Information and Communication Science

Information and Communication Science (ICS) — sometimes called Information and Communication Studies — is the interdisciplinary field that explores how information is created, transmitted, stored, understood, and used across different media and technologies. It combines insights from computer science, communication studies, cognitive science, sociology, linguistics, and media studies to understand both the technical systems and human behaviors involved in the flow of information.

Why “Smart” Systems Learn Like Our Group Chats

~ Manuel Castañón-Puga

Imagine your friend group chat. Some people talk a lot, others only chime in when tagged, and the vibe shifts as new friends join or old ones mute the thread. That’s a simple way to picture how many “smart” systems work: they’re networks whose connections matter and can change. Farmer calls these “connectionist” models and defines them by two things: only certain parts talk to each other at a time, and those lines of talk can strengthen, weaken, or rewire as the system runs. He also argues this idea isn’t just for neural networks—it also fits rule-based learners, immune systems, and even chemical reaction webs. 

Under the hood, the common picture is a graph: dots (nodes) connected by links (who can influence whom). You can describe that picture with a matrix or a compact list, and whether the web is dense or sparse changes how you store and work with it. What makes these systems feel “alive” is that there are often three tempos at once: fast changes to the node states (what’s happening right now), slower tweaks to parameters like weights (learning), and the slowest shifts to the wiring itself (who talks to whom). That last part—rewiring—can also be a form of learning. 

Take neural networks you’ve seen in AI headlines. In a feed-forward net, signals move layer by layer; in a recurrent net, outputs can circle back, which adds memory but also makes “when to stop” less noticeable. Learning can be as simple as “cells that fire together wire together” (a Hebbian principle that amplifies activity) or as guided as backpropagation, which adjusts connections to minimize error on known examples. Classifier systems look different on the surface—lots of if-this-then-that rules that post messages—but they’re still networks. Messages act like node activations, rules carry strengths, and a “bucket brigade” passes credit backward along the chain while genetic tweaks (mutations and crossovers) keep improving the rule set. Even practical details like thresholds and “support” change how many messages get through each step. 

Now zoom way inside your body. Farmer and colleagues show how the immune system can also be read as a learning network. It must tell “self” from “not-self,” and that skill is learned rather than hard-wired. Beyond single cells reacting, there are interactions across types that may form a regulating web. To model this, they create an “artificial chemistry” where antibody and antigen types are encoded as strings that match more or less firmly. Then, the system learns through clonal selection and even “gene shuffling” to explore new kinds. The point isn’t fancy math—it’s the practical lesson: functional systems learn by adjusting both how strongly parts talk and which parts talk at all. Think of your own routines like that chat: prune noisy threads, boost the ones that move you forward, and don’t be afraid to rewire who—and what—you let influence you.

Reference:
Farmer, J. D. (1990). A Rosetta stone for connectionism. Physica D: Nonlinear Phenomena, 42(1–3), 153–187. https://doi.org/10.1016/0167-2789(90)90072-W

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This blog provides simplified educational science content, created with the assistance of both humans and AI. It may omit technical details, is provided “as is,” and does not collect personal data beyond basic anonymous analytics. For full details, please see our Privacy Notice and Disclaimer. Read About This Blog & Attribution Note for AI-Generated Content to know more about this blog project.

Reality in Bits: Why Your Questions Matter (Wheeler’s Big Idea)

~ Manuel Castañón-Puga

You check your phone and see a notification. Tap or ignore. Yes or no. That tiny choice decides what you see next, which ad appears, and which song autoplays. John Archibald Wheeler, a physicist with a flair for bold ideas, argued that the universe itself works a bit like that. He claimed every “it” in the world—particles, fields, even space and time—gets its meaning from “bits,” the simple yes-no answers our measurements pull from nature. He called it “it from bit,” and he thought observer participation is not a footnote, but the starting point. 

According to Wheeler, an experiment is like asking nature a clear question and writing down a clean answer. No question, no answer. When a detector clicks, we often say “a photon did it,” but what we truly have is a recorded yes-no event, a single bit that makes the story real for us. In another example, turning on a hidden magnetic field shifts an interference pattern; the shift is again read as counts—yes–no answers that reveal the field. Even black holes, the ultimate cosmic mystery, carry “entropy” that can be read as the number of hidden bits about how they were formed. Everyday version? Think of scanning a ticket at a concert: the gate doesn’t “know” you until your QR code returns a yes. The event becomes real for the system at the moment of that verified click. 

Wheeler also lays down four shake-ups: no infinite “turtles all the way down,” no eternal prewritten laws, no perfect continuum, and not even space and time as basic givens. He urges a loop: physics gives rise to observer-participancy, which gives rise to information, which then gives rise to physics. Meaning isn’t private; it’s built through communication—evidence that can be checked and shared. That’s why the past, in this view, is what’s recorded now; our arrangements today decide which path that ancient photon “took” when we finally measure it. In daily life, that’s how group chats settle plans: until a poll closes, there is no fixed “Friday plan.” Once the votes (bits) are in, the plan (the “it”) exists for everyone. 

So what’s useful here? First, ask better questions. The choice of question shapes what you have the right to say about the world. Second, respect the click—the simple, reliable bit—because significant patterns grow from countless small answers; “more is different” when many bits combine. Third, remember that meaning needs community. A claim doesn’t count until others can check the evidence. In short, your everyday yes-no choices—what you measure, share, and record—are not trivial. They’re how reality, in Wheeler’s sense, gets built, from the lab to your life.

Reference:
Wheeler, J. A. (1990). Information, Physics, Quantum: The Search for Links. In Feynman and Computation (pp. 309–336). CRC Press. https://doi.org/10.1201/9780429500459-19

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This blog provides simplified educational science content, created with the assistance of both humans and AI. It may omit technical details, is provided “as is,” and does not collect personal data beyond basic anonymous analytics. For full details, please see our Privacy Notice and Disclaimer. Read About This Blog & Attribution Note for AI-Generated Content to know more about this blog project.

How Flies Read the World—And What That Teaches Us About Signals

~ Manuel Castañón-Puga

Imagine biking downhill with the wind in your face. Everything is moving fast, yet you still dodge potholes and react in a blink. Your brain is turning bursts of electrical “pings” from your eyes into smooth, useful information about motion. That everyday magic—making sense from quick spikes—is exactly what Bialek and colleagues set out to understand. They flipped the usual lab view. Instead of asking how a known picture makes a neuron fire on average, they asked how a living creature could decode a short, one-off burst of spikes to figure out an unknown, changing scene in real time. They showed it’s possible to “read” a neural code directly, not just describe it in averages. 

According to Bialek and colleagues, the classic “firing rate” concept is an average over many repetitions or across many cells. Real life rarely gives you that luxury. You usually get one noisy shot. So they focused on decoding from a single spike train, as an organism must do on the fly—literally. In the blowfly’s visual system, a motion-sensitive neuron called H1 feeds fast flight control. With only a handful of neurons in that circuit, the animal can’t compute neat averages; decisions rely on just a few spikes. The team’s key move was to replace rate summaries with a real-time reconstruction of the actual motion signal from those spikes. 

Here’s how they put it to the test. The fly viewed a random moving pattern whose steps changed every 500 microseconds, while the researchers recorded H1’s spike times. Then they built a decoding filter to turn spikes back into the motion waveform. To make it realistic, they required the filter to be causal and studied the tradeoff between speed and accuracy: waiting a bit longer improves the estimate, but you can’t wait forever if you need to act. Performance rose with delay and then leveled off around 30–40 milliseconds—right around the fly’s behavioral reaction time. The reconstructions were strong across a useful bandwidth, with errors that looked roughly Gaussian rather than systematic. Best of all, the neuron achieved “hyperacuity”: with one second of viewing, the motion could be judged to about 0.01°, far finer than the spacing of photoreceptors and close to theoretical limits set by the input itself. 

Why does this matter for your daily life? First, simple tools can decode rich signals: a straightforward linear filter turned spikes into motion with surprising fidelity. Second, quick decisions don’t require tons of data; a brief ~40 ms window and a few spikes can convey what matters, which is why “firing rate over time” isn’t always the right mental model. Third, robust systems tolerate minor timing errors; the code still works even when spike times are nudged by a few milliseconds. In short, smart decoding beats brute averaging, waiting just long enough maximizes usefulness, and good designs are fault-tolerant. That’s a handy recipe for studying, sports, or any fast choice you make under uncertainty. And yes—this work demonstrates that we can literally read a neural code in real-time.

Referencia:
Bialek, W., Rieke, F., de Ruyter van Steveninck, R. R., & Warland, D. (1991). Reading a Neural Code. Science, 252(5014), 1854–1857. https://doi.org/10.1126/science.2063199

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This blog provides simplified educational science content, created with the assistance of both humans and AI. It may omit technical details, is provided “as is,” and does not collect personal data beyond basic anonymous analytics. For full details, please see our Privacy Notice and Disclaimer. Read About This Blog & Attribution Note for AI-Generated Content to know more about this blog project.

Taming Information Chaos with a Two-Number Trick

~ Manuel Castañón-Puga

You open your phone to study and see a mess: 200 screenshots, 40 notes, five half-finished playlists, and a dozen tabs about “how to learn faster.” It feels random and overwhelming. Yet some parts repeat—your class schedules, the way you name files, your favorite study playlist order. Murray Gell-Mann and Seth Lloyd suggest a simple way to think about this mix of pattern and noise: separate what’s regular from what’s random, then measure both. In their view, “information” isn’t just messages or data—it’s also the uncertainty you still have. That’s why the same math that measures entropy in physics also measures surprise in messages, and in everyday choices like a coin flip. When all outcomes are equally likely, that uncertainty is highest; when you’ve seen the answer, uncertainty drops to zero.

Here’s the trick. First, describe the regular parts of your world as compactly as possible—the rules, templates, and habits you actually use. In the authors’ terms, that compact description is called effective complexity, and it’s the length of the shortest “program” that captures your recognized regularities. Think of it like the few lines you’d write to describe your note-taking system or playlist rules. Second, add a number for what’s left over—the unpredictable bits you can only label with probabilities. Add those two numbers and you get total information: “regularities length” plus “randomness left.” That sum is what it really takes to describe your situation. When you compare different ways of spotting patterns, the best choice is the one that makes the total information smallest, and then, given that, makes your regularities description as short as possible within a reasonable computing time. In plain terms: pick patterns that both explain a lot and are easy to use.

What does that look like on a busy day? Suppose your lecture notes often follow the same outline. Writing a short template (headings, quick symbols, highlight colors) encodes those regularities. That’s your effective complexity. The unexpected parts—off-syllabus examples, a surprise quiz—are the random remainder. Your goal is to choose a template that keeps the total low: simple enough to apply fast, specific enough that less is left to chance. The authors demonstrate the same logic with coin-toss sequences and even with recognizing the digits of π: a concise, insightful description can transform what initially appears random into something far easier to comprehend. In the π case, once you spot the rule, you trade randomness for a slightly richer description, and the overall effort drops. In study life, that’s like replacing “save everything and hope” with a tiny rule set that makes new material land in the right place automatically.

There’s also a helpful mindset for uncertainty itself. When you don’t know details, don’t pretend you do; assign fair weights and move on—what statisticians call “maximum entropy.” That keeps your randomness honest while you continue to refine the patterns. In practice, shrink your regularities until they’re easy to compute (templates you can apply quickly), and let the leftovers be labeled as “to triage later.” As Gell-Mann and Lloyd argue, any process that lowers total information makes a system easier to understand and control, whether it’s a physics model or your week. So next time your phone feels like chaos, write the tiniest rule that explains most of your flow, and let chance have the rest. You’ll spend fewer bits on confusion—and more on getting things done.

Reference:
Gell-Mann, M., & Lloyd, S. (1996). Information measures, effective complexity, and total information. Complexity, 2(1), 44–52. https://doi.org/10.1002/(SICI)1099-0526(199609/10)2:1<44::AID-CPLX10>3.0.CO;2-X

Privacy Notice & Disclaimer:
This blog provides simplified educational science content, created with the assistance of both humans and AI. It may omit technical details, is provided “as is,” and does not collect personal data beyond basic anonymous analytics. For full details, please see our Privacy Notice and Disclaimer. Read About This Blog & Attribution Note for AI-Generated Content to know more about this blog project.