Information processing is what every living system and every computer does to survive, adapt, or produce useful output: take in raw signals, transform them, and generate responses. The phrase covers an enormous range of activity, from a single cell detecting a chemical gradient to a human brain parsing a sentence to a financial market absorbing an earnings report. What ties these together is a shared logic of input, transformation, and output, even though the hardware varies wildly. The details of how that logic plays out at each scale reveal surprising constraints, clever workarounds, and some deep similarities between systems that look nothing alike on the surface.
How a Single Cell Keeps Its Signals Straight
Before you get to brains or computers, information processing happens at the level of individual cells. Each of the trillions of cells in a human body can detect and respond to a wide range of stimuli, relying on an extensive toolkit of signaling proteins to interpret incoming chemical messages and make decisions accordingly.1PubMed Central. Keeping Signals Straight: How Cells Process Information and Make Decisions A cell sitting in a tissue might simultaneously receive signals about nutrient availability, stress hormones, immune alerts, and instructions from neighboring cells. The challenge is not just receiving those signals but keeping them from crosstalk. Cells solve this partly through spatial separation (different signaling molecules activating on different parts of the cell membrane) and partly through timing (a sustained signal can mean something different from a pulsed one).
This cellular-level processing is not a metaphor. It is genuine computation. A cell integrates multiple inputs, weighs them against each other, and produces an output, whether that output is dividing, moving, secreting a molecule, or dying. The entire immune system depends on white blood cells making remarkably sophisticated friend-or-foe judgments based on molecular information. Cancer, in one framing, is what happens when a cell’s information processing goes wrong and it starts ignoring the “stop dividing” signals that healthy cells obey.
How Neurons Encode Information
The brain scales up cellular signaling into something far more elaborate. Individual neurons communicate through electrical impulses called spikes, and the question of exactly how those spikes carry information has been debated for decades. The two broad camps are “rate coding,” where what matters is how many spikes fire per second, and “temporal coding,” where the precise timing of each spike carries the message. In practice, both appear to operate. Temporal codes rely on the timing patterns of neuronal spiking responses rather than on which specific neurons are activated, and they contrast with channel or rate-place codes that depend on activation profiles among selectively tuned neurons.2Frontiers in Computational Neuroscience. Survey of temporal coding of sensory information
Short encoding timescales, from milliseconds to tens of milliseconds, appear to be particularly useful for sensory information encoding and perception.3PubMed Central. Contribution of spike timing to the neural code: from fast to slow timescales When you touch a textured surface, for instance, the precise timing of spikes from your fingertip sensors lets your brain distinguish silk from sandpaper with remarkable speed. This millisecond-level precision is one reason biological neural processing can sometimes outperform digital systems that rely on comparatively slow clock cycles, despite the fact that individual neurons fire far slower than transistors switch.
The Brain’s Bottlenecks
For all its complexity, the human brain is severely capacity-limited. Behavioral research has identified at least three major bottlenecks that can cripple your ability to consciously perceive, hold in mind, and act upon the visual world.4Trends in Cognitive Sciences. Capacity limits of information processing in the brain These show up in everyday life more than you might expect. The attentional blink is why you miss a second important thing if it appears less than half a second after the first. Visual short-term memory is why you can hold only about three or four objects in mind at once (not seven, as the old myth goes for most visual tasks). And the psychological refractory period is why you cannot truly do two demanding tasks at the exact same instant, despite what multitaskers believe.
These limits are not failures of evolution. They reflect genuine design trade-offs. A brain that tried to process every incoming signal with full conscious attention would be overwhelmed immediately. Your retinas alone send data to your brain at a rate comparable to a high-definition video stream. The solution is aggressive filtering: the vast majority of sensory input gets processed unconsciously, and only a thin slice reaches awareness. The bottlenecks exist precisely at the points where information transitions from automatic, parallel processing to the kind of conscious, serial processing you experience as “paying attention.”
Predictive Coding and How the Brain Saves Bandwidth
One of the brain’s most elegant strategies for handling its bandwidth limits is predictive coding. Rather than faithfully transmitting every detail of an incoming sensory signal, the nervous system builds a running model of what it expects to perceive next, and then transmits only the unpredicted portions. By doing so, predictive coding reduces redundancy and makes full use of the limited dynamic range of neurons.5PubMed. Predictive coding
Think of it like video compression. Instead of sending every pixel of every frame, a compressed video file sends the first full frame and then sends only what changed. Your brain does something analogous. If you are walking down a familiar hallway, your visual cortex is not painstakingly encoding every wall tile. It is encoding the prediction “same hallway as usual” and then flagging anything surprising, like a new painting on the wall. That surprise signal, the prediction error, is what gets the full processing treatment. This is why unexpected events grab your attention so powerfully and why you can drive a familiar route on “autopilot” but snap to full alertness when a child runs into the street. The system is tuned to process novelty, not repetition.
How Fast Languages Actually Transmit Information
Human language is itself an information processing channel, and one with a striking property. Languages that sound fast, like Spanish or Japanese, tend to pack less information into each syllable, while languages that sound slow, like Mandarin or Thai, pack more. The net result is that the information rate across languages converges. A large cross-linguistic study found that spoken language information rate centers on a mean of about 39 bits per second, even though syllable rates vary widely from one language to another.6PubMed Central. Different languages, similar encoding efficiency: Comparable information rates across the human communicative niche
Mandarin and Spanish illustrate this nicely. Mandarin speakers talk slower but use syllables that are denser and more complex, while Spanish speakers talk faster with lighter, less informationally dense syllables. Both strategies arrive at nearly the same average information rate.7Language. A cross-Language Perspective on Speech Information Rate This suggests a deep constraint, probably set by the listener’s processing capacity rather than the speaker’s production ability. You can only take in and decode about 39 bits of linguistic information per second, regardless of whether those bits arrive in fast-but-light syllables or slow-but-heavy ones. Languages appear to have independently evolved toward this shared ceiling.
DNA as an Information Storage Medium
Biological systems do not just process information in real time. They also store it, and the storage medium they evolved is extraordinarily dense. DNA encodes information at roughly 2 bits per nucleotide base, which translates to a theoretical physical density of about 460 exabytes per gram.8PubMed Central. DNA storage: research landscape and future prospects To put that in perspective, a single gram of DNA could in principle store more data than all the hard drives produced in a year.
Researchers have been working to exploit this density for artificial data storage. Any digital information can be synthesized into DNA in the lab and stored in tiny capsules that promise reliability for hundreds of years.9EURASIP Journal on Image and Video Processing. Data and image storage on synthetic DNA: existing solutions and challenges The practical challenges remain significant: writing (synthesizing) and reading (sequencing) DNA is still slow and expensive compared to conventional storage. But the appeal is obvious for archival purposes where you need extreme density and long-term durability. A vial of DNA the size of a pencil eraser could, in theory, hold a library’s worth of data for centuries without electricity.
Swarms, Flocks, and Crowds
Information processing is not limited to what happens inside a single organism. Groups of animals, and groups of people, can process information collectively in ways that no individual in the group could manage alone. A school of fish evading a predator is performing distributed computation: each fish responds to the movements of its nearest neighbors, and the collective pattern that emerges, the school splitting and reforming around a threat, reflects integrated partial knowledge of the environment at the group level.10PubMed. Collective information processing and pattern formation in swarms, flocks, and crowds
Human crowds do the same thing. If you are in a crowded plaza and people around you start moving in one direction, you pick up on that flow and adjust your path even if you have no idea why they are moving. Information about a blocked exit, a street performer, or a threat propagates through the crowd via local interactions, and the crowd as a whole “knows” something that most individuals in it do not. Financial markets exhibit a more formalized version of this. Options markets, for example, reflect roughly a quarter of new information before it shows up in stock prices, acting as a kind of leading edge of collective information processing about a company’s prospects.11Journal of Financial Markets. Price discovery in stock and options markets
Underground Networks in Forests
Plants are often thought of as passive organisms, but they engage in information processing through underground fungal networks. Mycorrhizal networks, webs of fungal filaments connecting the roots of neighboring plants, can alter the adaptive behavior of connected plants, including rapid changes in physiology, gene regulation, and defense responses.12PubMed Central. Inter-plant communication through mycorrhizal networks mediates complex adaptive behaviour in plant communities These networks also transfer resources. Experiments using fluorescent dye and isotopic tracers have demonstrated the transfer of water between plants connected only by fungal hyphae, with no direct root contact required.13Journal of Experimental Botany. Common mycorrhizal networks provide a potential pathway for the transfer of hydraulically lifted water between plants
The popular narrative of “trees talking to each other” through these networks needs some nuance, though. When one plant is attacked by a pest, neighboring plants on the same network sometimes ramp up their defenses, and the natural interpretation is that the attacked plant sent a warning. But theoretical modeling suggests that genuine plant-to-plant warning signals face a steep evolutionary problem: warning your neighbors benefits competitors, which reduces your own relative fitness. The more likely explanation may be that the attacked plant cannot fully suppress the chemical cue of being attacked, or that the fungi themselves detect the attack and relay the information to protect their other host plants.14PubMed Central. The evolution of signaling and monitoring in plant-fungal networks The information transfer is real. Whether it is truly cooperative communication or something more self-interested on the part of the fungi is an open question.
How Fast Different Species See the World
One of the most striking ways that information processing differs across species is in temporal resolution of vision. The critical flicker fusion frequency, or CFF, measures how fast a light has to flash before an animal perceives it as continuous rather than flickering. A higher CFF means an animal can resolve faster visual events, essentially seeing the world in higher temporal resolution.
Across seven taxonomic classes, insects had the highest flicker fusion rates, averaging about 151 Hz, followed by birds at roughly 89 Hz. Mammals came in at about 42 Hz, which means we perceive a light flickering above that speed as a steady glow.15PLoS ONE. A flashing light may not be that flashy: A systematic review on critical fusion frequencies This explains why a housefly can dodge a swatter so effectively: to the fly, your hand is moving in something approaching slow motion. The differences are linked to both the physical properties of photoreceptors and the post-retinal signal processing capabilities of different species.16PubMed. A mechanistic inter-species comparison of flicker sensitivity Evolution has tuned each species’ visual processing speed to its ecological niche. Fast-moving predators and prey tend to have higher temporal resolution, because the cost of missing a rapid visual event is life or death.
Neuromorphic Computing and the von Neumann Bottleneck
Conventional computers process information through a strict separation between memory and processing. The processor fetches data from memory, operates on it, and writes results back. This back-and-forth creates a fundamental bottleneck: the speed of computation is limited by how fast data can shuttle between the two. Brains, by contrast, compute and store in the same place. Every synapse is both a memory element and a processing element.
Neuromorphic computing tries to borrow this biological advantage. These chips use event-driven, asynchronous neurosynaptic elements with colocated memory for processing and machine learning tasks.17PubMed Central. Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directions Instead of processing data in continuous clock cycles, they respond to “events,” spikes of incoming data, only when there is something new to process. This makes them vastly more energy-efficient for tasks that involve sparse, real-time data, like processing input from sensors or cameras.
The SpiNNaker2 chip is one of the more developed examples. It is a digital neuromorphic processor designed for scalable machine learning, and its event-based, asynchronous architecture allows large-scale systems of thousands of chips to be composed together. Applications range from conventional artificial neural networks through biologically inspired spiking neural networks to generalized event-based neural networks.18arXiv. SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning The appeal for edge computing, where processing happens on the device itself rather than in a distant data center, is clear. A neuromorphic sensor on a drone or a prosthetic limb can process environmental data locally, in real time, without the latency or energy cost of sending everything to the cloud.
Reading the Brain’s Output Directly
If information processing in the brain follows certain regularities, it should be possible to read those patterns and translate them into commands. That is exactly what brain-computer interfaces attempt. The core challenge, neural decoding, involves converting high-dimensional neural activity into a low-dimensional output signal, like a cursor movement or a robotic arm command.19PubMed Central. Review: Human intracortical recording and neural decoding for brain computer interfaces
Motor cortex neurons have a property called cosine tuning: each neuron fires most vigorously when you move (or intend to move) in a particular preferred direction, and its firing rate drops off smoothly as the actual direction deviates from that preference. By recording from dozens or hundreds of these neurons simultaneously and applying statistical models that exploit the cosine-tuning property, researchers have enabled a person with tetraplegia to control a robotic limb using thought alone.20PubMed Central. Review: Human intracortical recording and neural decoding for brain computer interfaces The decoding algorithms are, in essence, running the brain’s information processing in reverse: instead of sensory input flowing in and motor commands flowing out, the system reads the motor commands directly and converts them into machine actions.
Integrated Information and Consciousness
At the most speculative frontier, some researchers have tried to formalize what distinguishes the kind of information processing that gives rise to consciousness from the kind that does not. Integrated information theory proposes a quantitative measure, denoted Φ (phi), of the amount of integrated information in a physical system, which is proposed to have an identity relationship with consciousness.21PubMed Central. Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans A system has high Φ when its parts are both highly differentiated (they do different things) and highly integrated (they work together as a unified whole). A pile of sand has many parts but no integration; a simple thermostat has integration but no differentiation. A brain has both.
The theory remains contentious. Computing Φ for anything larger than a tiny system is prohibitively difficult, and critics argue the theory’s predictions are hard to test in practice. Still, the attempt highlights something genuinely interesting about information processing: not all information processing feels like anything. Your laptop processes enormous amounts of data without any apparent inner experience, while your brain processes far less data per second but generates the rich subjective world you live in. Whether that gap can be explained by the structure of information integration, or whether it requires something else entirely, is one of the hardest open questions in science.
Nested Boundaries From Cells to Ecosystems
One of the more ambitious theoretical attempts to unify information processing across biological scales uses the concept of a Markov blanket, a statistical boundary that separates a system from its environment while still allowing the two to interact. Under this framework, any living system, from an individual cell to an entire organism, can be understood as having layers of nested, self-sustaining boundaries. A cell has a Markov blanket (roughly, its membrane and the signals crossing it). A tissue has one. An organism has one. Even elements of the local environment can fall within the blanket of a biological system.22PubMed Central. The Markov blankets of life: autonomy, active inference and the free energy principle
The practical implication is that information processing is not something that happens at just one level of biological organization. It is fractal. A cell processes information about its chemical environment. A brain processes information about the world. A flock of birds processes information about a predator. A forest, connected by mycorrhizal networks, processes information about drought. At each scale, the same abstract logic applies: a boundary separates inside from outside, sensors pick up signals from the environment, internal states update, and actions flow back out. The hardware changes completely, from protein cascades to neural spikes to fungal hyphae to the movement of individual birds, but the computational architecture rhymes.