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Articles about computational science and data science, neuroscience, and open source solutions. Personal stories are filed under Weekend Stories. Browse all topics here. All posts are CC BY-NC-SA licensed unless otherwise stated. Feel free to share, remix, and adapt the content as long as you give appropriate credit and distribute your contributions under the same license.
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Helmholtz’s dissertation on the nervous system: A forgotten early contribution to neuroscience
While Hermann von Helmholtz is widely known for his foundational contributions to physics, his early scientific work was actually focused on the nervous system. In this post, I share insights from reading Helmholtz’s 1842 dissertation, which was recently translated from Latin into English by Helmut Kettenmann and colleagues. The dissertation reveals Helmholtz’s detailed anatomical study of invertebrate nervous systems, conducted at a time when the conceptual distinction between neurons and glial cells did not yet exist.
Clopath plasticity rule
The Clopath learning rule or the Clopath synaptic plasticity rule is a biophysically inspired model of synaptic plasticity that extends traditional Hebbian learning by incorporating both spike-timing-dependent plasticity (STDP) and voltage-dependent plasticity mechanisms. This rule was introduced by Claudia Clopath et al. in 2010 to address some of the limitations of classical STDP models, providing a more accurate representation of synaptic changes observed in biological neurons.
Urbanczik-Senn plasticity
The Urbanczik-Senn plasticity model proposes a learning rule for dendritic synapses in a simplified compartmental neuron model. This rule extends traditional spike-timing-dependent plasticity (STDP) by incorporating the local dendritic potential as a crucial third factor, alongside pre- and postsynaptic spike timings. In this post, we briefly introduce the Urbanczik-Senn plasticity model and discuss its implications for neural computation and learning.
Implementing a minimal spiking neural network for MNIST pattern recognition using nervos
In this post, we use the open source spiking neural network (SNN) framework nervos to implement a minimal two layer SNN for pattern recognition on the MNIST dataset. We analyze how the network learns to classify digits through spike timing dependent plasticity (STDP) and how the synaptic weights evolve during training.
Spike-timing-dependent plasticity (STDP)
Another frequently used term in computational neuroscience is spike-timing-dependent plasticity or STDP. STDP is a form of synaptic plasticity that adjusts the strength of synaptic connections between neurons based on the relative timing of pre- and postsynaptic spikes. In this post, we briefly explore the concept of STDP and how it is implemented in neural modeling.
Revisiting the Moore’s law of Neuroscience, 15 years later
Just figured out that neuroscience appears to have its own version of Moore’s law, at least when it comes to the number of neurons that can be recorded simultaneously. This empirical scaling has profound implications for data analysis, modeling, and theory in computational neuroscience. In this post, we briefly review the original 2011 paper by Stevenson and Kording and reflect on its relevance today.
Neural Dynamics: A definitional perspective
Neural dynamics is a subfield of computational neuroscience that focuses on the time dependent evolution of neural activity and the mathematical structures that govern it. This post provides a definitional overview of neural dynamics, situating it within the broader context of computational neuroscience and outlining its key themes, methods, and historical developments.
Neural plasticity and learning: A computational perspective
After discussing structural plasticity in the previous post, we now take a broader look at neural plasticity and learning from a computational perspective. What are the main forms of plasticity, how do they relate to learning, and how can we formalize these concepts in models of neural dynamics? In this post, we explore these questions and propose a unifying framework.
Incorporating structural plasticity in neural network models
In standard spiking neural networks (SNN), synaptic connections between neurons are typically fixed or change only according to specific plasticity rules, such as Hebbian learning or Spike-Timing Dependent Plasticity (STDP). However, the brain’s connectivity is not static. Neurons can grow and retract synapses in response to activity levels and environmental conditions. A phenomenon known as structural plasticity. This process plays a crucial role in learning and memory formation in the brain. To illustrate how structural plasticity can be modeled in spiking neural networks, in this post, we will use the NEST Simulator and replicate the tutorial on ‘Structural Plasticity.
Linear mixed models in practice: When ANCOVA is enough and when you really need random effects
Linear mixed models (LMMs) are a powerful statistical tool for analyzing hierarchical or grouped data, common in neuroscience experiments. This post provides a practical guide on when to use LMMs versus traditional ANCOVA approaches, highlighting the advantages of mixed models in handling dependencies, unbalanced designs, and stabilizing estimates through shrinkage. Through simulated examples, we illustrate the differences in model performance and interpretation, helping you to make informed decisions about your statistical analyses.