Consolidate following text and provide me 3 questions i can ask. " First, could you explain a bit more about the Softmax function? Why did we use it, and what are its characteristics?
Also, please elaborate on how the Modern Hopfield network addresses the limitations related to storing patterns in the network.
What do you think is the fundamental problem in executing the paper's approach regarding the definition of the creativity? I believe that the creativity itself isn’t inherently present. In many cases there is no idea available from the past and things come to your mind all of a sudden. however, the Hopfield networks have a number of predefined patterns which means that we have some background information stored. This means that even though the results may appear creative, the search for those patterns somewhat restricts creativity. What do you think about this inherent paradox?
What are your thoughts on patterns that have high correlation and covariance in the inputs? Do you think traditional Hopfield systems can retrieve information from them? If not, How can we fix this issue of correlation in our patterns. What if more than  one set of pattern could be retrieved from a stored pattern ( like what if we have a combination of two images in one image?) How can we discern these two?
I studied Boltzmann functions a bit for my presentation. Do you think there is a conceptual connection between these functions and the concepts presented in your slides? Also what about the concept of Transformers in Neural Networks? 

1) I understand that for a perturbed (noisy) pattern to be fed into the network, the original version of this "noisy" pattern needs to be suppressed. This suppression of the original pattern is attained by removing its contribution from the weight matrix of the trained Hopfield network. My question is as follows:

Wouldn’t the deletion of a pattern result in the formation of other spurious states? Since the “reduced” weight matrix (obtained by subtracting the contributions of pattern k from W) contains information with respect to the (k-1) patterns and the set of unseen spurious states which are formed as result of this training. If this is true, how do you account for these effects?
2) The authors also mention that the "threshold" is an important parameter for their conceptual framework. It was mentioned that there is a range of acceptable threshold values within which the network has perfect pattern recollection for each of its stored patterns. However, isn't this range also dependent on the number of neurons in the network (i.e., the size of the network)? I would like to understand this, especially since we know that a Hopfield network's storage capacity is known to depend on its size.

Besides this, I would appreciate if you could provide an intuitive explanation of the modern HNN as described in the paper. I am sure it is a super useful concept, but I am still struggling to (intuitively) understand its underlying working mechanism.
Thank you for the wonderful and clear presentation, it was to the point, also I liked your choice of the paper, not something we read about everyday. I appreciated the innovative approach of using Hopfield Neural Networks to model creativity-based semantic associations. This is a novel and exciting direction in the field of computational neuroscience. Moreover, the paper provides a clear and detailed explanation of the concepts of the "first knob of creativity" and the "second knob of creativity," which are crucial for understanding the creative functioning of the network, and the author suggesting several promising future directions for research, which opens up new avenues for exploration and further development of the framework. However, I have some questions:

How does the conceptual framework for creativity-based semantic associations using Hopfield Neural Networks (HNN) differ from traditional models of creativity?
 

The paper discusses the importance of threshold limits in HNN. How do these thresholds influence the network's ability to act creatively, and what are the implications for practical applications?
 

The authors introduce the idea of pattern suppression to facilitate creative functioning. Can you elaborate on the mechanism of pattern suppression and its role in enhancing creativity?
 

What are the key differences between modern HNN and binary HNN, and why was the transition to modern HNN necessary for the study?"