Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: summary: This work combines probabilistic learning in the tradition of the Helmholtz machine with the recent hypothetical ideas about apical dendrites carrying top-down learning signals (expectation) to implement BP using local learning rule promoted earlier by numerous authors such as Larkum,  Lillicrap and Richards.  There are innovations. The impression learning mechanism proposed does sleep-wake in real-time.  It avoids the offline sleep phase in learning by using a global switching signal that can briefly substitute real incoming data with generative samples to enable learning continuously in a way that minimally perturbs natural neural trajectories.  This presents another new approach that allows the brain to learn generative models through local synaptic plasticity while concurrently performing sampling-based approximate inference wrt these models.  Incidentally, this work has been presented in COSYNE 2021 by the authors.


main_review: Conceptually, the advance might be rather incremental.   It synthesizes many current existing ideas into a coherent framework.  It is well-written,  with mathematical rigor (more so than most of the earlier models). One weakness is that the approach has not been tested rigorously and compared against BP and other biologically-plausible BPs on benchmark datasets ( MINST, CIFAR or ImageNet) to evaluate its performance or to establish its competency as are customarily done nowadays in similar submissions.  Besides, as the authors admit, there is no biological evidence for this global switching signal.  While its relevance to understanding the brain is rather limited at present, it might stimulate new experimental research. 
limitations_and_societal_impact: Broader impact section is missing.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: The authors proposed an algorithm called impression learning as a mechanism for the brain to learn the generative model of sensory inputs and meanwhile performs probabilistic inference of this model. The authors proposed a local synaptic learning rule to learn the generative model, and considered sampling-based inference. It is a valuable contribution to our understanding the general principle of neural coding.
main_review: Originality:
The main idea is based on wake-sleep algorithm, but has novel non-trivial improvements.
Strength: 
This work proposes a novel mechanism for the brain to perform approximate online Bayesian inference through a local learning rule, which is quite interesting.
Weaknesses: 
The biological plausibility of some elements need to be clarified.	
1) what is the biological meaning of D_r in equation 6 ?  D_r  seems to be a recurrent connection matrix in the latent layer, but it is  diagonal and does not play a role when k_t=1.
2) the neuron should receive both top-down (apical) and bottom-up (basal) inputs concurrently，the variable λ_t induced by dopamine may control the learning phase (the infer phase and generative phase), but should not also control the apical inputs and basal inputs.
Clarity:
It is OK.
Significance:
It is an interesting idea to implement concurrent the generative model learning and the model inference.  

limitations_and_societal_impact: Not applicable.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: In this paper the authors introduce a framework for approximate online Bayesian inference in an unsupervised setting. This framework generalises the wake-sleep algorithm and enables the derivation of a local learning rule. The generative and inference components of the model are mapped onto distal and basal dendrites, respectively. A number of experiments are conducted to demonstrate the model behaviour using both artificial (toy) and naturalistic tasks.
main_review: The framework introduced by the authors is interesting and novel, and it provides a generalization of the wake sleep algorithm. The paper is well written, but some elements are missing to make the contribution clear (see below). The main contribution is the derivation of a local learning rule with elements that can be mapped onto distal and basal dendrites. This could be of high significance for computational neuroscience but its at present hard to fully assess (see below).

*Major points*:
 - The link between the learning rule derived and biology is far from clear. There is only a very brief discussion on this in page 5 and the Discussion. But if this model is to be considered for understanding how basal/distal interactions lead to synaptic plasticity, this needs more discussion.
 - Importantly the basal learning rule (14) does not seem to depend on distal activity? This would be at odds with biology, as there is strong evidence that distal activity drives basal plasticity (see for example work by the labs of Anthony Holtmaat, Jeffrey Magee and Jesper Sjostrom).
 - Given that the algorithm proposed can do faster/online learning compared to the WS algorithm, why was it not used as one of the comparisons? It seems like one of the key models to contrast with. In particular, it would be important to show that this model does indeed need shorter offline phases. This seems to be one of the key contributions here that does not appear to have been clearly demonstrated.
 - In general a closer link between the model and existing/recent observations in how basal/distal interact would strength the story.


*Minor points*:

 - 119: What is the biological interpretation for the gating variable, k? 

 - 160: Does the network still learn if the latent space dimension or generative noise level do not match their ground truth values?  

 - 226: It would be interesting to mention if the network can learn when the phase duration is not the same for the two phases or if it can vary over the course of training. 

 - 274: I think the distinction between excitatory and inhibitory neurons is an important point. Perhaps this could be elaborated more. 

 - Fig 2c, 2d, 3b: choice of colours for NVI vs IL could be more distinctive 

- The authors use D donation for both Diagonal and non-diagonal matrices. This can cause confusion, in particular in Appendix D.1 (model structure), where D_2 is the diagonal transition matrix, and D_1 is the prediction weights. I suggest authors use different matrix notation or more informative subscripts.

 - In Eq.6, when the /lambda switches to 0 from 1, the value of k becomes 1, which in result the r^{gen}_t = r_{t-1} + \sigma_{r}^{gen} \eta_t . However, in Eq.15, there is no update when k_t = 1. So, it is not clear why this gating bias is in the training of the generative transition parameters D_r when there is no update at that time step. Can you please clarify this.

 - In Eq. 3, the second line, [log /hat{p} / \hat{q}] are approximated by Taylor expansion around 1. It is not clear why it is a valid expansion about 1. The authors explain in detail how this leads to introducing bias in parameter update in appendix A, but it would be good to briefly explain what are the consequences of this approximation, in particular as this seems to be a novel element compared to previous work.


limitations_and_societal_impact: There are a number of limitations that have not been fully addressed in terms of relating the model to biology (see comments above).
review_title: null

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: The authors are interested in understanding how brain circuits are able to perform unsupervised learning. The work is an extension of the wake-sleep algorithm, where the algorithm is asked to perform two functions: i) infer latent variables from observations and ii) build a generative model of the received observations. They start by defining a loss function to be minimized for their system to perform both functions and then interpret this minimization as a plasticity rule between the weights of a network of neurons. Their learning algorithm is compared to other existing one in the literature, and its efficiency is probed on a real-world task.  
main_review: Although I am not an expert on this topic I have enjoyed reading the paper. The problem of designing unsupervised learning algorithm that could be mapped onto the functioning of brain circuits is of fundamental importance for both neuroscience and AI, and this work seems a solid contribution towards this goal. The paper is well written and clearly situates its contribution with respect to the existing literature. The direct comparison with other existing approaches, as well as the probing of performance of the algorithm on a real-world task make the work appears as high-quality. The mapping between the inference part of the algorithm and processing at the basal part of neurons, and the mapping between the generative part of the algorithm and processing at the apical part of neurons seem well sounded and allows to discuss experimental results in neuroscience. It would be nice to mention how current views on plasticity relates to the idea that weight updates should depend from firing rates of pre- and post- synaptic cells as well as basal current. 



limitations_and_societal_impact: Yes
review_title: null

Meta-review: This paper introduces an unsupervised local plasticity rule to perform approximate online Bayesian inference of latent structure from sensory stimuli. All reviewers agree on the novelty and significance of the paper. However, reviewers brought up concerns about biological plausibility of some elements of the proposed algorithm, which should be addressed.
Decision: Accept (Poster)