Plasticity rules

Animals learn complex cognitive maps to navigate through physical environments or reason about abstract concepts. These maps are believed to be encoded by mammalian hippocampal grid and place cells, forming low-dimensional neural manifolds that support flexible navigation. However, beyond simple line and ring attractors, how the brain represents and navigates more complex neural manifolds remains unclear. In this study, we propose a solution for representing arbitrary nonlinear neural manifolds by approximating them with piecewise linear spaces composed of simplexes, known as “simplicial compl

Plasticity rules governing synaptic changes vary widely across species, brain regions, and brain states. Despite this variability, these rules often give rise to neural circuits with similar structural motifs and functional properties, a phenomenon known as degeneracy. While functional degeneracy---where different plasticity rules result in similar network functions---has been widely studied, the structural aspects of degeneracy remain underexplored. Structural degeneracy refers to cases where distinct plasticity rules produce neural circuits with similar connectivity patterns. To investigate

Synaptic plasticity is known to be a key player in the brain’s life-long learning abilities. However, due to experimental limitations, the nature of the local changes at individual synapses and their link with emerging network-level computations remain unclear. I will present a numerical, meta-learning approach to deduce plasticity rules from either neuronal activity data and/or prior knowledge about the network's computation. I will first show how to recover known rules, given a human-designed loss function in rate networks, or directly from data, using an adversarial approach. Then I will pr

Graphics processing units (GPUs) are widely available and have been used with great success to accelerate scientific computing in the last decade. These advances, however, are often not available to researchers interested in simulating spiking neural networks, but lacking the technical knowledge to write the necessary low-level code. Writing low-level code is not necessary when using the popular Brian simulator, which provides a framework to generate efficient CPU code from high-level model definitions in Python. Here, we present Brian2CUDA, an open-source software that extends the Brian simul

Understanding synaptic plasticity is critical for elucidating mechanisms for neural development, learning and memory consolidation. Synaptic plasticity is modelled by simulating the activity of interacting neural populations, using hypothesized functions (plasticity rules) that modify the connections between neurons. Plasticity rules are typically hand-crafted from single-synapse experiment data, but have shown limited success in understanding network-level properties. Recent studies aim to discover rules using supervised learning rather than hand-crafting them. However, these approaches stil

Synaptic plasticity is known to be a key player in the brain’s life-long learning abilities. However, due to experimental limitations, the nature of the local changes at individual synapses and their link with emerging network-level computations remain unclear. In theoretical work, synaptic plasticity is often modelled with unsupervised local plasticity rules. However, deriving a complete set of functional plasticity rules analytically will require divine intuition and numerous assumptions. Here, we approach the problem numerically. We show how to deduce plasticity rules in silico with supervi

We introduce Norse: An open-source library for gradient-based training of spiking neural networks. In contrast to neuron simulators which mainly target computational neuroscientists, our library seamlessly integrates with the existing PyTorch ecosystem using abstractions familiar to the machine learning community. This has immediate benefits in that it provides a familiar interface, hardware accelerator support and, most importantly, the ability to use gradient-based optimization. While many parallel efforts in this direction exist, Norse emphasizes flexibility and usability in three ways. Use