Eros F. Geppi Argentina 🇦🇷 - Canarias 🇮🇨 estudiante PhD en Ecología aplicada a sistemas marinos 🌊
Canary Island
Joined June 2011
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- Following 547
- Followers 183
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Eros retweeted
El paper arxiv.org/abs/2510.27250 presenta una derivación de las ecuaciones de Einstein a partir de la entropía de entrelazamiento en la teoría conforme dual.
Eros retweeted
There were waves after all!
Just had to increase spike speed and increase fatigue recovery speed, add more neurons etc
updated the code (open source) below
Spiking neural net flow
neurons spike according to membrane dynamics, accumulating charge and firing when reaching threshold
if they fire too often they get fatigued and need to rest (red circle)
connections gets strengthened via Hebbian rule(fire together wire together)
I was expecting for more wave like behavior but I am tired for now will continue later
open source link to source code in comment
Eros retweeted
This is Microsoft SandDance. originally a closed-source project that was later open-sourced.
It lets you visually explore and understand data with smooth, animated transitions between multiple views.
Eros retweeted
I love teaching Monte Carlo simulation — such a powerful empirical method for exploring uncertainty distributions!
To help my students see how they can apply it in their own careers, I built an interactive #Python dashboard using @matplotlib.
It lets them simulate and visualize uncertainty in lithium resource estimates — a hands-on way to experience how Monte Carlo methods bring data-driven decision-making to life.
Eros retweeted
We don’t directly perceive the color of objects in the world. Instead, our brains infer color based on local context.
[Link below.]
Eros retweeted
Branching Probability Network
using Markov chains we grow node graphs that look pretty awesome :)
Link to code in comment
It has all the customization one can imagine and a Auto mode where endless many patterns just emerge, which is how I made the video
A Markov Chain is a mathematical model that describes a sequence of events where the probability of transitioning to the next state depends only on the current state, not on the sequence of events that preceded it. This is called the "Markov Property" or "memorylessness."
Bio inspired Hebbian probabilistic network learns in less than 5 minutes from a super sparse single reward per episode!
also has imitation learning (manual control)
system has 3 parallel competing networks which get sensory input from a 360 vision (27-direction sensory neuron array)
link to code in comment
each sub-network is responsible for a single motor action: forward, left and right. at each step whichever section has most neurons firing wins
neurons fire probabilistically and mark themselves with a time-decay tag which happens when a neuron fires and diminishes with time. you can see this "
tag countdown" on each neuron
when a reward is attained(eating the cheese) eligible connections gets strengthened
I included 2 runs in the video first was 15 minutes in real time and second was 5 minutes. red plot is the rolling average of last 10 time to cheese.
it is really not possible for agent to achieve full control due to probabilistic neural firing. that is why it has to learn while jittering all over the place, which in itself is interesting
in manual mode you can guide the cheese by stimulating its motor control networks ( still probabilistically ) and the rewards will still work
✅ Biologically Plausible Features:
Stochastic firing (neurons in the brain fire probabilistically)
Reward-based learning (dopamine-like neuromodulation)
Hebbian plasticity (well-established biological mechanism)
Eligibility traces (biological neurons have temporal credit assignment)
Sparse sensory encoding (similar to place cells, grid cells)
Competitive action selection (basal ganglia architecture)
No backpropagation (which is biologically implausible)
❌ Missing Biological Features:
No recurrent connections (real brains have extensive feedback loops)
No inhibitory neurons (GABAergic neurons are ~20% of cortex)
No spike timing (simplified from true spiking dynamics)
Uniform layer structure (biological networks are more heterogeneous)
Simple weight updates (real synaptic plasticity is more complex)
Eros retweeted
Patterns of Uncertainty node graphs with Markov chains Version 2
Markov chains are fascinating!
it is a set of probabilities (transition matrix) which determines the next state of a system or a branch based on its current state.
it is like saying if you are now "up" then go left 30% of the time and right %20, but for many states and many possibilities
Link to code in comment.
Let me know what you think
Branching Probability Network
using Markov chains we grow node graphs that look pretty awesome :)
Link to code in comment
It has all the customization one can imagine and a Auto mode where endless many patterns just emerge, which is how I made the video
A Markov Chain is a mathematical model that describes a sequence of events where the probability of transitioning to the next state depends only on the current state, not on the sequence of events that preceded it. This is called the "Markov Property" or "memorylessness."
Eros retweeted
“My brain is only a receiver, in the Universe there is a core from which we obtain knowledge, strength and inspiration. I have not penetrated into the secrets of this core, but I know that it exists”
~ Nikola Tesla
Eros retweeted
Majority of measurable quantities in our day-to-day life follow normal distribution. Example : length, height, weight, test scores etc.
So by fitting a normal curve onto the data, we can easily get desired probabilities. Like probability of getting accepted or rejected if the variable lies within a given range, or if the variable is less than or greater than a given value.
These concepts are used in a structured way in many applications, notably in Statistical Process Control and Process Capability Studies in industries.
Eros retweeted
The Geodesic Equation, which describes a particle's motion under gravity in general relativity.
Eros retweeted
Researchers have recorded the briefest interval of time ever measured: 247 zeptoseconds—the duration for a photon of light to traverse a hydrogen molecule.
That's 0.000000000000000000247 seconds.A zeptosecond equals one trillionth of a billionth of a second, a realm where light, the universe's speed champion, advances mere fractions of an atomic diameter. For scale, a single second contains as many zeptoseconds as there are seconds in 31.7 trillion years—vastly exceeding the age of the cosmos.
Physicist Reinhard Dörner and colleagues at Goethe University Frankfurt achieved this using intense X-rays from Hamburg's PETRA III accelerator. They aimed at hydrogen molecules—the simplest in existence, comprising two protons and two electrons. An incoming photon struck both electrons in rapid sequence, akin to a stone skipping across water.
To resolve this fleeting event, the team employed a COLTRIMS reaction microscope, an ultra-precise instrument that tracks particle positions and momenta. By examining the interference patterns from the two expelled electrons, they pinpointed the precise lag between the photon's impact on the first electron and the second.The finding: 247 zeptoseconds.
This demonstrates that light does not illuminate a molecule instantaneously, even at this tiny scale; the delay stems from light's finite velocity of roughly 186,000 miles per second (300,000 km/s). It represents the first direct observation of light propagating inside a molecule.
By contrast, chemical reactions unfold over femtoseconds—a thousandfold longer. Zeptosecond precision opens a window into quantum timescales, where electron and photon dynamics govern matter's core behaviors.
