Programmable electrical interconnection neural information detection and regulation microsystem and method

Abstract

The application discloses a programmable electrically interconnected neural information detection and regulation microsystem and method, and belongs to the technical field of neuroscience and biomedical engineering. The system comprises a partitioned in-vitro nerve chip, an electric stimulation module, a multi-channel neural signal acquisition module, an electrically interconnected control module and an upper computer module. The partitioned in-vitro nerve chip is manufactured based on a MEMS process and supports partitioned culture of in-vitro nerve cells; the electric stimulation module can generate multi-channel nanampere to milliamperes two-way constant current pulses, realizes precise nerve regulation; the multi-channel neural signal acquisition module is equipped with a low-noise amplifier array, realizes real-time acquisition and conversion of multi-channel neural signals; the electrically interconnected control module realizes dynamic signal routing between partitions through a switch array; and the upper computer module provides parameter configuration, signal processing and visualized interaction functions. The system has the advantages of high integration, high precision, programmability and real-time regulation, and is suitable for in-vitro brain-computer interface and neural network function research.

Description

Technical Field

[0001] This invention belongs to the field of neuroscience and biomedical engineering technology, specifically relating to a programmable electrically interconnected neural information detection and regulation microsystem and method. Background Technology

[0002] The brain, one of the most complex systems in nature, is composed of approximately 86 billion neurons interconnected through synaptic networks, enabling higher cognitive functions such as perception, learning, memory, and decision-making. Despite ongoing advancements in neuroscience research, the high complexity of neural networks means that a comprehensive understanding of their dynamic mechanisms and functional principles remains a significant challenge.

[0003] Against this backdrop, ex vivo brain-computer interface (BCI) technology has become an important tool for studying neural network functions. This technology cultivates neural networks on microelectrode arrays and connects them to external circuit systems, enabling real-time detection and feedback regulation of neural signals, providing an effective platform for simulating brain-computer interaction and exploring brain-like intelligence. Currently, ex vivo neural chip technology based on microelectrode arrays has made some progress in signal detection, neurotransmitter monitoring, and multifunctional integration.

[0004] However, existing technologies still have significant limitations: First, the system integration is low, making it difficult to simultaneously achieve high-precision signal acquisition, multi-channel electrical stimulation, and dynamic interconnection control on the same platform; second, existing devices lack flexibility and cannot reconstruct the neural network connections in real time according to experimental needs; in addition, the limited number of channels, signal resolution, and real-time processing capabilities of the system restrict the ability to perform precise analysis and intervention on complex neural activities.

[0005] To address the aforementioned issues, there is an urgent need to develop a highly integrated and flexibly programmable neural information detection and modulation system. This system should be able to integrate multi-channel signal acquisition, programmable electrical stimulation, and regional interconnection functions on a single platform. Unlike traditional systems that can only perform single-region stimulation or fixed connection modes, this system uses a programmable electrical interconnection module to achieve dynamic signal routing between different neuronal regions. It can flexibly construct various network topologies to support high-throughput and dynamic research on neural network activities, thereby promoting the development of brain-computer interfaces and neuroscience research to a deeper level. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a programmable electrically interconnected neural information detection and control microsystem and method. It integrates multi-channel neural signal real-time detection and regional electrical interconnection function dynamic control within a single platform. Through deep integration with a partitioned ex vivo neural chip, it achieves precise control of target neural regions, real-time acquisition of neural signals, flexible interconnection of neural partitions, and provides comprehensive data processing and user interaction functions.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A programmable electrically interconnected neural information detection and control microsystem, the system comprising a partitioned ex vivo neural chip, an electrical stimulation module, a multi-channel neural signal acquisition module, an electrical interconnection control module, and a host computer module, wherein...

[0009] The partitioned ex vivo neural chip is used to realize the partitioned culture and electrical signal transmission of ex vivo neural cells, providing an electrical interface basis for the detection and regulation of neural activity.

[0010] The electrical stimulation module is connected to the partitioned ex vivo neural chip and generates multi-channel biphasic constant current pulses to precisely control the target microelectrode array region according to the programmable parameters (including current amplitude, pulse width, frequency and channel selection) set by the host computer.

[0011] The multi-channel neural signal acquisition module is connected to the partitioned ex vivo neural chip and is used to acquire multi-channel neural signals from the partitioned ex vivo neural chip in real time.

[0012] The electrical interconnection control module employs an analog switch array to realize dynamic signal routing and electrical interconnection control between different partitions of the partitioned ex vivo neural chip.

[0013] The host computer module is communicatively connected to the electrical stimulation module, the multi-channel neural signal acquisition module, and the electrical interconnection control module, respectively, and is used to provide a user interface, process neural signals in real time, dynamically configure system parameters, and manage experimental data.

[0014] Furthermore, in the programmable electrically interconnected neural information detection and control microsystem, the partitioned ex vivo neural chip is manufactured based on microelectromechanical systems (MEMS) technology and integrates a microfluidic structure, which includes multiple independent cell culture chambers and microchannels connecting the chambers.

[0015] Furthermore, in the aforementioned programmable electrically interconnected neural information detection and control microsystem, the electrical stimulation module integrates 64 independent channels, capable of generating bidirectional constant current pulses in the range of 10nA to 2.55mA; the module communicates with the host computer module via an SPI digital interface to receive stimulation commands.

[0016] Furthermore, in the aforementioned programmable electrically interconnected neural information detection and control microsystem, the multi-channel neural signal acquisition module integrates 256 signal acquisition channels. Each channel includes a programmable bandwidth low-noise amplifier and a 16-bit analog-to-digital converter. The digital signal stream is transmitted to the host computer module through a high-speed SPI interface. The acquisition channels and filtering coefficients can be dynamically configured according to experimental requirements to achieve real-time acquisition, amplification, and feature extraction of multi-channel neural signals.

[0017] Furthermore, the aforementioned programmable electrically interconnected neural information detection and control microsystem integrates an interconnection control module with a crosspoint matrix composed of 64 analog switches. The system adopts a field-programmable gate array (FPGA) + analog switch array architecture, allowing users to configure the electrical connection relationships between different partitions in the microelectrode array in real time via a host computer. Compared to traditional fixed wiring or manual switching methods, it can achieve millisecond-level dynamic switching and automated signal routing, supporting reconfigurable interconnection experiments between neural blocks.

[0018] Furthermore, the software of the host computer module is developed based on the PyQt5 framework, providing a graphical user interface. This interface supports real-time adjustment of electrical stimulation parameters and interconnection configurations, and integrates signal processing algorithms. As the control center and visual interface of the entire data acquisition system, the host computer software is primarily responsible for receiving, parsing, displaying, and initially analyzing electrophysiological signal data from the slave computer, and sending programming instructions to the slave computer to realize regional interconnection, electrical stimulation, and signal acquisition functions.

[0019] Furthermore, the electrical stimulation module, the multi-channel neural signal acquisition module, and the electrical interconnection control module are all integrated on a multilayer printed circuit board, and are uniformly coordinated and controlled by a core field-programmable gate array (FPGA).

[0020] Furthermore, the programmability of the neural connection topology implemented through the electrical interconnect module allows for software-defined, real-time, and dynamic reconstruction of the system's topology, functional behavior, and information flow paths without altering any physical connections. For example, when an instruction requests "connecting the electrode of partition A to the electrode of partition B," the FPGA will find the specific switch controlling these two electrode paths and close it, thereby physically establishing a low-impedance signal path from A to B. The entire process is driven entirely by software instructions, realizing the "software definition" of neural network connections. Users can dynamically create, modify, or dismantle multiple such signal paths in a single experiment to simulate different neural network topologies (such as fully connected, loop, chain, etc.).

[0021] Furthermore, the programmable neural stimulation mode achieved through the electrical stimulation module allows for the independent and precise setting of a set of stimulation parameters for each stimulation channel (64 channels in total). Based on the feedback of real-time acquired neural signals, any parameter of any channel can be dynamically and timely modified during experimental operation, thereby achieving adaptive closed-loop neural modulation.

[0022] Furthermore, the signal acquisition and processing modes are programmable through the multi-channel neural signal acquisition module. The bandwidth and gain of the low-noise amplifier in each acquisition channel (256 in total) are programmable. The high-pass cutoff frequency and low-pass cutoff frequency of the amplifier filter can be dynamically set. For example, when observing local field potentials, it can be set to 0.1Hz-500Hz; when detecting neuronal action potentials, it can be set to 300Hz-3000Hz. This flexibility ensures the optimal signal-to-noise ratio for neural signals in different frequency bands. The host computer software integrates configurable signal processing algorithms. For example, for Spike detection, users can programmatically set the detection threshold and filter parameters (such as the upper and lower limits of a 4th-order Butterworth bandpass filter). The adjustability of these algorithm parameters allows the system to adapt to the characteristic variations of neural signals under different cell types and culture conditions.

[0023] Furthermore, the programmability of the overall experimental process through the host computer module enables the system to achieve closed-loop programmability of "detection-decision-control". The host computer analyzes the incoming neural signals in real time (such as calculating the firing rate and oscillation power) and, based on preset software logic rules (if-then-else), "intelligently" determines the next stimulation or interconnection strategy and executes it immediately. This transforms the system from a passive observation tool into an active experimental platform capable of dynamically interacting with neural networks.

[0024] On the other hand, the present invention also provides a method for detecting and regulating neural information in a programmable electrical interconnect, comprising:

[0025] Step 1: In vitro neural cells are cultured in a partitioned manner on a partitioned ex vivo neural chip fabricated using MEMS technology to form multiple independent neural functional regions.

[0026] Step 2: The host computer module sends configuration commands to the electrical interconnection control module to dynamically set the electrical interconnection relationship between each partition of the microelectrode array. Specifically, the host computer selects the blocks to be interconnected through the human-machine interface according to the experimental requirements, generates corresponding control commands, and sends them to the analog switch array in the electrical interconnection control module via the FPGA to realize the connection or disconnection of specific signal paths and realize dynamic routing between partitions.

[0027] Step 3: The host computer module sends stimulation parameters to the electrical stimulation module, including stimulation mode, amplitude, frequency and pulse width. The electrical stimulation module generates multi-channel bidirectional constant current or constant voltage pulse signals accordingly and performs precise electrical stimulation on the target nerve area.

[0028] Step four: The multi-channel neural signal acquisition module acquires neural electrical signals in real time after partition interconnection and electrical stimulation. The acquired neural signals are then amplified with low noise, filtered, and converted from analog to digital before being uploaded to the host computer module.

[0029] Step 5: The host computer module processes, visualizes, and stores the acquired neural signals in real time, and can perform closed-loop feedback control or offline analysis based on signal characteristics to achieve programmable electrical interconnection and dynamic control of the neural network.

[0030] Thirdly, the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned programmable electrically interconnected neural information detection and control method.

[0031] Fourthly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned programmable electrically interconnected neural information detection and control method.

[0032] The beneficial effects of this invention are as follows:

[0033] High integration and multi-functionality: The system integrates the partitioned ex vivo neural chip, electrical stimulation, multi-channel neural signal acquisition, interconnection control and host computer software into a unified platform, realizing closed-loop operation of neural signal detection-analysis-regulation, which significantly improves experimental efficiency and system reliability.

[0034] High precision and high flexibility control capabilities: The electrical stimulation module supports adjustable bidirectional constant current output from nanoamps to milliamps, and parameters can be configured in real time with FPGA; the electrical interconnection control module realizes dynamic routing between neural regions through an analog switch matrix, giving the system high reconfigurability and adaptability, and can flexibly cope with a variety of neural network experimental scenarios.

[0035] Multi-channel parallel processing and high signal-to-noise ratio acquisition: The system provides up to 256 channels of synchronous signal acquisition capability, equipped with a programmable bandwidth low-noise amplifier and a 16-bit high-precision ADC, which can effectively capture weak neural electrical activity and ensure data authenticity and integrity.

[0036] Hardware and software collaboration and user-friendly interaction: The host computer is developed based on PyQt5, which supports real-time parameter configuration, online signal processing and multi-dimensional offline analysis. There is no need to recompile hardware code, which greatly improves the convenience and repeatability of experiments.

[0037] Strong scalability and stable reliability: The six-layer PCB design and partitioned power supply strategy optimize signal integrity and electromagnetic compatibility. The modular architecture facilitates channel and function expansion, providing a solid foundation for long-term and complex neuroscience research. Attached Figure Description

[0038] Figure 1 This is a block diagram of a programmable electrically interconnected neural information detection and control microsystem according to the present invention.

[0039] Figure 2 This is a circuit connection diagram of a programmable electrically interconnected neural information detection and control microsystem according to the present invention;

[0040] Figure 3 This is a schematic diagram of the host computer interface control.

[0041] Figure 4 This is a schematic diagram of multi-channel neural signal acquisition on a host computer.

[0042] Figure 5 Extracting a schematic diagram for spike;

[0043] Figure 6 This is a schematic diagram of a neural information detection and control method for programmable electrical interconnects according to the present invention. Detailed Implementation

[0044] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0045] This invention provides a programmable electrically interconnected neural information detection and control microsystem, aiming to achieve precise control of target neural regions, real-time acquisition of neural signals, flexible interconnection of neural partitions, and comprehensive data processing and user interaction functions. Through innovative hardware and software integration, this system provides a highly efficient, accurate, and flexible tool platform for neuroscience research. The core innovation lies in the software definition of the physical connection topology of ex vivo neural networks through a collaborative architecture of "FPGA + analog switch array." Unlike traditional systems that can only achieve fixed connections or manual switching, the electrical interconnection control module of this system acts as a "programmable neural junction box," allowing users to graphically specify the functional connections between different functional partitions (such as area A and area B) on the chip in the host computer software. After receiving instructions, the FPGA drives the specific cross-point switches in the analog switch array to close or open within milliseconds, thereby physically establishing, modifying, or dismantling a low-resistance, high-quality electrical signal path in real time. This capability allows researchers to dynamically reconstruct fully connected, loop, chain, and even random network topologies on a well-trained neural network, and observe in real time how its electrical activity evolves with topological changes, providing an unprecedented controllable experimental platform for studying the structure-function relationship of neural networks.

[0046] like Figure 1 As shown, the core components of this system include a partitioned ex vivo neural chip, an electrical stimulation module, a multi-channel neural signal acquisition module, an electrical interconnection control module, and a host computer module. Among them,

[0047] The partitioned ex vivo neural chip is used to realize the partitioned culture and electrical signal conduction of ex vivo neural cells;

[0048] The electrical stimulation module is connected to the partitioned ex vivo neural chip and is used to generate multi-channel bidirectional constant current pulse signals according to preset parameters to precisely control the target microelectrode array region.

[0049] The multi-channel neural signal acquisition module is connected to the partitioned ex vivo neural chip, has low-power electrophysiological signal acquisition function, and is equipped with a programmable bandwidth low-noise amplifier array for real-time acquisition and amplification of multi-channel neural signals from the partitioned ex vivo neural chip.

[0050] The electrical interconnection control module employs an analog switch array and provides a highly flexible programming interface for realizing dynamic signal routing and electrical interconnection control between different partitions of the partitioned ex vivo neural chip.

[0051] The host computer module is communicatively connected to the electrical stimulation module, the nerve signal acquisition module, and the electrical interconnection control module, respectively. It is used to process nerve signals in real time, dynamically adjust stimulation parameters and interconnection channels, support data storage and analysis, and provide visual user interaction functions.

[0052] Furthermore, the partitioned ex vivo neural chip is manufactured using micro-electro-mechanical systems (MEMS) technology and integrates a microfluidic structure. This structure includes multiple independent cell culture chambers and microchannels connecting the chambers, providing ideal growth sites for in vitro neuronal cultures and enabling partitioned culture. The chip can be fixed to a PCB board using aluminum wire bonding technology and connected to a back-end electrical stimulation and acquisition module, enabling parallel recording of the firing activities of multiple neurons.

[0053] Furthermore, such as Figure 2As shown, the electrostimulation module integrates a multi-channel programmable amplitude constant current stimulation unit, capable of flexibly generating bidirectional constant current pulse signals ranging from nanoamperes to milliamperes to meet various bioelectric stimulation needs (such as 64 independent channels (numbered ELEC0 to ELEC63)). One end of the electrostimulation module is directly connected to the microelectrode array, and the other end is connected to the host computer via a digital bus for real-time and precise control of the stimulation signal. This not only ensures high-precision adjustment of the stimulation amplitude and frequency but also allows for personalized parameter configuration based on the characteristics of the neural network, thereby achieving precise intervention and control of neural network activity and meeting the needs of functional research and clinical applications of complex neural networks. The output current of each channel can be configured through a programmable logic unit to trigger registers, flexibly adjusting parameters such as the stimulator's step size, output current amplitude, and polarity. To improve the integrity of the power signal, the module adopts a low-voltage differential signaling (LVDS) conduction mode, effectively reducing electromagnetic interference and signal distortion. The module communicates via an SPI interface, including chip select signals (CS-, CS+), serial data clock (SCLK-, SCLK+), master output / slave input data lines (MOSI-, MOSI+), master input / slave output data lines (MISO-, MISO+), and is equipped with matching impedance terminating resistors to optimize signal transmission quality.

[0054] The multi-channel neural signal acquisition module employs a high-performance integrated chip (e.g., integrating 256 signal acquisition channels (IN0 to IN255)), featuring low-power electrophysiological signal acquisition capabilities and equipped with a programmable bandwidth low-noise amplifier array. This module can capture weak neural electrical activity signals from a microelectrode array and amplify them precisely and efficiently. The amplified signals are converted into high-precision digital data streams in real time, and the data is transmitted to a host computer module via a high-speed interface for further analysis and processing. The module's design optimizes the acquisition and transmission process of electrophysiological signals, achieving convenient and integrated acquisition equipment. It provides strong technical support for the detailed study, dynamic monitoring, and regulation of neural network activity, and can meet the needs of real-time acquisition and high-precision analysis of complex bioelectrical signals. The maximum supported channel sampling rate is 30 kS / s; its adjustable cutoff frequency range covers a high-frequency band with an upper limit of 100 Hz to 20 kHz and a low-frequency band with a lower limit of 0.1 Hz to 500 Hz; the analog signal of each channel undergoes 16-bit high-precision ADC processing to ensure high-precision signal acquisition and complete representation of minute fluctuations in neural signals. Similar to the electrical stimulation module, this module communicates via a differential signal transmission mode SPI interface, including chip select signals (CS-, CS+), serial data clock (SCLK-, SCLK+), master output / slave input data lines (MOSI-, MOSI+), master input / slave output data lines (MISO-, MISO+), and is equipped with matching impedance terminating resistors to optimize signal transmission quality.

[0055] The described electrical interconnection control module employs a high-performance analog switch array (e.g., integrating 64 high-performance, programmable analog switches (SW1 to SW64)). Utilizing a high-performance analog switch array chip, its core function is to provide a flexible crosspoint matrix, enabling arbitrary dynamic connections between inputs and outputs. This module uses an FPGA as its core controller, coordinating the operation of the switch array through precise configuration and operation commands. This module allows for precise control of signal transmission and regional interconnection between different sections of the microelectrode array, thereby supporting dynamic signal routing and regional modulation capabilities in complex neuromodulation tasks. This design provides the neuromodulation system with extremely high degrees of freedom and flexibility, ensuring that the range of electrical stimulation and signal recording areas can be dynamically adjusted according to experimental needs or clinical applications, effectively improving the system's adaptability and accuracy.

[0056] like Figure 3 As shown, the host computer module interacts with the hardware system in real time through a user interface developed using PyQt5. The host computer interface periodically reads serial port data to achieve real-time monitoring of the device's operating status, allowing users to intuitively view the device status, on / off status, and operation feedback. Users can directly adjust hardware parameters on the host computer interface; the adjusted parameters take effect immediately without the need for repeated compilation or flashing of hardware code. Using computational software, the host computer can perform offline analysis of the acquired neural signals, supporting multiple analysis functions such as discharge rate, power spectral density, ISI (interval between action potentials), and principal component analysis for various neural signals including Spike (action potential), Burst (burst discharge), and LFP (local field potential).

[0057] like Figures 4-5 As shown, the host computer software serves as the control center and visualization interface of the entire data acquisition system. It is mainly responsible for receiving, parsing, displaying, and performing preliminary analysis of electrophysiological signal data from the slave computer, and sending instructions to the electrostimulation module and the electro-interconnection control module.

[0058] After the software starts, it automatically detects the lower-level serial port, configures the user's parameters, establishes a connection, and then receives data temporarily stored in the FIFO via the FT232H chip's USB 2.0. Once the connection is successful, the lower-level device begins sending data, and the upper-level device enters the main loop: 1) Reading the raw data packet; 2) Verifying data integrity; 3) Parsing the data according to the protocol; 4) Updating the interface display; 5) Executing the user-triggered analysis task. This loop continues until the user disconnects.

[0059] If Spike detection is required, first perform a 4th-order Butterworth bandpass filter on the original signal to obtain a signal in the 30-3000Hz frequency band. Then detect whether there is a jump value between adjacent points (the threshold is manually set according to the actual situation). If there is a jump value, it is considered that a Spike signal has been detected. Take 48 adjacent points (1.6ms) and plot them for display.

[0060] The entire system is implemented using printed circuit board (PCB) technology, employing a six-layer PCB design. Through rational layered routing and power layer partitioning, the system's electromagnetic compatibility (EMC) and power integrity are improved, ensuring stable operation of each module under various operating environments. In particular, the sophisticated power layer design ensures a stable and uniform power supply to each module, effectively reducing the impact of voltage fluctuations on circuit performance, thereby enhancing system reliability and long-term stability.

[0061] like Figure 6 As shown, the present invention also provides a micro-method for detecting and controlling neural information via programmable electrical interconnects, applied to the above-mentioned system, comprising the following steps:

[0062] Step 1: In vitro neural cells are cultured in a partitioned manner on a partitioned ex vivo neural chip fabricated using MEMS technology to form multiple independent neural functional regions.

[0063] Step 2: The host computer module sends configuration commands to the electrical interconnection control module to dynamically set the electrical interconnection relationship between each partition of the microelectrode array. Specifically, the host computer selects the blocks to be interconnected through the human-machine interface according to the experimental requirements, generates corresponding control commands, and sends them to the analog switch array in the electrical interconnection control module via the FPGA to realize the connection or disconnection of specific signal paths and realize dynamic routing between partitions.

[0064] Step 3: The host computer module sends stimulation parameters to the electrical stimulation module, including stimulation mode, amplitude, frequency and pulse width. The electrical stimulation module generates multi-channel bidirectional constant current or constant voltage pulse signals accordingly and performs precise electrical stimulation on the target nerve area.

[0065] Step four: The multi-channel neural signal acquisition module acquires neural electrical signals in real time after partition interconnection and electrical stimulation. The acquired neural signals are then amplified with low noise, filtered, and converted from analog to digital before being uploaded to the host computer module.

[0066] Step 5: The host computer module processes, visualizes, and stores the acquired neural signals in real time, and can perform closed-loop feedback control or offline analysis based on signal characteristics to achieve programmable electrical interconnection and dynamic control of the neural network.

[0067] Thirdly, the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned programmable electrically interconnected neural information detection and control method.

[0068] Fourthly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned programmable electrically interconnected neural information detection and control method.

[0069] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A programmable electrically interconnected neural information detection and control microsystem, characterized in that, The system includes a partitioned ex vivo neural chip, an electrical stimulation module, a multi-channel neural signal acquisition module, an electrical interconnection control module, and a host computer module. The partitioned ex vivo neural chip is manufactured using MEMS technology and integrates microfluidic structures to realize the partitioned culture of ex vivo neural cells and the conduction of electrical signals. The electrical stimulation module is connected to the partitioned ex vivo neural chip and is used to generate multi-channel biphasic constant current pulses according to programmable parameters set by the host computer to precisely regulate the target neural region. The parameters include current amplitude, pulse width, frequency and channel selection. The multi-channel neural signal acquisition module is connected to the partitioned ex vivo neural chip and is used to acquire multi-channel neural signals from the partitioned ex vivo neural chip in real time. The electrical interconnection control module employs an analog switch array to realize dynamic signal routing and electrical interconnection control between different partitions of the partitioned ex vivo neural chip. The host computer module is communicatively connected to the electrical stimulation module, the multi-channel neural signal acquisition module, and the electrical interconnection control module. Through a graphical user interface, it performs unified parameter configuration and real-time command issuance to these modules. It also processes, displays, and stores the acquired neural signals in real time. Based on the real-time processing results and a preset programmable closed-loop control logic, it automatically generates and sends configuration commands to the electrical interconnection control module to dynamically set the electrical interconnection relationships between different regions. Furthermore, it automatically generates and sends stimulation parameters to the electrical stimulation module to dynamically adjust the stimulation of the target neural region, thereby achieving closed-loop feedback control of the neural network.

2. The programmable electrically interconnected neural information detection and control microsystem according to claim 1, characterized in that, The microfluidic structure includes multiple independent cell culture chambers and microchannels connecting the chambers.

3. The programmable electrically interconnected neural information detection and control microsystem according to claim 1, characterized in that, The electrical stimulation module integrates 64 independent channels, capable of generating bidirectional constant current pulses in the range of 10nA to 2.55mA, and communicates with the host computer module via an SPI digital interface.

4. The programmable electrically interconnected neural information detection and control microsystem according to claim 1, characterized in that, The multi-channel neural signal acquisition module integrates 256 signal acquisition channels. Each channel includes a programmable bandwidth low-noise amplifier and a 16-bit analog-to-digital converter. It transmits digital signal streams to the host computer module via a high-speed SPI interface and receives control commands from the host computer.

5. A programmable electrically interconnected neural information detection and control microsystem according to claim 1, characterized in that, The electrical interconnection control module integrates a crosspoint matrix consisting of 64 analog switches; it adopts a collaborative architecture of FPGA and analog switch array, which allows users to configure the electrical connection relationship between different partitions in the microelectrode array in real time through a host computer.

6. The programmable electrically interconnected neural information detection and control microsystem according to claim 1, characterized in that, The software of the host computer module is developed based on the PyQt5 framework and provides a graphical user interface. The interface supports users to adjust electrical stimulation parameters, acquisition parameters and interconnection configurations in real time, and integrates configurable signal processing algorithms.

7. A programmable electrically interconnected neural information detection and control microsystem according to claim 1, characterized in that, The electrical stimulation module, multi-channel neural signal acquisition module, and electrical interconnection control module are integrated on a six-layer printed circuit board, and are uniformly coordinated and controlled by a core field-programmable gate array (FPGA).

8. A method for detecting and controlling neural information through programmable electrical interconnects, applied to the method described in any one of claims 1-7, characterized in that, include: Step 1: Perform isolated neural cell culture on a partitioned isolated neural chip; Step 2: Send configuration instructions to the electrical interconnection control module through the host computer module to dynamically set the electrical interconnection relationship between each partition of the isolated neural chip; including: according to the experimental requirements, select the block to be interconnected through the host computer interface, generate control instructions and send them to the analog switch array in the electrical interconnection control module through the FPGA to connect or disconnect specific signal paths and realize dynamic routing between partitions. Step 3: The host computer module sends stimulation parameters to the electrical stimulation module to generate multi-channel constant current pulses and apply electrical stimulation to the target nerve region. Step 4: The multi-channel neural signal acquisition module is used to acquire the neural electrical signals after the interconnection in Step 2 and the stimulation in Step 3 in real time, and the data is uploaded to the host computer module. Step 5: The acquired neural signals are processed, displayed, stored, or analyzed offline in the host computer module.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When one or more programs are executed by the one or more processors, the one or more processors implement the neural information detection and control method of claim 8.

10. A computer-readable storage medium, characterized in that, It stores executable instructions that, when executed by a processor, enable the processor to implement the neural information detection and control method of claim 8.

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