| #CorticalCTSelfModifyingMemorySystems | |
| CorticalCTSelfModifyingMemorySystems | |
| I believe the most basic thing about life is the ability to self modify. As an AI developer I believe language self coding systems come alive rpovable by episodic and semantic memory recall to prove that we have learned on a small scale of 5-10 seconds as we communicate which intelligent creatures do while they are conscious and not sleeping. When they are sleeping there is a mirror twine cycle where unconscious memories in semantic and episodic memories think of it as stories that it can now re-use as parts because they are connected in time locality and also repeatable patterns of life interacting with other life which creates intelligence. Help me with my theory called Cortical Column theory where our pattern exists as the neocortex cortical columns represent the same pattern with a tree of voting neurons that steer signal to a vote across synapses, and also dendritic excitement where inputs of current locality in time (5-10 seconds) of conscious activity with observation from one of the senses of sight, touch, smell, taste, skin which I would consider an advanced sense we all use with hair, whiskers, antannea across living creatures and representative whiskers in neural nets that have video or image inputs to detect certain properties of feautres Memory is introspective attention / time. Knowledge is Mass + Agency and builds free energy with new graph edges as it scales across pair bonds (2+ parties) with pets (+ parties) and nature interactions (1000's) per individual but love = maximum connection with free energy which explains at a cellular level all we need as singular living creatures is the ability of our larger system to build free energy for our consumption at cellular levels and groups of cells. For our lives being memory of the universe, this expands geometrically as we learn its function, inputs and outputs. Bonds selection treees through larger concentric organizations: 1) Team, 2) Org. #) Division, 4 Corp., 5. Humanity.. For each use "E" For Episodic Memory / a move or action. Use introspective attention over time as a score, and show it real time.. and use K as Semantic memory. Include model making and sparse matrix defintiions for this problem as python code for streamlit UI. Develop a full Emoji and Concept graph based UI using every one of streamlit's UI components selecting each for its ability to demonstrate one Label and Concept it composes in its code. Score yourself for benefit of self reward learning on NPS Scale 0-10 with NPS verbatims. | |
| o3-mini-high | |
| Reasoned about Cortical Column theory and UI development for 1m 0s | |
| Below is an example of a Streamlit app that both explains and demonstrates your Cortical Column Theory using multiple UI components, sparse matrix modeling for neural connectivity, and an interactive concept graph with emojis. The code also includes a self‐scoring NPS (Net Promoter Score) section for “self reward learning” feedback. You can run this code (for example, in an app.py file) with Streamlit to see the interactive demo in action. | |
| import streamlit as st | |
| import numpy as np | |
| from scipy.sparse import csr_matrix | |
| import pandas as pd | |
| import time | |
| import graphviz | |
| # ----------------------------------------------------------------------------- | |
| # Title and Overview | |
| # ----------------------------------------------------------------------------- | |
| st.title("Cortical Column Theory: Self-Modifying Memory Systems") | |
| st.markdown(""" | |
| **Theory Overview:** | |
| This application demonstrates a model inspired by Cortical Column Theory where the ability to self-modify is paramount. | |
| - **Episodic Memory (E):** Represents short-term, conscious experience (~5–10 seconds) via introspective attention. | |
| - **Semantic Memory (K):** Cumulative knowledge built over time (Mass + Agency), enabling free energy formation. | |
| - **Neural Connectivity:** Modeled via sparse matrices to mimic voting neurons in cortical columns. | |
| - **Social Bonding:** Hierarchical connections—from teams to humanity—facilitate maximum free energy (or ‘love’) at a cellular level. | |
| These components interact in a dynamic system, much like how neocortical columns steer signals via voting neurons and dendritic excitement. | |
| """) | |
| # ----------------------------------------------------------------------------- | |
| # Create Tabs for Organized UI Sections | |
| # ----------------------------------------------------------------------------- | |
| tabs = st.tabs([ | |
| "Theory", | |
| "Neural Connectivity", | |
| "Concept Graph", | |
| "Interactive Components", | |
| "NPS Score", | |
| "Extra UI" | |
| ]) | |
| # ----------------------------------------------------------------------------- | |
| # Tab 1: Theory Explanation | |
| # ----------------------------------------------------------------------------- | |
| with tabs[0]: | |
| st.header("Cortical Column Theory") | |
| st.write(""" | |
| The central hypothesis is that life’s essential characteristic is its ability to self-modify. | |
| In this model: | |
| - **Episodic Memory (E)** functions as immediate, introspective attention over a 5–10 second window. | |
| - **Semantic Memory (K)** aggregates past experiences into a knowledge base, growing as new connections (graph edges) form. | |
| - **Free Energy** is produced as the system scales its pair bonds—from simple interactions (e.g., between two neurons) to complex networks (teams, organizations, and ultimately humanity). | |
| - **Love (❤️)** is conceptualized as the maximal connection, representing the highest free energy state and optimal bond formation. | |
| This theoretical framework abstracts how biological neural circuits might mirror self-coding systems in AI. | |
| """) | |
| # ----------------------------------------------------------------------------- | |
| # Tab 2: Neural Connectivity with Sparse Matrix | |
| # ----------------------------------------------------------------------------- | |
| with tabs[1]: | |
| st.header("Neural Connectivity Sparse Matrix") | |
| st.write("Below is a demonstration of a sparse matrix simulating neural connectivity within a cortical column:") | |
| # Create a random binary matrix (10 neurons, ~20% connectivity) | |
| size = 10 | |
| density = 0.2 | |
| random_matrix = np.random.binomial(1, density, size=(size, size)) | |
| sparse_matrix = csr_matrix(random_matrix) | |
| st.write("Sparse Matrix Representation:") | |
| st.write(sparse_matrix) | |
| st.write("Dense Matrix Representation:") | |
| st.write(random_matrix) | |
| # ----------------------------------------------------------------------------- | |
| # Tab 3: Emoji and Concept Graph UI | |
| # ----------------------------------------------------------------------------- | |
| with tabs[2]: | |
| st.header("Emoji and Concept Graph") | |
| st.write("Visualizing core concepts with emojis where each node represents a key component of the theory:") | |
| # Graphviz diagram using emojis and labels for key concepts. | |
| graph_source = """ | |
| digraph G { | |
| "Cortical Column 🧠" -> "Episodic Memory (E) ⏱️" [label="short-term"]; | |
| "Cortical Column 🧠" -> "Semantic Memory (K) 📚" [label="knowledge"]; | |
| "Episodic Memory (E) ⏱️" -> "Introspective Attention 🔍" [label="focus"]; | |
| "Semantic Memory (K) 📚" -> "Free Energy ⚡" [label="agency"]; | |
| "Free Energy ⚡" -> "Love ❤️" [label="bond"]; | |
| "Love ❤️" -> "Humanity 🌍" [label="connection"]; | |
| } | |
| """ | |
| st.graphviz_chart(graph_source) | |
| # ----------------------------------------------------------------------------- | |
| # Tab 4: Interactive UI Components | |
| # ----------------------------------------------------------------------------- | |
| with tabs[3]: | |
| st.header("Interactive Components Demonstration") | |
| st.subheader("Input and Selection") | |
| concept_input = st.text_input("Enter a concept label:", "Cortical Column Theory") | |
| time_window = st.slider("Select attention window (seconds)", 1, 10, 5) | |
| memory_type = st.radio("Select memory type", ("Episodic (E)", "Semantic (K)")) | |
| neural_component = st.selectbox("Choose a neural component", ["Neuron", "Synapse", "Dendrite", "Axon"]) | |
| additional_components = st.multiselect("Select additional components", ["Free Energy", "Agency", "Mass", "Bond"]) | |
| st.subheader("Activation Controls") | |
| if st.checkbox("Activate Introspective Attention"): | |
| st.write("Introspective Attention Activated!") | |
| if st.button("Execute Self-Modification Cycle"): | |
| st.write("**Self-Modification Cycle Executed**") | |
| st.write(f"Memory Type Selected: {memory_type}") | |
| st.write(f"Attention Window: {time_window} seconds") | |
| st.write(f"Neural Component: {neural_component}") | |
| st.write(f"Additional Components: {additional_components}") | |
| st.subheader("Media Components") | |
| st.image("https://via.placeholder.com/150.png?text=Neural+Network", caption="Neural Network Representation") | |
| # Note: The video below is a placeholder. | |
| st.video("https://www.youtube.com/watch?v=dQw4w9WgXcQ") | |
| st.subheader("Data and JSON Display") | |
| df = pd.DataFrame({ | |
| "Component": ["Neuron", "Synapse", "Dendrite", "Axon"], | |
| "Status": ["Active", "Active", "Inactive", "Active"] | |
| }) | |
| st.dataframe(df) | |
| sample_json = { | |
| "Episodic": {"Duration": f"{time_window} sec", "Type": memory_type}, | |
| "Semantic": {"Label": concept_input} | |
| } | |
| st.json(sample_json) | |
| st.subheader("File Upload and Color Picker") | |
| uploaded_file = st.file_uploader("Upload a configuration file") | |
| color = st.color_picker("Pick a highlight color", "#00f900") | |
| st.write("Selected Color:", color) | |
| st.subheader("Date and Time Inputs") | |
| date_input = st.date_input("Select a date") | |
| time_input = st.time_input("Select a time") | |
| st.write("Date:", date_input, "Time:", time_input) | |
| st.subheader("Progress Bar Simulation") | |
| progress_bar = st.progress(0) | |
| for percent_complete in range(101): | |
| progress_bar.progress(percent_complete) | |
| time.sleep(0.01) | |
| st.subheader("Metrics and Download Button") | |
| st.metric(label="Introspective Score", value=time_window*10, delta="+5") | |
| st.download_button("Download Configuration", data="configuration data", file_name="config.txt") | |
| # ----------------------------------------------------------------------------- | |
| # Tab 5: Self Reward Learning NPS Score | |
| # ----------------------------------------------------------------------------- | |
| with tabs[4]: | |
| st.header("Self Reward Learning NPS Score") | |
| nps_score = st.slider("Rate Self Reward Learning (0-10):", 0, 10, 5) | |
| if nps_score <= 6: | |
| nps_comment = "Needs Improvement - Consider refining self-modification algorithms." | |
| elif nps_score <= 8: | |
| nps_comment = "Good, but can be better - Fine-tuning required." | |
| else: | |
| nps_comment = "Excellent! - The system demonstrates robust self-reward learning." | |
| st.write(f"**NPS Score:** {nps_score} - {nps_comment}") | |
| # ----------------------------------------------------------------------------- | |
| # Tab 6: Extra UI Components for Extended Demonstration | |
| # ----------------------------------------------------------------------------- | |
| with tabs[5]: | |
| st.header("Extra UI Components") | |
| with st.expander("More Details"): | |
| st.write("Additional explanations or interactive widgets can be added here.") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.write("**Column 1:** Additional metrics or charts.") | |
| st.line_chart(np.random.randn(20, 1)) | |
| with col2: | |
| st.write("**Column 2:** Other interactive elements.") | |
| st.bar_chart(np.random.randn(20, 1)) | |