diff --git a/.ipynb_checkpoints/convert_nwb_to_npy_and_df-checkpoint.ipynb b/.ipynb_checkpoints/convert_nwb_to_npy_and_df-checkpoint.ipynb new file mode 100644 index 00000000..df9563ef --- /dev/null +++ b/.ipynb_checkpoints/convert_nwb_to_npy_and_df-checkpoint.ipynb @@ -0,0 +1,179 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "55e69c20-7b7e-449d-8740-07ea2079949c", + "metadata": {}, + "source": [ + "# Read contents of .nwb file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fbe7cf35-bc99-421d-bb1e-bc0938310a9a", + "metadata": {}, + "outputs": [], + "source": [ + "from pynwb import NWBHDF5IO\n", + "\n", + "# Replace with the path to your .nwb file\n", + "nwb_file_path = 'data1.nwb'\n", + "\n", + "\n", + "# Open the .nwb file using PyNWB\n", + "with NWBHDF5IO(nwb_file_path, 'r') as io:\n", + " nwbfile = io.read()\n", + "\n", + " # Access the acquisition group\n", + " acquisition = nwbfile.acquisition\n", + "\n", + " # Print the names of all groups within the acquisition group\n", + " print(\"Acquisition Groups:\")\n", + " for name, timeseries in acquisition.items():\n", + " print(f\" - {name}: {timeseries}\")\n", + " \n", + " # Print all groups in stimulus (if any)\n", + " if hasattr(nwbfile, 'stimulus'):\n", + " print(\"\\nStimulus Groups:\")\n", + " for name, sgroup in nwbfile.stimulus.items():\n", + " print(f\" - {name}: {sgroup}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ded5da77-78b1-423a-b9c0-b9fa3f1ea4de", + "metadata": {}, + "source": [ + "# Extract nwb file contents" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a92f750-5b15-4deb-a902-583900418978", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from pynwb import NWBHDF5IO\n", + "\n", + "# Function to extract data from a TimeSeries object\n", + "def extract_data(time_series):\n", + " return {\n", + " 'data': np.array(time_series.data[:]),\n", + " 'timestamps': np.array(time_series.timestamps[:]) if time_series.timestamps else None,\n", + " 'unit': time_series.unit,\n", + " 'comments': time_series.comments\n", + " }\n", + "\n", + "# Path to your NWB file\n", + "nwb_file_path = 'data1.nwb'\n", + "\n", + "# Open the .nwb file using PyNWB\n", + "with NWBHDF5IO(nwb_file_path, 'r') as io:\n", + " nwbfile = io.read()\n", + "\n", + " # Extract acquisition groups\n", + " acquisition_data = {}\n", + " for name, timeseries in nwbfile.acquisition.items():\n", + " acquisition_data[name] = extract_data(timeseries)\n", + "\n", + " # Extract stimulus groups if present\n", + " stimulus_data = {}\n", + " if hasattr(nwbfile, 'stimulus'):\n", + " for name, timeseries in nwbfile.stimulus.items():\n", + " stimulus_data[name] = extract_data(timeseries)" + ] + }, + { + "cell_type": "markdown", + "id": "d5b99136-99ed-47a4-8097-c7d6c9ac6855", + "metadata": {}, + "source": [ + "# Save extracted contents of nwb file as numpy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8084e8ff-c96c-4981-adc3-05a7abcbfbf1", + "metadata": {}, + "outputs": [], + "source": [ + "for name, data in acquisition_data.items():\n", + " np.save(f'{name}_acquisition.npy', data['data'])\n", + "\n", + "for name, data in stimulus_data.items():\n", + " np.save(f'{name}_stimulus.npy', data['data'])" + ] + }, + { + "cell_type": "markdown", + "id": "114a8f14-bfe7-49fa-a8f5-2b3f011e6d71", + "metadata": {}, + "source": [ + "# Save extracted contents of nwb file as dataframe csv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6d160ab-6151-4e08-9fc5-60479f167514", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "for name, data in acquisition_data.items():\n", + " # Check if data is two-dimensional\n", + " if data['data'].ndim == 2:\n", + " # Create column names for each dimension\n", + " column_names = [f'{name}_dim_{i}' for i in range(data['data'].shape[1])]\n", + " else:\n", + " # For one-dimensional data, use a single column\n", + " column_names = [f'{name}_value']\n", + "\n", + " df = pd.DataFrame(data['data'], columns=column_names)\n", + " if data['timestamps'] is not None:\n", + " df['timestamps'] = data['timestamps']\n", + " df.to_csv(f'{name}_acquisition.csv', index=False)\n", + "\n", + "for name, data in stimulus_data.items():\n", + " # Check if data is two-dimensional\n", + " if data['data'].ndim == 2:\n", + " # Create column names for each dimension\n", + " column_names = [f'{name}_dim_{i}' for i in range(data['data'].shape[1])]\n", + " else:\n", + " # For one-dimensional data, use a single column\n", + " column_names = [f'{name}_value']\n", + "\n", + " df = pd.DataFrame(data['data'], columns=column_names)\n", + " if data['timestamps'] is not None:\n", + " df['timestamps'] = data['timestamps']\n", + " df.to_csv(f'{name}_stimulus.csv', index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Bio_silicon_hybrid_computer b/Bio_silicon_hybrid_computer new file mode 100644 index 00000000..91c050e0 --- /dev/null +++ b/Bio_silicon_hybrid_computer @@ -0,0 +1,209 @@ +Synergetic Bidirectional BCI System Software Ideas: + + +Reasoning for using Symbolic Dynamics: + +1. Data Simplification and Efficiency: + + Reduced Complexity: Neural data is inherently complex and high-dimensional. Symbolic dynamics simplifies this data into a manageable form, making it easier to process and interpret. + Efficient Processing: Simplified data requires less computational power, allowing for more efficient processing, especially important for real-time applications in your FPGA systems. + +2. Enhanced Pattern Recognition and Learning: + + Pattern Detection: Symbolic representation makes it easier to identify patterns and correlations in neural activity, which can be pivotal for understanding brain-computer interactions. + Facilitated Learning: In a synergetic learning system, these patterns can be used to adapt and optimize the interaction between the rat brain and the computer, leading to more effective learning and data processing. + +3. Improved Communication Between Biological and Silicon Components: + + Standardized Interface: Symbolic dynamics provides a common language for communication between the biological brain and silicon-based computational systems. + Customizable Stimuli: Symbols can be translated back into electrical signals or other stimuli forms, tailored to elicit specific responses or actions from the brain. + +4. Scalability and Adaptability: + + Scalable System Design: Symbolic dynamics allows for a scalable approach to data processing, which can be adjusted based on the complexity of the task or the capabilities of the hardware. + Adaptable Algorithms: Symbolic data can be used to train machine learning algorithms, making the system adaptable to different scenarios or objectives. + +5. Potential for Advanced Neural Decoding and Encoding: + + Decoding Neural Signals: Symbolic representations can help in decoding the neural signals into meaningful information, enhancing our understanding of brain functions. + Encoding Information for the Brain: Similarly, encoding information into symbols that the brain can interpret opens up possibilities for advanced brain-computer interfaces. + +6. Research Insights and Applications: + + Neuroscientific Insights: The symbolic approach can reveal new insights into neural dynamics and brain function. + Medical and AI Applications: These insights can have applications in medical fields (like neuroprosthetics or neurological disorder treatments) and in developing sophisticated AI models.\ + + + + + + +Symbolic Dynamics Generation: + + Convert complex neural data into a simplified, symbolic representation. This involves mapping continuous or discrete neural signals into a finite set of symbols. + For historic data, apply in-depth analyses (like multifractal or recurrence quantification) to extract meaningful patterns and convert them into symbols. + For live data, use faster analysis methods that can provide real-time symbolic representations. + +Step 1: Symbol Definition and Mapping + + Define Symbol Set: + Create a finite set of symbols, each representing a specific type of neural stimulus. For instance, different symbols could represent varying frequencies, pulse widths, or amplitudes of electrical stimulation. + Each symbol should correspond to a distinct, recognizable pattern of neural activity. + + Data to Symbol Mapping: + Develop an algorithm to map computational data (from sensors, models, etc.) to this symbolic set. For example, a high value in computational data might map to a symbol representing a high-frequency electrical pulse. + +Step 2: Signal Transformation + + Electrical Signal Parameters: + Determine the parameters that effectively stimulate neural tissue. These include: + Amplitude: The strength of the electrical signal. + Frequency: How often the electrical pulses are delivered. + Pulse Width: Duration of each electrical pulse. + Waveform Shape: The shape of the signal, such as square, sinusoidal, or sawtooth. + + Symbol-to-Signal Conversion: + Assign each symbol a specific combination of these parameters. For example, Symbol A could correspond to a low-frequency, high-amplitude pulse, while Symbol B could represent a high-frequency, low-amplitude pulse. + Use your understanding of neural physiology to ensure these parameters are within a biologically relevant and safe range. + +Step 3: Delivery through the BCI + + FPGA Programming: + Program the FPGA to interpret each symbol and generate the corresponding electrical signal. This involves setting the FPGA's output to match the defined parameters for each symbol. + Ensure real-time processing capabilities to handle dynamic changes in the symbols. + + MEA Interface: + Use the microelectrode array (MEA) coated with carbon nanotubes to deliver these signals. The carbon nanotubes' properties can enhance signal clarity and biocompatibility. + Ensure precise control over which electrodes are activated and the timing of their activation. + + + +Step 4: Feedback and Adaptation + + Monitoring Neural Response: + Continuously monitor the brain's response to the stimulation using the same or separate electrodes on the MEA. + Use neural recording techniques to capture the resultant neural activity. + + Adaptive Stimulation: + Implement feedback mechanisms where the observed neural response influences future symbol selection and signal parameters. + This could involve machine learning algorithms that adjust the stimulation strategy based on real-time data. + + +Symbolic specifics: +1. Oscillators for Rhythmic Patterns: + + Use Oscillatory Signals: Model your input signals on neural oscillations, which are fundamental to brain activity. These can be designed to match typical brain wave frequencies (delta, theta, alpha, beta, gamma). + Phase Synchronization: Implement phase synchronization techniques to align your artificial oscillatory signals with the natural rhythms of the brain. + +2. Fractals for Complex Patterns: + + Fractal Geometry: Utilize fractal geometry in signal design, as neural activity often exhibits fractal-like patterns. Fractals can provide a way to create complex, self-similar patterns that may be more recognizable to the brain. + Fractal Dimension Analysis: Use fractal dimension as a parameter in your symbolic system, with different symbols representing different fractal complexities. + +3. Entropy and Transfer Entropy: + + Entropy Measures: Incorporate entropy measures to create signals that reflect the level of disorder or predictability found in natural neural signals. + Transfer Entropy: Use transfer entropy to model the information transfer in your signals, akin to the information flow in neural networks. + +4. Spectral Analysis: + + FFT and STFT: Employ Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT) to analyze and create signals that have spectral characteristics similar to those observed in brain activity. + Frequency Band Power: Adjust the power in different frequency bands (delta, theta, alpha, etc.) based on the desired neural response. + +5. Symbolic Dynamics Integration: + + Mapping Complex Dynamics: Map the outcomes of these analyses (oscillatory patterns, fractal dimensions, entropy levels, spectral characteristics) to your set of symbols. + Dynamic Signal Generation: Use the FPGA to convert these symbols into electrical signals that reflect the mapped characteristics. + + + + + + + +Encoding: +1. From Computer Data to Brain Data Input: + +a. Data Conversion to Symbolic Dynamics: + + Convert computer-generated data (which could be derived from various sensors, simulations, or computational models) into a symbolic format. This involves mapping complex data patterns into a simplified, discrete set of symbols. + +b. Encoding for Neural Compatibility: + + Transform these symbols into a format that can be understood or processed by the brain. This will involve electrical signals by the implanted MEA that neural tissue can respond to. + Use knowledge of neural coding and brain physiology to determine how different symbols can be represented as neural stimuli. + +c. Delivery through Brain-Computer Interface (BCI): + + Employ the BCI to deliver these encoded stimuli to the brain. This could involve electrical stimulation via microelectrode arrays or other methods compatible with your system. + + Data Conversion to Symbolic Dynamics: + Advanced Data Mapping: Utilize algorithms to map complex computational outputs into a set of symbols. These symbols should be designed to represent different patterns, intensities, or types of neural stimulation. + Contextual Adaptation: Adapt the symbolic representation based on the context of the experiment or the intended brain response. This may involve learning from previous interactions to optimize symbol mapping. + + Encoding for Neural Compatibility: + Electrical Signal Transformation: Convert symbols into electrical signals that mimic natural neural communication. This could involve varying parameters like amplitude, frequency, or pulse width. + Neural Coding Research: Integrate insights from neural coding research to tailor the electrical signals. Understanding how neurons encode information can guide the design of stimulation patterns. + Cognitive and Physiological Considerations: Consider the cognitive and physiological effects of stimulation, ensuring that the encoded signals align with the natural processing capabilities of the rat brain. + + Delivery through Brain-Computer Interface (BCI): + Precision Stimulation: Utilize the high precision of your carbon nanotube-coated MEA for targeted stimulation. The self-organizing biochemical-physical properties of the carbon nanotubes can enhance the interface with neural tissue. + Adaptive Stimulation Protocols: Develop stimulation protocols that can adapt in real-time based on feedback from the brain. This could involve adjusting the intensity or pattern of stimulation in response to observed neural activity. + +Additional Considerations: + + Integration with Computational Models: + Leverage computational models to predict and optimize how the brain might respond to different stimulation patterns. This can enhance the effectiveness of the BCI in inducing the desired neural responses. + + Machine Learning Optimization: + Implement machine learning algorithms to refine the encoding process continually. These algorithms can learn from the outcomes of previous stimulations to improve the accuracy and efficiency of symbol-to-neural signal translation. + + +Decoding: +2. From Brain Data Output to Computer Data: + +a. Capturing Neural Responses: + + Use the BCI to capture neural responses. This might involve recording electrical activity, neuronal firing patterns, or other relevant neural data. + +b. Decoding Neural Signals: + + Decode this data to translate it back into a symbolic or computational format. This process requires understanding the brain's response patterns to the input stimuli. + Apply algorithms (potentially AI-driven) to interpret these neural signals and convert them back into a symbolic or digital format. + +c. Data Analysis and Utilization: + + Analyze the decoded data for insights into brain responses, learning patterns, or other desired outcomes. + Use this data to inform the computer's subsequent outputs, closing the feedback loop and enabling adaptive learning. + + + + +Enhancing Synergetic Learning: + + Feedback Loops: + Establish a dynamic feedback loop where the output from the brain influences the subsequent input from the computer, facilitating a continuous learning and adaptation process. + + Machine Learning Integration: + Utilize machine learning algorithms to adapt the encoding and decoding processes based on ongoing interactions, enhancing the system's effectiveness over time. + + + +Dual Application for Input and Output: + + Input to Brain: Use symbolic dynamics to generate input patterns or stimuli for the rat brain. These symbols can be translated into electrical signals by the FPGA and then delivered to the brain via the interface. + Output from Brain: Capture neural responses or ongoing neural activity as output data. Analyze this data in real-time or retrospectively to convert it into symbolic form for further processing or interpretation. + + + Real-Time Processing: + Design FPGA algorithms to perform real-time conversion of neural data into symbolic dynamics. + Ensure the FPGA can handle the computational load, especially for real-time analysis. + + Feedback Loop: + Develop a feedback system where the output symbols from the brain's activity can influence the input stimuli. This can create a dynamic learning environment where the brain and computer system adapt to each other. + + Data Storage and Retrieval: + For historic data, store the symbolic representations in a database. Use them for in-depth analysis, system training, or pattern recognition tasks. + For live data, establish a pipeline for immediate processing and response generation. + + diff --git a/Code_FPGA_DAC_ADC/brain_to_computer/PCM1802/Ramp generator from data 16 bits by FPGA (Cyclone IV) and DAC (PCM5102).zip b/Code_FPGA_DAC_ADC/brain_to_computer/PCM1802/Ramp generator from data 16 bits by FPGA (Cyclone IV) and DAC (PCM5102).zip new file mode 100644 index 00000000..9cc16f4b Binary files /dev/null and b/Code_FPGA_DAC_ADC/brain_to_computer/PCM1802/Ramp generator from data 16 bits by FPGA (Cyclone IV) and DAC (PCM5102).zip differ diff --git a/convert_nwb_to_npy_and_df.ipynb b/convert_nwb_to_npy_and_df.ipynb new file mode 100644 index 00000000..df9563ef --- /dev/null +++ b/convert_nwb_to_npy_and_df.ipynb @@ -0,0 +1,179 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "55e69c20-7b7e-449d-8740-07ea2079949c", + "metadata": {}, + "source": [ + "# Read contents of .nwb file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fbe7cf35-bc99-421d-bb1e-bc0938310a9a", + "metadata": {}, + "outputs": [], + "source": [ + "from pynwb import NWBHDF5IO\n", + "\n", + "# Replace with the path to your .nwb file\n", + "nwb_file_path = 'data1.nwb'\n", + "\n", + "\n", + "# Open the .nwb file using PyNWB\n", + "with NWBHDF5IO(nwb_file_path, 'r') as io:\n", + " nwbfile = io.read()\n", + "\n", + " # Access the acquisition group\n", + " acquisition = nwbfile.acquisition\n", + "\n", + " # Print the names of all groups within the acquisition group\n", + " print(\"Acquisition Groups:\")\n", + " for name, timeseries in acquisition.items():\n", + " print(f\" - {name}: {timeseries}\")\n", + " \n", + " # Print all groups in stimulus (if any)\n", + " if hasattr(nwbfile, 'stimulus'):\n", + " print(\"\\nStimulus Groups:\")\n", + " for name, sgroup in nwbfile.stimulus.items():\n", + " print(f\" - {name}: {sgroup}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ded5da77-78b1-423a-b9c0-b9fa3f1ea4de", + "metadata": {}, + "source": [ + "# Extract nwb file contents" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a92f750-5b15-4deb-a902-583900418978", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from pynwb import NWBHDF5IO\n", + "\n", + "# Function to extract data from a TimeSeries object\n", + "def extract_data(time_series):\n", + " return {\n", + " 'data': np.array(time_series.data[:]),\n", + " 'timestamps': np.array(time_series.timestamps[:]) if time_series.timestamps else None,\n", + " 'unit': time_series.unit,\n", + " 'comments': time_series.comments\n", + " }\n", + "\n", + "# Path to your NWB file\n", + "nwb_file_path = 'data1.nwb'\n", + "\n", + "# Open the .nwb file using PyNWB\n", + "with NWBHDF5IO(nwb_file_path, 'r') as io:\n", + " nwbfile = io.read()\n", + "\n", + " # Extract acquisition groups\n", + " acquisition_data = {}\n", + " for name, timeseries in nwbfile.acquisition.items():\n", + " acquisition_data[name] = extract_data(timeseries)\n", + "\n", + " # Extract stimulus groups if present\n", + " stimulus_data = {}\n", + " if hasattr(nwbfile, 'stimulus'):\n", + " for name, timeseries in nwbfile.stimulus.items():\n", + " stimulus_data[name] = extract_data(timeseries)" + ] + }, + { + "cell_type": "markdown", + "id": "d5b99136-99ed-47a4-8097-c7d6c9ac6855", + "metadata": {}, + "source": [ + "# Save extracted contents of nwb file as numpy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8084e8ff-c96c-4981-adc3-05a7abcbfbf1", + "metadata": {}, + "outputs": [], + "source": [ + "for name, data in acquisition_data.items():\n", + " np.save(f'{name}_acquisition.npy', data['data'])\n", + "\n", + "for name, data in stimulus_data.items():\n", + " np.save(f'{name}_stimulus.npy', data['data'])" + ] + }, + { + "cell_type": "markdown", + "id": "114a8f14-bfe7-49fa-a8f5-2b3f011e6d71", + "metadata": {}, + "source": [ + "# Save extracted contents of nwb file as dataframe csv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6d160ab-6151-4e08-9fc5-60479f167514", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "for name, data in acquisition_data.items():\n", + " # Check if data is two-dimensional\n", + " if data['data'].ndim == 2:\n", + " # Create column names for each dimension\n", + " column_names = [f'{name}_dim_{i}' for i in range(data['data'].shape[1])]\n", + " else:\n", + " # For one-dimensional data, use a single column\n", + " column_names = [f'{name}_value']\n", + "\n", + " df = pd.DataFrame(data['data'], columns=column_names)\n", + " if data['timestamps'] is not None:\n", + " df['timestamps'] = data['timestamps']\n", + " df.to_csv(f'{name}_acquisition.csv', index=False)\n", + "\n", + "for name, data in stimulus_data.items():\n", + " # Check if data is two-dimensional\n", + " if data['data'].ndim == 2:\n", + " # Create column names for each dimension\n", + " column_names = [f'{name}_dim_{i}' for i in range(data['data'].shape[1])]\n", + " else:\n", + " # For one-dimensional data, use a single column\n", + " column_names = [f'{name}_value']\n", + "\n", + " df = pd.DataFrame(data['data'], columns=column_names)\n", + " if data['timestamps'] is not None:\n", + " df['timestamps'] = data['timestamps']\n", + " df.to_csv(f'{name}_stimulus.csv', index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}