Unverified50% confidenceFactExact time
Neurons are not a direct token count or number of calls, but an abstract unit converted based on model computational complexity, with input and output portions often billed at different rates.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
Unverified神经元激活的度量方式包括原始激活值、激活频率和相对激活强度等多种,反映模型内部不同的信息组织逻辑71% similarUnverifiedActivation-swapping on the selected neurons flips model predictions at a rate an order of magnitude higher than an equally sized set of random neurons, and this holds in both directions.67% similarUnverifiedLarge models are billed by the number of input plus output Tokens for each inference.66% similarUnverified神经网络输出连续概率分布而符号推理引擎需要离散结构化输入,两者接口设计(神经-符号接口问题)至今没有公认最优解64% similarUnverifiedThe output of each layer in a neural network can be expressed as f(Wx + b), where f is the activation function, W is the weight matrix, b is the bias vector, and x is the input vector.64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1010350API
curl https://kongchang.com/api/v1/knowledge/claims/1010350MCP
get_claim(id=1010350)