper proposes a neuromorphic system for real-time burst detection in neuronal cultures, achieving comparable performance to conventional methods with lower power and latency.

Integrating Non-Spiking Interneurons in Spiking Neural Networks

This paper combines spiking and non-spiking neurons to create a sensorimotor pathway that shapes network output based on analog input, validated through simulation.

CloudBrain: Online neural computation in the cloud

CloudBrain provides an open-source cloud-based platform for neuromorphic computing that enables real-time simulation and online reconfiguration of neural networks.

The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks

This paper investigates how surrogate gradient learning performs in spiking neural networks, showing its robustness to different surrogate derivatives and the importance of regularization for effective learning.

A solution to the learning dilemma for recurrent networks of spiking neurons

This paper introduces a new learning method for spiking neural networks that enables biologically plausible online learning through gradient descent, achieving performance comparable to BPTT.

One-shot learning with spiking neural networks

This paper introduces a new learning approach for spiking neural networks that enables one-shot learning through local synaptic plasticity, inspired by dopamine-based learning signals in the brain.

A Spiking Neuron and Population Model based on the Growth Transform Dynamical System

This paper introduces a spiking neuron and population model based on Growth Transform dynamical systems, enabling independent control of neurodynamical properties and efficient spiking associative memory.

Dendrites decrease the synaptic weight resolution necessary to implement linearly separable computations

This paper shows dendrites allow neurons to perform complex computations with less precise synaptic weights, improving efficiency in both biological and artificial systems.

Toward One-Shot Learning in Neuroscience-Inspired Deep Spiking Neural Networks

This paper develops a spiking neural network that learns hand-written digits from few examples, inspired by neuroscience mechanisms, and achieves efficient learning with minimal training data.

Back-propagation learning in deep Spike-By-Spike networks

This paper develops a learning rule for deep Spike-by-Spike networks and demonstrates its effectiveness on MNIST classification with 99.3% accuracy.

Massively Parallel FPGA Hardware for Spike-By-Spike Networks

The paper develops FPGA hardware for Spike-By-Spike networks, demonstrating efficient parallelization and optimization for spiking neural network computations.

Biologically plausible learning in a deep recurrent spiking network

This paper introduces a framework of spiking neurons with local learning rules that achieve performance comparable to artificial deep networks in image classification.

A Coupled Network of Growth Transform Neurons for Spike-Encoded Auditory Feature Extraction

This paper introduces a novel spiking neuron model called the Growth Transform (GT) neuron and demonstrates its application for spike-based auditory signal processing, showing stable and interpretable network dynamics.