{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"7f418071-0fdc-4a5a-a4c5-82dd1979d9c2","title":"人工智能哲学","author":null,"edition":null,"publisher":null,"year":null,"isbn":"7532740544","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"7f418071-0fdc-4a5a-a4c5-82dd1979d9c2","id":"7f418071-0fdc-4a5a-a4c5-82dd1979d9c2","uuid":"7f418071-0fdc-4a5a-a4c5-82dd1979d9c2","title":"人工智能哲学","normalized_title":"人工智能哲学","author":null,"edition":null,"publisher":null,"year":null,"isbn":"7532740544","isbns":["7532740544"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":501,"navigation_url":"https://workshop.closeclaw.top/ai/book/7f418071-0fdc-4a5a-a4c5-82dd1979d9c2","title_url":"https://workshop.closeclaw.top/ai/title/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E5%93%B2%E5%AD%A6","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/7f418071-0fdc-4a5a-a4c5-82dd1979d9c2/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/7f418071-0fdc-4a5a-a4c5-82dd1979d9c2/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/7f418071-0fdc-4a5a-a4c5-82dd1979d9c2","page_url":"https://workshop.closeclaw.top/api/ai/books/7f418071-0fdc-4a5a-a4c5-82dd1979d9c2/page","search_url":"https://workshop.closeclaw.top/api/ai/books/7f418071-0fdc-4a5a-a4c5-82dd1979d9c2/search"},"page":{"printed_page":382,"pdf_page":390,"physical_index":390,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"83b1abac5da3c9ef0843fbd0e0ad0ff25c53c8453abd66278c82abe1edca901f"},"chapter":"人工智能哲学","content":"对图14-9中神经矩阵的最后三点总结。第一，它不必局限于线性变换的计算。单个的突触联系能表示广泛范围内的任何功能特性。它们无需只是简单的乘法器。所以它们能以协同方式计算多种多样的非线性变换。第二，神经矩阵具有与状态空间分层结构同样的非常之快的速度。第三，如果给出的是大型矩阵和(或)细胞的庞大组织，即使它们分散地损失一些细胞成分，这样的结构也会表现出对功能的保持。\n\n这些简要的评述当然不是对派利欧尼斯和利纳斯极其丰富的研究成果的全面评价，同时我也没有提出任何批评意见。（后者见Arbib andAmari 1985。对他们的答复见 Pellionisz and Llinas 1985。)至于更深入的讲解，读者必须查阅文献。这一节的主要内容是说明，本文前面提到的一般性功能的图式，即用状态空间位置表述和由坐标变换计算的图式，即使在表述作业和计算作业超出二维情况时，也不会遇到实施的困难。相反，脑包容的神经机构可以完美地适合于维度很高的情况。这样，我们至少具有两种已知的执行坐标变换的脑机制：专门用于二维情况的状态空间分层结构，以及不论对何种维度都适合的神经矩阵。\n\n人工智能哲学\n\n382","previous_page":381,"next_page":383}