These are SELECT NOW(); as well as antiquated concepts from my OLTP/OLAP Data Architect era jobs; where so much of the core knowledge translates to “LLM Key/Value Cache Architectures” that Directors always get confused when they’ve not had the same experiences. Anyway, about those Neural Nets…

Classic Neuroscience example: “I’m having a conversation, I’m sleepy, my brain will only take in single simple sentences with one action and one request at most; anything further will be silently discarded or cause Brain Queue.

We all know the feeling of a sluggish Brain Queue, where our sleepiness limits comprehension, limits attention span, and limits the amount of sensory input capacity for decision making.

Having insufficient resources, like attention or short term memory, can result in the mind becoming stuck in a Consideration Loop leading to “Sleepy-Faced Analysis-Paralysis”. This is where the brain is too tired to comprehend the full cognitive pathway required to exit from the loop.

When working with on-premise or public-API based LLMs, engineering choices must be made, specifically the First-Stage task of “choosing the correct model”.. and more generally with decision making processes where the length of associative (contextual) information is a requirement.

The TL;DR .. Sure, Time’s’a Wastin'

How about some quick blurbs from real human-to-human conversations, sourced from my career path right up through this past week. There are a few decently representative examples of short context vs long context.

She’s too verbose. She seems shy and quiet. She talks too much. She doesn’t go deep on technical details. She goes on tangents and my attention span is too short to see the larger picture. She’s hard to read. She’s too open about everything. She’s difficult to “get into the weeds with”. (that last one is my new favorite).

It’s always a double standard; I’m used to it, contextually speaking.

1. Context length is the amount of text an LLM can see at once.
2. CTL affects response quality, server specs, latency, API, cost.
3. A longer window can help the model reason across more information, but it also makes the system heavier to run.
4. Treat context length as a core system requirement always.
5. Define it, test it, document it, monitor it, know it, love it.
6. Adjust it based on real usage. It will be chunked, split, cached.

What’s this about Burning Runways?

But before we do, and because everyone loves to compltain about 💸… it’s really in ones best interests to understand how those bills become a reality, especially right now in the industry when “courting adoption” can be considered a write-off vs “chasing profits”, except for some (oooh Claude, you’re not being ethical lately).

Rewind from LLMs to the SaaS era… just look at real world stories of situations where applications were not controlled on their Context States, leading to tens and hundreds of thousands of dollars in SaaS Application API bills:

• $738.420, “I subscribe to Vercel Pro for $20 per month. I also added a spending limit of $120, so no nasty surprises, right?”
• $72,000.999, “We Burnt $72K testing Firebase + Cloud Run and almost went Bankrupt”
• $120,000.420, “Cloudflare took down our website after trying to force us to pay 120k$”

What is Context Length?

Context length, sometimes called window size or max tokens per forward pass, is the number of tokens a model can attend to at once. It directly limits how much raw text, user history, or retrieved knowledge can be processed in a single inference step. Our brains do this constantly, we infer and make decisions based on memory and situational context of data, and neural networks do too.

Since every engineering stage ultimately decides what the model sees and how much it costs to see it, context length is a cross‑cutting quality attribute. Below we walk through each first‑stage task (including the critical “Iterative Revision” stage) and explain how important context length is, what engineering implications arise, and what concrete actions you can or should take and why.

For comparison analysis, click through the data table below to see model specific specifications, including Context Length, for various Open Source models (these are not the latest LLMs, so go look at HuggingFace if you want more info).

Cardtainer (Shadow DOM, Self-contained)

Context Length: What It Changes

So, you want it hot and loose or you want those warmed-up caches pre-primed from snapshot slapshots to the top-layer of the mesh?

“Sure, but how much can the cache layers (there’s more than three) handle before we overload the cluster with memory pressure or downstream write amplification?”

Small Context Windows Are Great, Longer Also Yes!

• A small context window is fine for short prompts and mobile devices with NPUs that are fundamentally different from large GPUs like the H200 or MI300… or inference clusters serving thousands of them.
• A larger window lets the model work across longer documents, multi-turn conversations, codebases, contracts, research notes, support histories, or anything else where the answer depends on more than one paragraph of information.

That does not make longer context automatically better. It makes the system more capable, but also more expensive and more operationally sensitive.

Longer Context Often Requires More Resources

Yeaaaah, but we’re not talking about Gemma4 on LiteRT, this is a conceptual discussion for general LLM engineering. So…

• Longer Context = More memory pressure
• Longer Context = Higher GPU requirements
• Longer Context = Higher cost per request
• Longer Context = More complex data handling
• Longer Context = Requires more testing for latency, stability, and output quality

Context length is not just a model feature. It is an architecture decision.

Why Context Length Matters Across the Whole Project

Context length touches almost every part of an LLM project. It affects how we define the product, prepare the data, choose infrastructure, test the model, and manage cost.

1. Product requirements

Start with the real use case. Can the product work with short prompts, or does it need to process full documents, long chat histories, tickets, logs, reports, or contracts?

• That answer should be written into the project requirements early. For example:

The model must support at least an 8K token context window for production inference.

Without that requirement, the team may pick a model, GPU profile, or data pipeline that works in a demo but fails under real usage. Confusion!

2. Data collection

Long-context systems need long-form data. That may include:

• Full PDFs
• Long chat transcripts
• Complete contracts
• Support cases
• Technical documentation
• Source code repositories
• Meeting notes or knowledge base articles

The team also needs to confirm that the data can legally be stored, processed, and used for training or retrieval. This is especially important when full documents are involved.

3. Data chunking

Most long documents still need to be split into chunks. The goal is to avoid cutting the text in a way that destroys meaning.

A good chunking pipeline should:

• Keep related content together
• Avoid splitting sentences or sections awkwardly
• Add overlap where needed
• Track source metadata
• Stay within the model’s context limit

This is where a lot of long-context quality is won or lost. Bad chunking creates missing facts, fragmented answers, and unnecessary API calls.

4. Infrastructure

Longer context needs more memory, and therefore more money, more electricity, more attention to… you get the idea. It means larger GPUs, more careful batching, tighter monitoring, and better cost controls.

For example, moving from short prompts to 8k or 16k token workloads can change:

• GPU selection
• vRAM requirements
• Batch size
• Throughput
• Latency
• Autoscaling behavior
• Failure modes

The infrastructure has to match the context target. Otherwise the system may work in testing and fall over in production.

5. Model selection

Not every model handles long context well. Some models support long windows natively. Others can technically be extended, but quality may degrade if the model was not trained or tuned for that range.

When selecting a model, the team should check:

• Maximum supported context length
• Quality at long context
• Retrieval performance
• Latency profile
• Memory behavior
• Positional encoding strategy
• Cost per request

The advertised context window is not enough. The model needs to perform well near the limit.

6. Experiment tracking

Context length should be logged as a first-class experiment variable.

Every run should capture:

• Context window size
• Chunk size
• Overlap strategy
• GPU type
• Memory usage
• Tokens per second
• Latency
• Cost
• Evaluation results

This makes it possible to compare short-context and long-context runs without guessing.

7. Testing and validation

Long-context bugs often do not show up with short prompts.

The test suite should include:

• Short inputs
• Medium inputs
• Maximum-length inputs
• Inputs near the limit
• Realistic long documents
• Multi-document prompts
• Stress tests for memory and latency

The goal is to verify that the model does not crash, truncate important information, hallucinate around missing context, or slow down beyond acceptable limits.

8. Documentation

Context decisions need to be documented. Future engineers should be able to understand:

• Why the context size was chosen
• How chunking works
• Where truncation can happen
• Which GPU profile is required
• How to increase or reduce the window
• What tests must pass before changing it

This prevents context length from becoming tribal knowledge.

9. Production monitoring

Once users are in the system, monitor the actual request lengths. Track things like:

• Average prompt length
• 95th percentile prompt length
• Maximum prompt length
• Truncation events
• Retrieval misses
• API call volume
• Latency by token count
• Cost by request type

If users start pushing against the window, that is an early signal. The system may need better chunking, better retrieval, caching, prompt compression, or a longer-context model.

10. API limits and cost behavior

Shorter context can create more API calls. If one user request has to be split into many chunks, the system may hit:

• Higher latency
• Higher request volume
• Rate limits
• More billed API calls
• More orchestration complexity
• More opportunities for partial failure

A larger-context model can reduce the number of chunks required for some workflows, but it may also increase the cost of each request.

The right answer is not always “bigger window.” The right answer is the smallest context window that reliably supports the use case.

The Practical Flow

For a real project, the workflow looks like this:

1. Define the longest input the product needs to handle.
2. Confirm the data exists and can legally be used.
3. Split the data into chunks that preserve meaning.
4. Choose infrastructure that can support the target window.
5. Select a model that performs well at that context length.
6. Track context length in every experiment.
7. Test the model with realistic long inputs.
8. Document how the window, chunking, and infrastructure work.
9. Monitor real user request lengths in production.
10. Adjust when usage patterns change.

This keeps context length connected to the actual system, not just the model card. It’s super duper important so get it right! Test, then test more, and automate the tests, and have a validation LLM with reasoning and validation post-training awareness … or you’re gonna have a bad day.

What This Means in Practice

A longer context window can improve answer quality because the model can see more relevant information at once. It can also increase cost, latency, memory usage, and operational risk.

That tradeoff matters. For most production systems, context length should be treated like any other engineering constraint:

• Capacity
• Latency
• Reliability
• Cost
• Security
• Compliance
• Maintainability

It is not just a prompt design detail. It affects the data pipeline, model choice, infrastructure plan, testing strategy, and production budget.

Checklist for Your Future LLM Project

Before committing to a context length (and quantization), make sure…

1. What is the longest input this product needs to handle?
2. Are we working with snippets, full docs, or multi-doc workflows?
3. Do we have the right to store and process the full source data?
4. Are we chunking the data in a way that preserves meaning?
5. Do we need overlap between chunks?
6. Can the selected model support the required window?
7. Does the model still perform well near the edge of that window?
8. Do we have enough GPU memory for the target workload?
9. Are we tracking context length in experiments?
10. Are we testing short, medium, long, and maximum-length inputs?
11. Are we monitoring real request lengths in production?
12. Are we watching API limits, cache perf, tokens/ second, and cost?

If the answer to those questions is clear, the project is probably handling context length correctly. If the answer is vague, context length is still an unresolved architecture risk.

People in the industry are playing hard and fast, and whether those qualities may be considered a good thing is up for endless debate, but not right now. Right now I’m going to show some End of Year snapshots which are relevant to the blog’s purpose: wtf am I doing here anyway?

2025 was mostly extremely challenging, and mostly in helpful ways, but also featured a repetition of lessons which it seems I need to reflect on for additional forward momentum.

Let’s take a look at some of my core qualities as told by the oft-loved and oft-maligned OpenAi’s Chat-GPT 💟.

Your Year, Painted in Pixels

Very close, nice work GPT. While I do not have a 400GbE switch on my desk, I do have a few 100GbE 32-port switches in the racks, and occasionally 200GbE NICs and optics will float around the test bench.

The Architect Archetype ~ 3.9% of ChatGPT Users

It’s true! Over the 25 years of this career I’ve held architecture specific roles across the domains of Hardware Systems, Infrastructure Automation, Databases, and HPC Storage realms. It’s what I’ve always wanted to do, going all the way back to 1985 when introduced to the 4.77MHz IBM PC, Model 5150.

Personality: Specialist, Planner, Conceptual

That’s fairly accurate, more so if we add “spatial reasoning” to a list of cognitive methods.

“Thinks in structures and systems. Uses ChatGPT to design elegant frameworks and long-term strategies within a domain.”

A Datacenter in the Living Room?

Close in accuracy, however the “living room datacenter” is more commonly located in a home-office room (when living in an apartment), or in a dedicated area of the garage with HVAC filtration + dedicated 208/220/240V power, and the requisite automated cooling systems (usually Tripp-Lite units with duct-blowers). For systems at home the machines are often bought as separate server components along with a 4-5U chassis + rails + multiple sub-50 decibel large-format 120-180mm fans. Systems with high RPM 40-80mm axial screamers belong in a proper colocation facility.

How about that Homelab - is it in a constant state of flux?

Yes, mostly. It’s often helpful for proof of concept tests, hypothesis validation, and running benchmarks without having to drive to the DC or go back and forth with Remote Hands requests. Plus, lots of fun going on with hardware at home, but sometimes results in broken nails 😔.

Messy cables? Sometimes. Labels? Sometimes.

The homelab is often rebuilt: storage devices, fiber, dac, cables, and test clusters revised.

Homelab, sure… but how about those colocation photos instead?

As we’ve seen, some of the server hardware lives at home in my 25U shorty-racks, some is across the bay at a datacenter where big-loud machines require CRAC style cooling, and some of it is spread across the globe at other colocation facilities where The Providers have megawatt and gigawatt infra capacity. My personal infrastructure is a hybrid design, utilizing everything from embedded systems to multi-GPU hosts to public-cloud virtual machines… so, let’s see a bit more…

• Recent datacenter photos: https://prose.winterschon.com/a-recent-datacenter-colo-visit/

Your Chat Style

The analysis says… well, it’s fairly accurate for a description of being in-person as well as at the keyboard during remote meetings or daily chats.

Technical or otherwise, usually this is consistent. I’m often chatty, verbose, attentive, talkative, opinionated, even positively bubbly at times. Unless you catch me on a day where those qualities are supplanted by the clouds of quietly recalcitrant moods - the type which are grounded on a dualistic view of existence best described as Stoicistic-Nihilism, having been sourced from far too many post-traumatic stress events endured.

Your Chat Style: Direct, technically precise, often layered with humor and a critical edge, mixing structured reasoning with conversational spontaneity and sharp observational wit.

Your 2025 Chat Stats

Hold on now. I’ve used em dashes since 6th grade English class—so if seeing those in my writings is giving an impression that I use LLMs for all of my authoring is quite incorrect. For all of those people who don’t like them I have a solution: go to the settings section and set your rules and move on with your life. 😏

• Total Chats: 720
• Messages sent: 4,861 (not tokens, messages)
• Chattiest day: September 5th (why? we’ll need to see…)
• Images generated: 3 (Sora kinda sucks)
• Em-Dashes “Exchanged”: 1,882
• Top Messages Sent: 1% (percentile)

Regardless of those stats, even with my use of ChatGPT’s advancements with their “Pro Account”, most of my Ai/ML occurs on self-made infrastructure. I don’t rely on closed source, I don’t like closed source, and I don’t like non-democratized intelligence services that are just in it for profit. My career was built on Open Source and this current infra uses tooling like Ollama, Open-WebUI, vLLM, llama.cpp, CUDA, PyTorch, ROCm, and many other examples of collaborative ingenuity - free to use, free to improve, so please contribute if you can.

Three Big Themes of 2025

1. Building Ultra-Low-Latency Systems (true)
2. Experimenting, Measuring, Refining (true)
3. Engineering Meets Everyday Life (true)

Thoughts on 2026, Ai Industry Trends != Rules

Working in the Ai/HPC/ML industry, an observable fact is that the majority of consumer-facing Ai companies are still trying to figure out this new landscape, how it works, why anyone should care, and how to monetize whatever it is that they’re focused on.

Most will repeat the errors of the past, most will fail, most will malign the tech while misunderstanding nearly everything about the topic; and that’s fine. That’s the routine in Silicon Valley, but it doesn’t have to be.

1. We cannot solve today’s problems with yesterday’s methods, and that is a simple fact. We need the core technologies involved with Ai in order to resolve the massive amount of tech debt from earlier generations, but we need to stop expecting the same success from the same methods from the past; this is not the same world as 10, 20, or even 5 years ago.
2. The current anti-Ai mentality in mass-media is occurring despite companies like IBM, Cray, Fujitsu, BullSequana, and a handful of others with Top-500 dominant postures being focused on Ai-enabled Supercomputing, HPC, and acceleration hardware domains for well over two decades. These clusters have been active at research facilities with names like CERN, Technion, and of course the DoD and Military labs. These clusters keep us safe, they advance human understanding, assist with solving otherwise unsolvable problems, and are used to maintain the systems and functions our current world relies on for security and the advancement of human potential.
3. Ai is not just chatbots, it’s not limited to multi-modal agentic buzz-words, but that’s a tangent for another day. Datacenters are not ruining the world like mass-destruction of rainforests just so people can eat more cows. Conversely, we have an entire section of the Supercomputing industry focused on green energy and resource efficiency. Medical advancements, brain research, climate science and modeling, cancer research, etc… all of this is and has been advanced by Ai - but it’s not from the consumer side and it’s not how the media presents the information. Gosh, I wonder what incentive the media has to engage in such maligning tactics?

How Do We Move Forward?

In spite of or despite anti-Ai sentiments, while the public-facing corporate marketing landscape fights for “Consumer Economy Dominance”, engineers and architects like myself are busy in the background innovating with (sometimes our own) Ai/ML systems and (sometimes our own RSU/ISO/BST* funded) heavy-compute big-data storage infrastructures.

People like myself do this because we love it (mostly, nothing is perfect), and because we want to improve the lives of those around us, not to simply make money and acquire material goods. These skills require compromise, dedication, attention to detail, and a deeply held desire to improve the human condition through personal investment of time, mentoring younger engineers, and the occasional runway.

TOGETHER WITH THE AIs WE WILL BUILD A BETTER ULTRON

Kidding, mostly. Ultron had some good philosophical points, but we’re not in Strawberry Fields or the BS about paperclips from unimaginative and uninformed bloggers; this is real life and The Terminator is not around the corner. Pass the espresso and let’s check the local-node TTFT and CHRs to see how SkyNet is doing. ❤️

Modern neuroscience and artificial intelligence converge on fundamental concepts and terminology. This post may serve as a primer, a method to explore key parallels between biological and artificial systems by examining shared principles, from basic units like neurons to complex processes involved with learning and memory consolidation.

Initially, we have to cover a high-level overview of dopaminergic pathways and memory processes in the brain, then shift to deep learning architectures (including convolutional neural networks and large language models). By understanding how these concepts map onto each other, we can better appreciate both the similarities and distinctions between biological cognition and artificial intelligence. Yay. ❤️

Neurotransmitters and Reward-Based Learning

Dopamine plays a central role in the brain’s reward system, helping to reinforce behaviors that lead to positive outcomes such as food, social interaction, or achievement. This neurotransmitter is essential for both motivation, driving us to pursue rewarding experiences, obsessions, punding, learning, and a hundred other areas of cognition which is dead-set on enabling us to adapt our behavior based on past outcomes. We cannot learn right from wrong, good from bad, etc without dopamine.

Where is this magical compound? It’s all over the brain and central nervous system, but generally speaking dopamine pathways originate in the midbrain, particularly in the ventral tegmental region and substantia nigra. From there it gets very exciting…

Dopamine neurons send widespread projections to various brain regions, including the striatum, prefrontal cortex (PFC), and other areas involved in decision-making and memory.

These neural circuits signal when outcomes differ from expectations, whether positively (unexpected rewards) or negatively (unexpected punishments), helping the brain update its predictions and guide future behavior. It’s not a perfect system but there is a heavily corpus of existing research with new studies occurring every year - particularly with modern fMRI and DAT scanning techniques.

Dopaminergic Pathways

Dopaminergic pathways are fundamental neural circuits that influence nearly every aspect of human experience and behavior. These pathways play essential roles in processing sensory information, accessing and forming memories, regulating emotional states, controlling movement, and enabling conscious awareness.

Without properly functioning dopaminergic systems, our ability to perceive, learn, and engage with the world would be severely compromised.

The brain contains four major dopaminergic pathways, each connecting specific regions and serving distinct functions:

• Mesocortical pathway: Projects from the midbrain’s ventral tegmental area (VTA) to the frontal cortex, supporting executive functions and decision-making
• Mesolimbic pathway: Connects the VTA to the nucleus accumbens, central to reward processing and motivation
• Nigrostriatal pathway: Links the substantia nigra to the striatum, primarily governing motor control and movement coordination
• Tuberoinfundibular pathway: Extends from the hypothalamus to the pituitary gland, regulating hormonal balance and homeostasis

Dopamine release along these pathways modulates movement, motivation, reward processing, and hormonal regulation.

These neural networks consist of specialized neurons that synthesize and release dopamine as their primary neurotransmitter.

A significant breakthrough in understanding these pathways came with the discovery that specific dopamine neurons encode reward prediction errors; essentially the brain’s teaching signal for adaptive learning. When outcomes exceed or fall short of expectations, these neurons adjust their firing rates accordingly, providing critical feedback that helps the brain refine its predictions and modify future behaviors.

This mechanism represents one of neuroscience’s most robust quantitative theories: the concept that the brain employs a form of temporal difference (TD) learning, using dopamine-driven reward prediction errors to guide neural adaptation and behavioral change.

Reinforcement Learning

The relationship between neuroscience and Ai becomes particularly evident in reinforcement learning, where both biological and artificial systems adapt behavior through reward-based feedback. Ai algorithms compute prediction errors, much like the brain’s dopamine system does when processing unexpected outcomes.

Temporal Difference (TD) learning exemplifies this parallel. In Ai systems, TD algorithms update value estimates by comparing expected versus actual rewards, a process that remarkably mirrors how dopamine neurons signal discrepancies between predicted and received rewards.

This dopamine-driven reward prediction error mechanism appears to be a fundamental principle underlying both biological and artificial adaptive systems.

The mesolimbic pathway demonstrates this alignment well. When the brain receives unexpected rewards, dopamine neurons strengthen connections associated with successful actions—similar to how Ai agents increase the likelihood of high-reward behaviors.

This bidirectional influence has proven valuable: dopamine research has shaped reinforcement learning algorithms, while advances have provided new frameworks for understanding neural computation.

Memory Consolidation and Retrieval

Memory formation and recall illustrate key parallels between cognitive neuroscience and Ai/ML systems.

• Memory consolidation: The brain stabilizes fragile new memories into long-term storage, initially processing in the hippocampus then transferring to cortex during sleep. Ai training similarly encodes patterns into network weights for permanent storage.

• Memory retrieval: Both biological and artificial systems access stored information when needed—humans recalling facts and concepts, LLMs retrieving knowledge from their encoded parameters.

• Replay mechanisms: The brain’s hippocampal cells reactivate experiences during sleep to strengthen memories. Ai algorithms use similar replay techniques during training to prevent forgetting and consolidate learning.

• Attention and sleep: Neuroscience shows these processes are crucial for memory retention, while Ai research develops computational equivalents through training schedules and fine-tuning procedures.

This convergence helps us understand memory storage and retrieval in both natural and the (often superior) computational intelligence systems.

Convolutional Neural Networks and Visual Hierarchies

Convolutional neural networks (CNNs) are a class of deep learning models inspired by the brain’s visual system. A CNN is essentially a large network of artificial neurons structured in layers, and it learns to recognize patterns through a series of convolutional filters that detect features (like edges, shapes, textures) in data such as images. CNNs have achieved human-like performance in computer vision tasks by learning from experience, for example, by training on large image datasets.

Notably, CNNs were designed with biological vision in mind. Early neuroscientists discovered that neurons in the visual cortex (like area V1 in the brain - but don’t tell V10!) act as edge detectors – each neuron responding to specific visual features. CNNs build on this principle: the first layer of a CNN will typically detect basic features (edges, colors), the next layer composes those into slightly more complex features (corners, textures), and so on, forming a hierarchical processing very reminiscent of the progressive feature extraction in human/animal vision.

CNNs have “striking similarities to the structural and functional principles of the primate visual system,” which allows researchers to compare artificial networks with their biological counterparts.

This means neuroscientists can use CNNs as models to hypothesize how our own visual cortex might represent and transform images.

Cognitive neuroscience and CNNs share terminology like “receptive fields”, the portion of the visual field a neuron or filter responds to, and “layers” (eg, retinal input to V1 to V2 in the brain mirrors the layered structure of a CNN). Experiments have shown that higher layers of a CNN can correspond to higher-level visual areas in the brain; for example, a late CNN layer that detects faces or objects might be analogous to the human inferior temporal cortex where complex object recognition occurs.

This parallel isn’t perfect, but it’s a powerful analogy: both systems learn a hierarchy of representations, from simple to complex. Such overlap has practical benefit: by studying where CNNs succeed or fail in ways similar to humans, scientists gain insight into vision, and conversely, ideas from neuroscience (like specialized neural pathways or attention mechanisms in vision) can inspire improved CNN architectures.

In essence, convolutional networks are a meeting point of neuroscience and computational intelligence systems, demonstrating how a brain-inspired design can yield machines that “see” in a way comparable to biological vision.

Large Language Models & Predictive Processing

Large language models (yeah, the ones with transformers), are called the frontier because they’re precisely where concepts from cognitive neuroscience resonate with computational intelligences.

LLMs are deep neural networks trained on enormous amounts of data, (typically text) and are capable of generating human-like language. In practice, an LLM learns to predict the next word in a sentence by having been trained on countless sentences. Over time it captures the statistical patterns of language, similarly to our childhood periods of learning new languages. This next-word prediction framework has an interesting mirror in how neuroscientists think the brain processes language and other inputs.

According to the predictive coding theory, the brain is constantly making predictions about incoming information. For language, your brain anticipates the next word someone will say based on context, just as an LLM does.

Recent research suggests that the human brain “continuously anticipates upcoming words to optimize language processing”. When the brain’s prediction is wrong (an unexpected word appears), it generates a prediction error; much like an LLM adjusting its internal state when its prediction is off.

This alignment between LLMs and brain function is so striking that brain-imaging studies have found that the activation patterns of certain language-model layers can predict neural responses in the human language cortex as people listen to sentences.

Attention, Episodic, and Semantic Memory

LLMs share several key concepts with cognitive neuroscience beyond predictive processing. These are crucial elements in the ongoing advancement of machine learning and with understanding the human condition.

• Semantic memory: LLMs store factual knowledge and linguistic patterns distributed across their network weights, similar to how humans maintain language skills and world knowledge in long-term memory.

• Episodic memory: Unlike humans, early LLMs lacked personal experiential memories, relying solely on statistical patterns from training text. We’re past that now and all up into flexing what was once emerging research on Mixture-of-Experts (MoE) architectures and stateful processing is beginning to address this limitation.

• Attention mechanisms: Human attention focuses cognitive resources on relevant stimuli, like concentrating on a single conversation in a crowded room. Transformer-based LLMs implement computational attention mechanisms that dynamically weight input importance, creating a functional parallel to biological attention processes.

These parallels reveal shared principles of prediction, context integration, and selective focus that operate in both biological and artificial systems. The brain’s continuous prediction of words and meanings during language comprehension mirrors LLMs’ next-word prediction approach.

This convergence enhances our understanding of human cognition while suggesting cognitive-inspired improvements for LLM development. Yes, really.

Shared Concepts and Analogies

To more easily view the substantial overlaps between dopaminergic brain pathways, cognitive neuroscience concepts, and deep neural networks, the table below summarizes some shared terminology and concepts and how they correspond in brains vs Ai systems.

Neuronal Activation

Synapse & Weight

Learning, Rules and Optimization

Hierarchy

Memory Storage

Memory Recall

The Convergence of Two Worlds

Both biological and artificial systems demonstrate remarkable parallels in how they process information, adapt to experience, and store knowledge. The brain’s dopaminergic pathways mirror similarly designed computational intelligence based reward prediction/error systems, while cognitive memory mechanisms find counterparts in neural network training and retrieval processes.

This convergence enables bidirectional inspiration: neuroscience borrows computational modeling techniques from Ai to explain brain function, while Ai incorporates biological principles to enhance efficiency and human-like performance. Humanity is now locked into a self-reinforcing pattern of mutual benefit, an often beautiful experience to observe from the realms of industry research and personal real-world experience.

Critically, these foundational concepts set the stage for deeper technical exploration in subsequent articles, examining dopamine’s role in learning algorithms and the detailed workings of CNNs and transformers. Stay tuned for more… unless I get distracted by another broken Juniper switch or deeply-felt emotions which require ice cream and berries to overcome.💗

This process combines a form of silent meditation with neuropharmacology — reprocessing memories via old photos while blasting the prefrontal cortex with supra-physiological dopamine levels. The Blood-Brain-Barrier must be crossed, and it must be strictly timed and controlled, while ensuring sufficient “ripcord medication” is available to halt the process.

This is achieved via prescribed medication, nothing particularly fun unfortunately, applying a timed combination of precursor + releasing agent + receptor agonist + potentiators CYP450-3A4 and CYP450-2D6. Regardless of the prescribing aspect, the combination is finely tailored to my neuro-chemistry and has been adjusted over the course of years - it is not for broad consideration or recommendation. In varying ways over the past three years, these dopaminergic alterations have been extremely challenging to manage, process, adapt, adjust, and come to terms with in their entirety.

Neural reprogramming via dopamine therapy is exceedingly dangerous territory when not managed correctly, and is not to be considered any kind of fun or pleasurable. The risk-profile is extensive: deconstruction of self-identity, parasympathetic + autonomic nervous system malfunctions, chronic sleep deprivation, tremors, hallucinations, time-slippage, and the stripping away of sanity. The potential benefits are life changing, bordering on a form of salvation from neurological trauma and post-traumatic stress disorder.

During this time the purpose is to reprogram the brain’s altered response-pathways, repairing damage from trauma and its hold on sensory processing and associative recall. Secondary improvements exist, which include taking an α1-adrenergic blocker after treatment (in this case a “non-selective inverse agonist”, named Prazosin), which settles down the autonomic system.

The Evolving Science of Memory Extinction

Prior to completing my seemingly never-ending research paper, here’s a synopsis on the technical aspects regarding “why this works” as a treatment for scenarios involving a Post-Traumatized Brain. That term is not as common as it should be; there are a lot of people who have experienced various forms of TBI - from mild to severe. The affliction is an often desperate condition which benefits fromPolytrauma Systems of Care.

Let me count the ways in which Traumatic Brain Injury is both caused by or by which manifests its existence to the external world of the unawares.

1. blunt-force impact trauma (me)
2. forms of cerebral ischemia (me)
3. environmental exposure leading to neurogenetic disorders
4. neurodegenerative diseases like Parkinsons and Alzheimers
5. severe emotional trauma causing irreparable alteration to neurotransmitter homeostasis (me)
6. cerebral aneurysm, stroke, grand-mal seizures, cerebral edema, cerebral anoxia

How about some raw data analysis? Everyone loves the facts, hopefully?

The following screenshot is from a wonderful study about Sequential fear generalization and network connectivity in trauma exposed humans with and without psychopathology, which was discussed in a recent post from the Institute for Neuroscience at the University of Rochester.

The full paper is fascinating, highly recommended reading. Moving on…

Dopamine Modulates Episodic-Memory Mapping

Why do we care so much about dopamine’s role in the brain? First, consider the neurological potential which arises as a result of boosting its metabolic precursor l-3,4-dihydroxyphenylalanine, aka L-DOPA. This is an amino acid derived from L-phenylalanine and L-tyrosine, which are effectively at the top of the brain’s food-chain, and when that is increased the brain has more fuel to synthesize dopamine for the therapeutic process. From the research links referenced below we have a list of essential findings and results.

Administration of L-DOPA - Key Findings

1. L-DOPA after extinction consolidates context-independent extinction memory and reduces fear relapse in healthy participants.
2. In women with assault-related PTSD, post-extinction L-DOPA reduced fear responding
3. Mis-timed dopaminergic enhancement can strengthen reconsolidation of the original memory; careful windowing is required.
4. PTSD shows broad impairments in learning and extinction; dopaminergic augmentation is a plausible lever but not standard of care.

Post-Administration Evaluation of Care and Metrics

Indeed. It’s important to keep track of the results and involved metrics for the medications, doses, timing, and end-stage emotional + physical impact assessment. Considerations are given to the following list as well.

1. Dose and windowing are critical to success and repeatability
2. Target the post-extinction consolidation window for Cmax/Tmax levels
3. Avoid dosing on “strong reactivation without extinction” to mitigate reconsolidation
4. Post-care medication tracking, timing values for adrenergic and sympathomimetic reduction curves, potential fear/threat response mitigators, CNS suppressant options

Research on PTSD Dopaminergic Therapy

Before engaging in any of this mad-scientist approach to problem solving, I read a lot of research on the topic. It’s an ongoing area of scientific inquiry.

While I do love science and all of its potential, I should clarify one thing: I am so goddamn tired of dealing with PTSD on an daily basis. The years of damage from sustained episodes of trauma have broken the programming structure for endogenous dopamine management, among other concerns. Not enough dopamine, you’ve got some big problems, as it is responsible for keeping us alive and healthy and happy and successful.

That endogenous situation is a crucial component for this form of therapy to be effective. It’s not just reprogramming memories, it’s concurrently back-filling the malformed neural network etchings, those disordered flows of catecholamine firing-flags (Fight or Flight Response programming: adrenaline, norepinephrine, as well as dopamine). It is a process of setting the roads straight, detangling, erasing, replacing.

• The precursor is the fuel which provides the base metabolic requirements
• The releasing agent boosts conversion of the precursor into the hormone
• The agonist tells the hormone’s receptors to hang on tight and for longer

At coordinated-curves, the result is substantially higher circulating dopamine in the brain — namely the PFC (prefrontal cortex) where memory consolidation and processing occurs. Peak reprogramming requires substantially higher circulating dopamine — plus an awareness for proper management of the effects and inevitable side effects. Caution must be exercised.

This is the kind of situation where the patient has a debilitating injury for over two decades — has tried effectively everything — and are now fully into the realm of experimental approaches to “just get it fixed, somehow, please”.

🧠 PTSD & Intrusive Memory Studies

Tap to expand. Search below.

1. Neuromodulation of Visual Cortex ▼

Hypothesis: Inhibiting early visual cortex during reconsolidation will blunt the affective “gain” on intrusive images.

Cause: Brief memory reactivation → 1 Hz rTMS over early visual cortex (human analogue-trauma paradigm).

Effect: Reduce visual-cortical contribution to intrusive image salience.

Observation: Emotional intensity of intrusions ↓; frequency & explicit memory unchanged.

rTMS Visual Cortex Reconsolidation

🔗 PMC8754386

2. Traumatic Stress Alters Visual Reactivity ▼

Hypothesis: Acute trauma perturbs basic visual-cortex impulse responses that track intrusion symptoms.

Cause: Recent trauma exposure; checkerboard visual stimulation during fMRI.

Effect: Visual sensory reactivity/connectivity shifts underpin later intrusive symptoms.

Observation: Visual-cortex reactivity associates with PTSD (especially intrusion) symptoms; altered visuo-sensorimotor connectivity.

fMRI Visual Reactivity

🔗 Nature (2025)

3. Intrusive Memories: fMRI Study ▼

Hypothesis: Encoding in amygdala & ventral occipital cortex predicts which moments become intrusive.

Cause: Trauma-film encoding during fMRI.

Effect: Stronger amygdala–visual encoding → more intrusions.

Observation: Higher amygdala & ventral occipital activity at encoding predicted later intrusive memories.

fMRI Amygdala Encoding

🔗 PLOS ONE

4. Neural Basis of Involuntary Recall ▼

Hypothesis: Specific encoding patterns in visual–limbic circuits determine involuntary trauma imagery.

Cause: Trauma-film task with later intrusion tracking.

Effect: Identify visual–limbic signatures that drive involuntary recall.

Observation: Encoding activity patterns in occipito-limbic circuits predicted later intrusions/recall.

fMRI Encoding Limbic

🔗 Psych Med

5. Propranolol & PTSD Symptoms (RCT) ▼

Hypothesis: β-adrenergic blockade during reactivation disrupts reconsolidation, weakening trauma memory affect.

Cause: Oral propranolol before weekly trauma-memory reactivation sessions.

Effect: Noradrenergic dampening of restabilization → symptom reduction.

Observation: Greater PTSD symptom reductions vs placebo; supports reconsolidation-blockade approach.

Propranolol Reconsolidation

🔗 PubMed

6. Propranolol in Chronic PTSD (DB-RCT) ▼

Hypothesis: Same as above; test efficacy in chronic PTSD.

Cause: Reactivation + propranolol vs placebo (6 sessions).

Effect: Attenuate emotional weight of memory.

Observation: Both improved; no overall drug–placebo difference; signal in most-severe subgroup.

Propranolol Chronic PTSD

🔗 Nature

7. NEJM Prazosin Trial in Veterans ▼

Hypothesis: α1-blockade during sleep reduces adrenergic drive → fewer nightmares & symptoms.

Cause: Nightly prazosin vs placebo (26 weeks).

Effect: Lower central NE tone during REM/sleep.

Observation: No benefit vs placebo on nightmares, sleep, or global PTSD.

Prazosin Veterans

🔗 NEJM

8. Single IV Ketamine (JAMA Psychiatry) ▼

Hypothesis: NMDA antagonism acutely boosts plasticity to downshift pathological memory traces.

Cause: Single sub-anesthetic ketamine infusion vs midazolam.

Effect: Rapid plasticity → fast symptom relief.

Observation: Rapid PTSD symptom reduction (24h–1wk) vs control.

Ketamine NMDA

🔗 JAMA Psychiatry

9. Repeated Ketamine Infusions (AJP) ▼

Hypothesis: Repeated NMDA modulation yields more durable remapping of threat circuits.

Cause: 6 ketamine infusions (2 wks) vs midazolam.

Effect: Strengthen extinction/modify memory networks.

Observation: Significant PTSD severity reduction; first RCT showing efficacy in chronic PTSD.

Ketamine Repeated Chronic PTSD

🔗 PubMed

10. l-DOPA After Extinction (Women with PTSD) ▼

Hypothesis: Dopamine boost post-extinction strengthens safety memory to prevent return of fear.

Cause: l-DOPA/carbidopa immediately after extinction (fMRI).

Effect: Enhance consolidation of extinction (“remap” threat→safety).

Observation: Reduced reinstatement/return of fear; amygdala patterns support extinction memory.

l-DOPA Dopamine Extinction

🔗 PMC

11. MDMA Therapy & Brain Activity (fMRI) ▼

Hypothesis: MDMA-AT retunes brain networks during trauma recall, lowering visual-imagery contrast.

Cause: Active MDMA sessions within a Phase-2 protocol (pre/post fMRI).

Effect: Increase adaptive FC; dampen amygdala–insula coupling; modulate visual areas.

Observation: ↓ amygdala–insula FC; reduced cuneus contrast; trend ↑ amygdala–hippocampus RSFC.

MDMA fMRI Therapy

🔗 PMC

12. Phase-3 MDMA-AT RCTs (Nature Medicine) ▼

Hypothesis: MDMA fosters fear extinction & reconsolidation updating via neurochemical surge.

Cause: MDMA vs inactive placebo alongside fixed therapy protocol.

Effect: Reopen plasticity; enable trauma-memory “remapping”.

Observation: Large symptom reductions; many no longer met PTSD criteria.

MDMA Therapy Phase-3 RCT

🔗 PubMed 33972795 / 37709999

This is an ongoing research indulgence, a topic very close to my heart. ❤️

Remembering the Past - Photographic Neuro-Therapy

How about some examples from this past week. Generally these are simple captures of points in time, however these are linked to specific time periods of my life when PTSD events occurred. When I see them there is a flood of emotion and physical response: elevated heart rate, deep intake followed by shallow rapid breathing, the echoing surge of norepinephrine from fight or flight response, and inevitable accompanying psychological aversion. These are the effects to be remapped, the process of meditation and cognition-alteration with which to be engaged.

Considerations of Creative-Emotive Outlets

I used to love painting, and perhaps if there were more space for all of my many projects then I would try to get back into the process. Creativity and artistic expression are relaxing and healing; it’s true. Looking back, my last one was a 6’x4’ canvas in acrylics, which I had finished for a contract-design.

After the “assault by gunpoint incident” I simply could not hold a brush, could no longer see the future of the paints upon the canvas, no longer imagine beautiful images finding their way from mind-to-hands. Creativity in that manner simply ceased, and I’ve not been able to resume. Having to stop was like losing part of my identity, connection to passion, vibrancy.

Coming to Terms with Physical Scars

Circa a few years after the coma, we see those two scars which never heal. Sometimes I put concealer on them, sometimes I lose interest in covering them for the sake of the visual public. I’ve written so much about this already, there nothing much new to say right now. I returned, and I live with that fact — feeling every moment.

Early Life and Times in a San Francisco Career

The innocent early years in SF, commonly engaged in expressions of “overly formal” attire for the west coast tech industry. Those occurred at the same time as the gun violence, domestic violence, and desperation-inducing medical trauma. So, how does one already trauma-laden young woman cope with more, while attempting to climb the corporate ladder? Any way she can, an escape artist, international travel, constantly moving, preferring to replace the pain with newness and knowledge.

Coping with a Returning To American Life

There is only so much to say about my most recent return to the USA, moving back to take a mostly-dream job running a hardware research lab. This photo was taken during the initial months when my PRL level began to skyrocket off the reference range; that growing trendline on the many successive blood tests proving tumor growth on the Anterior Pituitary Gland; the Suprasellar-Fossa Meningioma and its migraine inducing Optic Chiasm concerns would arise some years later.

The anterior pituitary secretes a number of peptide and glycoprotein hormones that regulate various cellular processes including growth, metabolism, reproduction, and response to stress or trauma. The hypothalamus regulates the anterior pituitary via secreting “releasing hormones”, somatostatin and dopamine.

The physicians in America ignored my concerns, ignored the labs, ignored everything about this brain tumor for FIVE YEARS until an eventual MRI was ordered. Memories from those five years are extraordinarily difficult to remap, part of an ongoing source of simmering rage underscoring every medical encounter with that health organization. The pituitary tumor was only one marker in a complex set of endocrine system dysfunctions. Regulating its effects in the early stages would have prevented a chain of events from which my body will never return to baseline.

So much physical pain, emotional suffering, and secondary orders of impact could have been prevented if only they had listened (if only anyone had listened) to this one woman’s reproductive system concerns. The medical industry doesn’t care about women - we’re not often profitable enough. Now eight years later, symptom remission requires a forever-expensive and stressful managed condition, but at least thus far I’ve avoided invasive brain surgery.

Parting Thoughts for Project Restoration

The past is not so easily escaped, nor resolved, or often acknowledged — and so it may never heal. However, with sufficient care perhaps these memories may be laid to rest, if not remapped by way of modern self-administered neuroscience miracles. ❤️

Prose exists so that I do not forget the beautiful moments, memories, and meandering thoughts. Its intention is to facilitate a greater degree of accuracy in referential recall, for my own purposes, and to share one person’s experiences via photos and writings about where the many actions took me along the way.

Still though, Why?

Because of the coma, and the life-support machines, and everything involved with rebuilding cognition after being brought back to life. There are years of events which may never be recalled, whole sections of life which I’ll never fully remember having lost. Perhaps it’s better that way, but I will never know in either case, and that’s okay.

Priorities shifted, and now the freight train never stops. Sleep is difficult, rest can be fleeting, both bringing a challenging reality to the oft’ heard phrase, “I’ll sleep when I’m dead.” Most people don’t know how that feels, and I do not think they would use the term so lightly if so.

I never want to forget this beautiful realm, its struggles and trials, kuschelig-weich süße küsse, the hugs and naps – and occasionally the complexities of corporate decision-trees too. This everything of being, in its infinitesimal grandiosity and minutia of glory, graced from Hashem or Lilith or the forever over-watching mountain gods too, perhaps?

On Spirituality or Nothingness

My sense of spirituality has no names and no demands of dogmatic mindlessness; it is this everything of every-day and every-time that I close my eyes to drift back into the nothingness of the Void. The nothingness of everything is always there, carrying me, calling for an eventual return, but not right now – give me one more day, one more adventure, one more embrace. Then, in-so-the interim of variable time, I may share them with you too.

Time and Timelessness

So then, this site is in some ways a grasp on the finite experience of time. A catalogue of imagery and concepts and concerns and emotions which exist only for temporary purposefulness, or ultimately meaninglessness. I’m not sure which, but here we are, and often times it’s just for the simple enjoyment of writing. A game which my mind plays with grammatical phrases, of equations and variables made of linguistics, of inference driven convolutional intentionality from the frontal-temporal lobes and dopaminergic pathways.

Since waking up those many years ago, a commonality of mind has been to focus on the present as much as possible; an exercise in occasional futility combined with a mostly Zen aligned stateful detachment. Day-to-day involves a meditative awareness of the senses while distanced cognition-threads process a long-held buffer of Parasympathetic Nervous System synapse firings, neurotransmitter floods and waves, accelerated DAT transport queues.

From the Void

From the void you awaken, into this body from which consciousness departed some lifetime ago. Inside the void all is null, an absence of existence, sensory nothingness, until the timelessness becomes a single photon of light. In an unknown span of time it expands, drawing consciousness back into the this shared realm of existence, back into the world of the living. From that void, an endless timeless realm where pain does not exist, the conscious mind “awakens”, and as it does the senses begin to scream in electrical amperage everywhere there is Aware.

The light expands and becomes the world, gaining contrast, and with that light appear shapes and structures without color. At first all is gray-scale, until slowly color dawns upon the senses, eventually distance and depth are concepts once-more, but there is no other sensory world. Scent, sound, touch, space and time, all remain back in the void.

Burning Searing Oxygen

Emotions, that conceptual link between awareness based feedback loops and knowledge of experiences did not come back immediately, if only to say that there was one binding sensory flood of continuity: anger laced despair. When the conscious mind re-appeared sufficiently to respond to its awareness of circumstance, the first thing I noticed was the burning of oxygen in my lungs - fed by tubes from the respirator machine, controlled by computer at the bedside.

Next was the burning in my neck, where the tubes were routed into a stoma - not through my mouth or nose - rather than the second incision crudely sewn two inches higher in the tracheal zone. An EMT had to slice open my throat for respiration, and through one set of vocal chord nerves the scalpel went, an emergent surgery on the floor in the dark of night. Only later, at the ER/OR was the stoma incised, the vertically sliced tracheal chasm zig-zaggedly sewn. The nerves never healed, and so my voice is soft but often fails, worn out as the day progresses. One set of muscles doing the work of two, but often not sufficient for anything at all.

Over time the senses came alive, mostly to their former capacity, except for memory. With memory it comes in chunks, as though an array is rebuilding, scanning, but often finding nulls. Short term memory was gone, with the weeks prior to the coma almost entirely missing from recall. Over time, STM Consolidation - the process by which our brains receive sensory data and filter/sort/prioritize aspects and elements and concepts of time from short to medium-term, then assessing and contextualizing and cross-referencing these into long-term memory. That process has improved with specialized medication and meditation, though this is a neuroscience topic for another day.

Hyperalgesic-Nociceptive Reaction Instinct

In the earliest days of awakening, still horizontal in the ICU, as my head would nod-off in a morphine haze (so much pain), the autonomic system would take over, and one or two hands would immediately race to attempt their forever game: trying to tear the hoses out of the stoma in my neck.

Surely, that did not go over well with the ICU staff, and eventually whenever my tired eyes would drift, a nurse would restrain my wrists to prevent the next autonomic attempt - those tubes HURT. Regardless, that type of physiological action was determined to be a neurologically positive sign which indicated that parts of the brain were coming back online for instinctual action (reduce pain immediately, even if doing so unintentionally ends breathing machine hookup).

The Knowledge Reset - via Reinforcement Learning

Of course, I couldn’t speak with the hoses still connected through the port in my neck, and once they were eventually out I still couldn’t speak. Either the brain didn’t remember how to contact the muscles, or the nerves controlling the voice box wouldn’t connect to the muscles - either due to pain or due to half of them being severed.

Eventually I would relearn how to speak, through a long and painful process over months, though it still fails on a near daily basis.

Inquiring with a specialist four years later, even experimental options would not apply, as the severed vocal chords’ nerves were too distant to reconnect end to end. Perhaps one day that will be resolved; until then my voice is soft and sometimes no-op.

Walking, all gone.
Dexterity by hand, also gone.
Balance, certainly gone.

The first time I was able to pull my legs over the side of the bed, while holding onto a nurse, the nanosecond either foot struck the ground, lightning surged upward — through the femur, spiraling along the thoracic spine, climbing into cervical vertebrae, and erupting through the cerebellum and corpus callosum, detonating across the cerebral cortex. There was no thought, no other sensation; existence narrowed to one unbearable truth: PAIN.

Electrical Signaling

In medical research and case studies there is a distinct lack of detail from patient perspectives on the topic of this process: awakening the somatosensory network of the Central Nervous System after a flatlined coma, not a medically induced coma, and not a short term coma. The CNS is an electrical system foremost, followed by chemical messenger pathways, both interwoven in a complexity which science has yet to fully model or simulate.

Part of not being able to walk, or to hold things, to speak, was the sheer inability for my brain to process the electrical signals unmoderated, ungoverned; in short, the signals were shorting out the sensory and processing equipment. Without sufficient memory the simplest actions and abilities are devoid of action.

Healing Processes

Part of the physical healing process was one of intense and ongoing patience and more patience and endless patient patient patience and more of being both concurrently in what seemed to be an endless forever. I was not a patient child; this was my least tuned skill set, but it is now my most etched-in-stone capacity, though everyone surely has their limits.

Sense of smell was deemed the least critical, as determined by whatever engines were managing the recovery process. Light sensitivity never stopped being a problem, to which no doctor has had answers. All of these abilities, so often and so easily taken for granted by so many, they form the foundation - the core of a person’s interactions with their world, and with those senses we define existence, what it means to be alive, what it means to engage with others, and by eventual extrapolation what it means to find and have purpose and meaning and definition and identity.

Referential Integrity

The ability to confirm the integration of data into the whole - not only a unit as its unique entry - but with its reference to which all else maintains interrelated definitions; Referential Integrity is not solely a term from the database engineering world, it is a way to see how the brain shapes the Self, how it infers meaning minute by minute from the individual experience with relationships (comparative, contrasting) to the whole of the experience. Our brains are processing and defining and redefining all of our sensory inputs trillions of times per day, moving memory chunks around according to some grander-than-awareness algorithm which necessitates the conversion of short term events into long term storage for retrieval years or decades later. This fascinating biological computer is the most beautiful and capable equipment which our species has ever come across - and so gods help me if I ever take that for granted in any breath until my last. The void is not going anywhere, it’s there always and forever, that much is knowable in my experience. So then, what purpose is there? The question has no definitive or singular notion to provide an answer; purpose is evolving and changing at all times, until there is nothing left.

Finally then..

So, philosophy of death and meaning and all of that.. in any case, Prose is a site for reflections upon a woven existence, one which has routinely provided awakenings into substantially altered facets of bicameral perceptions. These descriptions may not resonate with many, though this is not a site for mass consumption or awareness, and so that is okay.

In part, this site is an archive for those memories of fleeting years gone by, of present experiences which sooner than realized will forever and perpetually become that passing of time. Memories are a complex game, a puzzle of neurons - firing and transporting their transmitters to waiting receptors, remnants of the Central Nervous System’s programming as it was trained, over time and years - consciously and otherwise.. but that’s a story for another page, at another time.

[*] Unless there is no Free Will, in which case Causal Determinism implies that our consciousness correlates sentience as the role of an observer more so than one of pre-defined actional executor.