Extending the Framework of Equilibrium Propagation to General Dynamics
The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists. Two major reasons are that neurons would need to send two different types of signal in the forward and backward phases, and that pairs of neurons would need to communicate through symmetric bidirectional connections. We present a simple two-phase learning procedure for fixed point recurrent networks that addresses both these issues. In our model, neurons perform leaky integration and synaptic weights are updated through a local mechanism. Our learning method extends the framework of Equilibrium Propagation to general dynamics, relaxing the requirement of an energy function. As a consequence of this generalization, the algorithm does not compute the true gradient of the objective function, but rather approximates it at a precision which is proven to be directly related to the degree of symmetry of the feedforward and feedback weights. We show experimentally that the intrinsic properties of the system lead to alignment of the feedforward and feedback weights, and that our algorithm optimizes the objective function.
 The paper proposes a new learning algorithm for learning neural networks that may be biologically plausible. The paper builds upon the paper by Scellier & Bengio but doesn*t assume symmetric weights. I couldn*t judge how solid the biological plausibility argument but my understanding is that there is no universal agreement in neuroscience about it so I would tend to be open to most of the suggestions. As a non-expert in this field, I found this result of this paper pretty interesting, given experimentally the algorithm does work well for MNIST (which is already interesting to me, given the limited progress in this area).
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Extending the Framework of Equilibrium Propagation to General Dynamics
The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists. Two major reasons are that neurons would need to send two different types of signal in the forward and backward phases, and that pairs of neurons would need to communicate through symmetric bidirectional connections. We present a simple two-phase learning procedure for fixed point recurrent networks that addresses both these issues. In our model, neurons perform leaky integration and synaptic weights are updated through a local mechanism. Our learning method extends the framework of Equilibrium Propagation to general dynamics, relaxing the requirement of an energy function. As a consequence of this generalization, the algorithm does not compute the true gradient of the objective function, but rather approximates it at a precision which is proven to be directly related to the degree of symmetry of the feedforward and feedback weights. We show experimentally that the intrinsic properties of the system lead to alignment of the feedforward and feedback weights, and that our algorithm optimizes the objective function.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The paper proposes a new learning algorithm for learning neural networks that may be biologically plausible. The paper builds upon the paper by Scellier & Bengio but doesn*t assume symmetric weights. I couldn*t judge how solid the biological plausibility argument but my understanding is that there is no universal agreement in neuroscience about it so I would tend to be open to most of the suggestions. As a non-expert in this field, I found this result of this paper pretty interesting, given experimentally the algorithm does work well for MNIST (which is already interesting to me, given the limited progress in this area).
assistant
 The paper proposes a new learning algorithm for learning neural networks that may be biologically plausible. The paper builds upon the paper by Scellier & Bengio but doesn*t assume symmetric weights. I couldn*t judge how solid the biological plausibility argument but my understanding is that there is no universal agreement in neuroscience about it so I would tend to be open to most of the suggestions. As a non-expert in this field, I found this result of this paper pretty interesting, given experimentally the algorithm does work well for MNIST (which is already interesting to me, given the limited progress in this area).