Leaky integrate-and-fire neurons

Within the broad theme of brain plasticity, this work explores a dimension that has not received as much research attention thus far - activity dependent changes in myelin. Myelin acts to speed up axonal conduction. Until recently, the dogma of the field was that myelination was part of a developmental program that set up the neural circuitry, after which it remained static; however, the past decade of research has begun to reveal that myelin plasticity continues well into adulthood, is modulated in an activity dependent manner, and contributes to skill learning. The first part of our study is

…the Wilson-Cowan model to networks of leaky integrate-and-fire neurons. These systems typically display a region of parameter space where two different states are possible, one of low and another of high activity with a phenomenology typical of first order phase transitions, with e.g. hysteresis in correspondence of a sweep of a control parameter. The introduction of a global inhibition, given by the presence of inhibitory interneurons with very large in-degree and out-degree, leads to a smoothing of t

Task-trained recurrent spiking neural networks (RSNNs) can provide insights into how the brain performs spike-based computations, especially those involved in temporal tasks. Training RSNNs with backpropagation through time (BPTT) faces the challenge of non-differentiable spiking functions, requiring an approximation gradient through each time state of the network. Evolutionary Algorithms (EAs) offer an alternative to BPTT by generating random populations of models and selecting those with the best performance to provide a broader initial search space and the ability to optimize non-differenti

…Network model, where each group of Leaky Integrate and Fire neurons represents a cortical region. A STDP-based rule is used to learn patterns of activat

Interactions between spiking units grouped in distinct excitatory (E) and inhibitory (I) populations can result in a complex dynamical landscape. Part of this complexity, namely dynamics that depend on undelayed connectivities and external inputs, is retained by a simplified model, the threshold-linear network (TLN), which allows to derive fully analytical conditions for the emergence of multistability [1], paradoxical perturbation effects [2,3], and some oscillations [4,5]. These results, however, have only been applied to specific architectures, such as canonical circuits or all-inhibitory n

…activity of simultaneously recorded neurons. To this end, we first developed a method to extract significant pairwise and triple-wise interactions among neurons. Using statistically significant data, we then analysed pairwise and HOI from large-scale spiking activities in visual areas of mice during natural movie

…We consider a network of leaky integrate-and-fire neuro

It is well known that sparse coding models trained on natural images learn basis functions whose shapes resemble the receptive fields (RFs) of simple cells in the primary visual cortex (V1). However, few studies have considered how these basis functions develop during training. In particular, it is unclear whether certain types of basis functions emerge more quickly than others, or whether they develop simultaneously. In this work, we train an overcomplete sparse coding model (Sparsenet) on natural images and find that there is indeed order in the development of its basis functions, with lower

Neurons in the brain communicate through action potentials (spikes) that are transmitted through chemical synapses. Throughout the last decades, the question how networks of spiking neurons represent and process information has remained an important challenge. Some progress has resulted from a recent family of supervised learning rules (tempotrons) for models of spiking neurons. However, these studies have viewed synaptic transmission as static and characterized synaptic efficacies as scalar quantities that change only on slow time scales of learning across trials but remain fixed on the fast