TL;DR: It is proposed that any type of information can be stored in the form of 'neuronal activity-associated magnetic fields' that would record information in much the same way as the magnetic tape of a tape recorder.
TL;DR: In this article, a system and method to optimize human learning process to achieve superior long-term memory results and to exercise the brain to enhance human memory is presented, which can be implemented as web application, a program in a PC, and through a wireless device or handheld devices such as cell phone, PDA, watch or toy among others.
Abstract: A system and method to optimize human learning process to achieve superior long-term memory results and to exercise the brain to enhance human memory. The system (100) comprises a display device (104) to present stimuli in visual/acoustical form and display progress information, an input device to receive a user's responses, and a database (108) containing the contents for learning or the stimuli for memory exercising. In addition to the above conventional components, an embodiment of the present invention presents a memory simulator (124) to track the temporal dynamics of human associative memory, and an optimizer (128) to optimize the learning sequence to achieve memory results that are far beyond that which can be achieved by the human brain itself. The system and method can be implemented as a web application, a program in a PC, and through a wireless device or handheld devices such as cell phone, PDA, watch or toy among others. An important feature of an embodiment of the present invention is that the learning/exercising process is optimized by the system and thus automates the process for the user. Due to the optimization according to the temporal dynamics of the human memory process, the process results in superior long-term memory formation and thus, a more robust human memory.
TL;DR: In this paper, a memory training and testing method based on speech recognition was proposed to improve the performance of memorizing the Chinese materials by the user, which conforms to the characteristics of the human memory process well.
Abstract: The invention discloses a memory training and testing method based on speech recognition. The method comprises the following steps: (1), performing memory training and memory testing on a user in theform of short sentences; (2), simulating a memorizing process including zero memory, partial memory, and complete memory, enabling the user carry out reading in a following manner, hiding part of texts gradually and enabling the user to recite the texts, and then hiding all texts and enabling the user to recite all texts; and (3), recording proficiency of the user for each Chinese character duringthe memorizing process and hiding the Chinese characters with low proficiency at a high probability during the training process so as to deepen the impression of the Chinese characters with low proficiency. According to the invention, the method conforms to the characteristics of the human memory process well; the efficiency and effect of memorizing the Chinese materials by the user are improved;and the good memory experience is provided.
TL;DR: The obtained results show that the proposed model is significantly associated with all the biological findings and theories related to memories and allows humanoid and game agents to take decisions and perform planning in novel situations.
Abstract:
The cognitive models based agents proposed in the existing patents are not
able to create knowledge by themselves. They also did not have the inference mechanism to take
decisions and perform planning in novel situations.
This patent proposes a method to mimic the human memory process for decision
making.
The proposed model simulates the functionality of episodic, semantic and procedural
memory along with their interaction system. The sensory information activates the activity nodes
which is a binding of concept and the sensory values. These activated activity nodes are captured by
the episodic memory in the form of an event node. Each activity node has some participation
strength in each event depending upon its involvement among other events. Recalling of events and
frequent usage of some coactive activity nodes constitute the semantic knowledge in the form of
associations between the activity nodes. The model also learns the actions in context to the activity
nodes by using reinforcement learning. The proposed model uses an energy-based inference mechanism
for planning and decision making.
The proposed model is validated by deploying it in a virtual war game agent and analysing
the results. The obtained results show that the proposed model is significantly associated with all the
biological findings and theories related to memories.
The implementation of this model allows humanoid and game agents to take decisions
and perform planning in novel situations.