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Medicine and the Arts: Humanising Healthcare
Exploring Play: The Importance of Play in Everyday Life
Songwriting: Writing the Lyrics
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Explore how the brain optimizes information processing by operating near critical points, examining phase transitions, neuronal avalanches, and scale-free properties in neural networks.
Neuroscience PhD student shares creative process, software tools, and techniques for making science animations, including mathematical visualizations, 3D neuron activity, and brain models.
Explore the Tolman-Eichenbaum Machine, a computational model unifying memory and spatial navigation in the hippocampus. Learn about its architecture, performance, and implications for neuroscience.
Explore engrams, the fundamental units of memory in the brain. Learn about memory allocation, storage, and linking, as well as the role of immediate-early genes and neuronal excitability in memory formation.
Explore cognitive maps and how the brain organizes information for flexible behavior. Learn about hippocampal neurons, non-spatial mapping, graph theory, latent spaces, and factorized representations in neuroscience.
Explore how individual neurons function like deep neural networks, examining their complex information processing capabilities and the physiological mechanisms behind their computational complexity.
Explore the theta rhythm's role in memory encoding and retrieval. Learn about its generation, functions, and impact on brain activity, including integrated representations and sequential organization.
Explore wavelet transform for signal processing, uncovering hidden structures in data. Learn to build a wavelet toolkit, understanding its applications from hydrodynamics to neuroscience.
Comprehensive guide to starting computational neuroscience: programming languages, coding practice, textbooks, math resources, project ideas, and datasets for self-study and skill development.
Explore the geometry of behavior through neural manifolds, combining topology and neuroscience to understand high-dimensional information in neural circuits. Gain insights into brain function using intuitive explanations.
Explore probability theory fundamentals, from surprise to entropy, cross-entropy, and KL divergence. Learn key concepts applicable to neuroscience and machine learning.
Explore Boltzmann Machines, early generative models learning data probability distributions using stochastic rules and latent representations. Understand their goals, distribution, update rules, and evolution to Restricted Boltzmann Machines.
Explore backpropagation's fundamentals, from curve fitting to gradient descent, chain rule, and computational graphs. Gain insights into this crucial machine learning algorithm's principles and applications.
Explore Hopfield networks, a foundational model of associative memory in neuroscience and machine learning. Learn about network architecture, inference, learning, and limitations.
Explore dynamical systems and differential equations through intuitive examples. Learn key concepts like state variables, phase portraits, and limit cycles to understand how systems change over time.
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