Neuroscience

First, the brain is organized into functionally specific areas, and second, neurons in different parts of the vertebrate nervous system, indeed in all nervous systems, are quite similar. Small comparison with Computers A gross observation between computer’s transistors and human neurons is that there a big difference of numbers: trillions of transistors vs billions of neurons. 6 orders of magnitude frequency difference (Ghz versus 1kHz for neurons). Many many neural types and different types of connections. And the digital vs analog and chemical modes of communication. Parallel processor abilities. Fixed vs plastic architectures But this is comparing with transistors with one higher level object, so this comparison might not be completely fair. They are very different from this point of view. And only some brain areas are similar to real neural networks. ...

This note is just a collection of past useful notes to know to apply machine learning methods for the analysis of topics interesting in the neural sciences. Estimators You need to know all Parametric Modeling. We want to estimate unknown random variables with some observations. Maximum Likelihood See Bayesian Linear Regression. Bias-Variance Decomposition Fisher Information See Parametric Modeling#Fisher information. Applications Echo-locating bats Egyptian fruit flies bats use echo location to locate the target, emitting clicks and capturing echo to navigate the environment. Bats use to click slightly left and right compared to their direction of motion. It is interesting to observe and study how these bats are clicking around to move. ...

The Potassium Exchange values We use the measurement by Cole and Curthis 40mS/cm squared was their measure of Potassium ions leaving the membrane $$ \Delta Q = Idt = GA \Delta E dt $$The potassium concentration is 0.155 moles per litre. Where $G$ is the conductance per unit area, $A$ the membrane surface, $E$ voltage deflection Remember that the conductance is the reciprocal of the resistance, and $V = IR \implies I = \frac{V}{R} = GV$ ...

Vision is THE most important sense for humans. Most of the information we get is through vision 90% vision This is true for humans, but for mice for example it is different, they have probably a 64x64 pixel resolution equivalent. For humans, visual data is more important, it is faster compared to speech and other senses. We can also see this when we are talking about quantity of neurons: 30% of neurons in the cortex are for vision. 8% tactile, touch 3% hearing Rest is for other Precision of human vision Human vision is estimated to be about 576 Megapixels of data (3M snapshots patched together with saccades, that has that pixel image value), since it can distinguish 0.6arc-minutes (0.01 degrees). There is an estimate of about 60kk ± 12kk rods and 3kk ±0.5kk cones. This means that the image is constructed by high resolution saccades (patches). ...

We have a classical exploration-exploitation tradeoff, see Reinforcement Learning, a introduction. Why would animals explore, even if there is no immediate reward based on it? Animals are able to adapt and explore nonetheless. It would be thus nice to understand and implement these features in artificial systems. We will attack this from an evolutionary psychology perspective: Playfulness and intrinsic motivation. Curiosity Here we will talk about why animals are pushed to explore. ...

Here we attempt to answer what is memory, how is it stored and retrieved. Human Memory Definition of Memory Memory is a process by which information is: Encoded Stored Retrieved The brain has different types of memories, and certain brain regions are specialized for this task. Ebbinghaus Curves Other experiments destroy parts of the cortex and correlate this with recall. Types of memory We have the memory pipeline: ...

Synapses are the connections that exist between one neuron and another, so we can think of them as the communication channel between neurons. Gap Junctions Electrical based These are also called Gap Junctions These are more direct connections between neurons, allowing excitation ions to pass through quite directly (this is the difference compared to chemically based ones). It’s a circuit more similar to an electronic one because it’s faster. The end of the presynapsic part is called axon bouton, or axon terminal. Another characteristic of these kinds of synapses is that they are two-way channels. ...

In general we want to understand how neurons encode the rate and temporal information to build specific features like place cells, grid cells, velocity, head direction, or how it can guide behaviour or coordination. Many neurons encode together some features, it is quite rare that you have the face neuron and similars. Imaging techniques help us to get more information about these parts. Basics of Microscopy Image of a classical microscope, from course slides ...

The synaptic connections that define such circuits are typically made in a dense tangle of dendrites, axons terminals, and glial cell processes that together constitute what is called neuropil. Knee-Jerk Response The knee-jerk reflex (also known as the patellar reflex) is a classic example of a mono-synaptic reflex arc, which involves a direct connection between sensory and motor neurons, as well as inhibitory circuits to regulate movement. This is important for us if we fall and hit something. ...

Introduction to the Course What is a neural system? A neural system is an intricately organized network of specialized cells—primarily neurons, along with a variety of supportive glial cells—that processes and transmits information via electrical and chemical signals. In biological organisms, such systems underpin the entire nervous system, coordinating functions that range from basic reflexes to the complex interplay of perception, thought, and behavior. Early studies in neurobiology revealed that even simple neural circuits can generate coordinated responses, while modern neuroscience has shown that vast, hierarchically structured networks (such as the central and peripheral nervous systems) are responsible for the rich tapestry of animal behavior and cognition ...