Advanced Memory and Neural Computing

Metabolic Cost Asymmetry as a Structural Regulariser for Recurrent Spiking Neural Networks

This paper introduces a metabolic cost asymmetry as a structural regularizer for recurrent spiking neural networks, showing it improves performance and reduces energy use.

Low-latency neuromorphic closed-loop control of hippocampal ripples in vivo

This paper develops a neuromorphic framework for real-time detection and manipulation of hippocampal ripples in vivo, achieving low-latency and energy-efficient performance.

Equilibrium Propagation with Predictive Learning in Leaky Integrate-and-Fire Spiking Neural Networks

This paper proposes an EP-based SNN framework with predictive learning in LIF neurons, showing competitive accuracy on image classification tasks while using a biologically motivated learning rule.

Predictive E-prop: A biologically inspired approach to train predictive coding-based recurrent spiking neural networks

This paper introduces Predictive E-prop, a biologically inspired model that trains recurrent spiking neural networks using gradient-based prediction error minimization, achieving self-supervised learning of complex time-series signals.

Direct Training of Networks of Morris-Lecar Neurons withBackprop

This paper trains biophysically detailed spiking neural networks using Morris-Lecar neurons and differentiable synapses, showing that they can be effectively trained with backpropagation for various tasks.

Voltage-dependent reversal potentials in spiking recurrent neural networks enhance energy efficiency and task performance

Neuromodulation enhances the capability and efficiency of spiking neural networks

This study shows neuromodulation improves spiking neural network performance and efficiency in sensory tasks with minimal resource use.

Efficient learning and intrinsic noise filtering in recurrent spiking neural networks trained with e-prop

This study examines how e-prop learning performs in recurrent spiking neural networks under varying connectivity and noise levels, finding that sparsity improves learning by enhancing activation diversity.

A canonical cortical electronic circuit for neuromorphic intelligence

This paper presents a neuromorphic electronic circuit architecture inspired by cortical microcircuits, demonstrating robust learning and classification with low power consumption and resilience to variability.

TDSTDP

This paper introduces TDSTDP, a variation of STDP that performs temporal difference learning without dopamine modulation, and demonstrates its biological plausibility through simulations and a synaptic calcium model.

A lightweight data-driven spiking neural network model of Drosophila olfactory nervous system with dedicated hardware support

Synaptic turnover promotes efficient learning in bio-realistic spiking neural networks

This study shows that synaptic turnover improves learning efficiency in spiking neural networks, especially under resource constraints.

Competitive performance and superior noise robustness of a non-negative deep convolutional spiking network

The paper introduces a spiking neural network framework called SbS that matches CNN performance on clean data and shows superior robustness to unseen noise.

Closing the Bio-Silicon Loop for Cellular Neural Prosthesis using FPGA-based Iono-Neuromorphic Models

This paper designs an FPGA-based neural model to simulate ion-channel dynamics and demonstrate a hybrid bio-silicon system for cellular rehabilitation.

Simulating Short-Term Synaptic Plasticity on SpiNNaker Neuromorphic Hardware

This paper implements a synaptic plasticity model on SpiNNaker neuromorphic hardware to study its role in deep brain stimulation effects.

Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks

The paper proposes training spiking neural networks with neuromodulated synaptic plasticity using gradient descent to improve online learning performance inspired by neuroscience.

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.

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.

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.

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.

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.

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.

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.