245 citations · 367 across the 14 of their papers we have counts for
16 papers
Neuromorphic Pseudo-Random Number Generators with a Low Power Hardware Implementation
Jafar Shamsi, Navid Akbari, Sonia Sennik +2
Pseudo-random number generation often requires trade-offs among quality, power consumption, and bandwidth to produce unpredictable sequences of numbers. The brain, on the other han…
Approximate Macroscopic Dynamics of Spiking Neural Networks Based on Solutions to the Transport Equation
Wilten Nicola, Sue Ann Campbell
Firing rate fluctuations in neural populations are observed experimentally over multiple time scales, in single neurons, across trials when elicited by stimuli, and across populati…
Characterizing Memristive Nanowire Network Models via a Unified Computational Framework
Marcus Kasdorf, Diego Simpson-Ochoa, Abdelrahman Bekhit +3
Randomly self-assembled nanowire networks (NWNs) are dynamical systems in which junctions between two nanowires can be modelled as memristive units viewed as adaptive resistors wit…
The Coupling Strength Is a Scale Parameter in Threshold Power-Law Reservoirs and Does Not Influence Training Accuracy
Wilten Nicola
In reservoir computing, the coupling strength of the initial untrained recurrent neural network (the reservoir) is an important hyperparameter that can be varied for accurate train…
Input Driven Synchronization of Chaotic Neural Networks with Analyticaly Determined Conditional Lyapunov Exponents
Jordan Culp, Wilten Nicola
Recurrent neural networks (RNNs) with random, but sufficiently strong and balanced coupling display a well known high-dimensional chaotic dynamics. Here, we investigate if external…
Rapid Changes in Synchronizability in Conductance-based Neuronal Networks with Conductance-based Coupling
Wilten Nicola
Real neurons connect to each other non-randomly. How the connectivity of networks of conductance-based neuron models like the classical Hodgkin-Huxley model, or the Morris-Lecar mo…
Photons guided by axons may enable backpropagation-based learning in the brain
Parisa Zarkeshian, Taylor Kergan, Roohollah Ghobadi +2
Despite great advances in explaining synaptic plasticity and neuron function, a complete understanding of the brain's learning algorithms is still missing. Artificial neural networ…
Normalized Connectomes Show Increased Synchronizability with Age Through Their Second Largest Eigenvalue
Wilten Nicola, Sue Ann Campbell
The synchronization of different brain regions is widely observed under both normal and pathological conditions such as epilepsy. However, the relationship between the dynamics of…
A High GOPs/Slice Time Series Classifier for Portable and Embedded Biomedical Applications
Hamid Soleimani, Aliasghar, Makhlooghpour +3
Nowadays a diverse range of physiological data can be captured continuously for various applications in particular wellbeing and healthcare. Such data require efficient methods for…
Chaos in Homeostatically Regulated Neural Systems
Wilten Nicola, Peter Hellyer, Sue Ann Campbell +1
Low-dimensional yet rich dynamics often emerge in the brain. Examples include oscillations and chaotic dynamics during sleep, epilepsy, and voluntary movement. However, a general m…
Gap junction plasticity can lead to spindle oscillations
Guillaume Pernelle, Wilten Nicola, Claudia Clopath
Patterns of waxing and waning oscillations, called spindles, are observed in multiple brain regions during sleep. Spindle are thought to be involved in memory consolidation. The or…
Gap junction plasticity as a mechanism to regulate network-wide oscillations
Guillaume Pernelle, Wilten Nicola, Claudia Clopath
Cortical oscillations are thought to be involved in many cognitive functions and processes. Several mechanisms have been proposed to regulate oscillations. One prominent but unders…
Supervised Learning in Spiking Neural Networks with FORCE Training
Wilten Nicola, Claudia Clopath
Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networ…
One-Dimensional Population Density Approaches to Recurrently Coupled Networks of Neurons with Noise
Wilten Nicola, Cheng Ly, Sue Ann Campbell
Mean-field systems have been previously derived for networks of coupled, two-dimensional, integrate-and-fire neurons such as the Izhikevich, adapting exponential (AdEx) and quartic…
Non-smooth Bifurcations of Mean Field Systems of Two-Dimensional Integrate and Fire Neurons
Wilten Nicola, Sue Ann Campbell
Mean-field systems have been recently derived that adequately predict the behaviors of large networks of coupled integrate-and-fire neurons [14]. The mean-field system for a networ…
Mean-Field Models for Heterogeneous Networks of Two-Dimensional Integrate and Fire Models
Wilten Nicola, Sue Ann Campbell
We analytically derive mean-field models for all-to-all coupled networks of heterogeneous, adapting, two-dimensional integrate and fire neurons. The class of models we consider inc…