Your task now is to draft a high-quality review for a top-tier Machine Learning (ML) conference for a submission:\n\nTitle:\n```\nA Study of Biologically Plausible Neural Network: the Role and Interactions of Brain-Inspired Mechanisms in Continual Learning\n```\n\nKeywords:\n```\nContinual Learning,Catastrophic Forgetting,Brain-inspired Mechanisms,Active Dendrites,Dale's Principle,Hebbian Learning,Sparsity\n```\n\nAbstract:\n```\nHumans excel at continually acquiring, consolidating, and retaining information from an ever-changing environment, whereas artificial neural networks (ANNs) exhibit catastrophic forgetting. There are considerable differences in the complexity of synapses, the processing of information, and the learning mechanisms in biological neural networks and their artificial counterpart, which may explain the mismatch in performance. We consider a biologically plausible framework that constitutes separate populations of exclusively excitatory and inhibitory neurons which adhere to Dale's principle and the excitatory pyramidal neurons are augmented with dendritic-like structures for context-dependent processing of stimuli. We then conduct a comprehensive study on the role and interactions of different mechanisms inspired by the brain including sparse non-overlapping representations, Hebbian learning, synaptic consolidation, and replay of past activations that accompanied the learning event. Our study suggests that employing multiple complementary mechanisms in a biologically plausible architecture, similar to the brain, can be effective in enabling continual learning in ANNs.\n```\n\nFigures/Tables Captions:\n```\nFigure 1: Architecture of one hidden layer in the biologically plausible framework. Each layer con- sists of separate populations of exclusively excitatory pyramidal cells and inhibitory neurons which adheres to Dale's principle. The shade indicates the strength of weights or activations where darker shade indicating higher value. (a) The pyramidal cells are augmented with dendritic segments which receive an additional context signal c and the dendrite segment whose weights are most aligned with the context vector (bottom row) is selected to modulate the output activity of the feedforward neu- rons for context-dependent processing of information. (b) The Hebbian update step further strength- ens the association between the context and the winning dendritic segment with maximum absolute value (indicated with darker shade for bottom row). Finally, Heterogeneous dropout keeps the ac- tivation count of each pyramidal cell (indicated with the gray shade) and drops the neurons which were most active for the previous task (darkest shade dropped) to enforce non-overlapping represen- tations. The top-k remaining cells then project to the next layer (increased shade).\nFigure 2: Total activation counts for the test set of each task (y-axis) for a random set of 25 units in the second hidden layer of the model. Heterogeneous dropout reduces the overlap in activations and facilitates the formation of task-specific subnetworks.\nFigure 3: Effect of dropout \u03c1 on the overlap between the distributions of layer two activation counts for each task in Perm-MNIST with 2 tasks. Higher \u03c1 reduces the overlap.\nFigure 4: Dendritic segment activations of a unit in layer 1 for the context vectors of each task for a model trained on Perm-MNIST with 5 tasks. Hebbian update strengthens the association between the context and the dendritic segments, increasing the magnitude of the modulating signal.\nTable 1: Effect of each component of the biologically plausible framework on different datasets with varying number of tasks. We first show the effect of utilizing feedforward neurons adhering to Dale's principle in conjunction with Active Dendrites to form the framework within which we evaluate the individual effect of brain-inspired mechanisms before combining them all together (along with Heterogeneous Dropout) to forge Bio-ANN. For all the experiments, we set the percentage of active neurons to 5. We provide the average task performance and 1 std of three runs.\nTable 2: Effect of different levels of sparsity in activations on the performance of the model. Columns show the ratio of active neurons (k in k-WTA activation), and the rows provide the number of tasks.\nTable 3: Effect of Heterogeneous dropout with increasing \u03c1 values on different datasets with varying number of tasks.\nTable 11: Effect of different levels of activation sparsity on Rot-MNIST with 5 tasks with varying degrees of incremental rotation (\u03b8 inc ) in each subsequent task. Row 0 shows (k l1 , k l2 ) the ratio of active neurons in the 1 st and 2 nd hidden layer, respectively.\nTable 4: Effect of each component of the biologically plausible framework on different Seq- FMNIST and Rot-FMNIST. For all experiments, we use a memory budget of 500 samples. HD refers to heterogeneous dropout. We provide the average task performance and 1 std of 5 runs.\nTable 5: The selected hyperparamneters for the experiments showing the individual effect of each component (Table 1). The base learning rate for all the experiments is 0.3 and the individual com- ponents use the same learning rates for W ie and W ei as (+ Dale's Principle). For + SC experiments, we use \u03bb Wie =10 and \u03bb Wei =10. For ER experiments, we use a memory budget of 500 samples.\nTable 6: The selected hyperparamneters for Bio-ANN experiments in Table 1. We use the same learning rate for each setting as + Dale's Principle (Table A.3).\nTable 7: Effect of adjusting the learning rates of W ie and W ei at the end of first task on different datasets with varying number of tasks.\nTable 8: Effect of scaling the importance estimate for W ie and W ei to reduce the parameter shift in the inhibitory weights on Rot-MNIST and Perm-MNIST datasets with varying number of tasks.\nTable 9: Effect of layerwise dropout \u03c1 on Rot-MNIST with 5 tasks with varying degrees of incre- mental rotation (\u03b8 inc ) in each subsequent task. Row 0 shows (\u03c1 l1 , \u03c1 l2 ) the \u03c1 values for the first and second hidden layer respectively.\nTable 10: Effect of different levels of sparsity in activations (ratio of active neurons, (k l1 , k l2 ) in 1 st and 2 nd hidden layer respectively) and connections (ratio of zero weights, S W ) on Rot-MNIST and Perm-MNNIST with increasing number of tasks. The best performance across the different sparsity levels for each task is in bold.\n```\n\nMain Content:\n```\n\nSection Title: INTRODUCTION\n  INTRODUCTION The human brain excels at continually learning from a dynamically changing environment whereas standard artificial neural networks (ANNs) are inherently designed for training from stationary i.i.d. data. Sequential learning of tasks in continual learning (CL) violates this strong assumption, re- sulting in catastrophic forgetting. Although ANNs are inspired by biological neurons ( Fukushima, 1980 ), they omit numerous details of design principles and learning mechanisms in the brain. These fundamental differences may account for the mismatch in performance and behavior. Biological neural networks are characterized by considerably more complex synapses and dynamic context-dependent processing of information. Also, the individual neurons have a specific role. Each presynaptic neuron has an exclusive excitatory or inhibitory impact on its postsynaptic part- ners, as postulated by Dale's principle ( Strata et al., 1999 ). Furthermore, distal dendritic segments in pyramidal neurons, which comprises the majority of excitatory cells in the neocortex, receive additional context information and enable context-dependent processing of information. This, in conjunction with inhibition, allows the network to learn task-specific patterns and avoid catastrophic forgetting ( Yang et al., 2014 ;  Iyer et al., 2021 ;  Barron et al., 2017 ). Furthermore, the replay of non- overlapping and sparse neural activities of previous experiences in the neocortex and hippocampus is considered to play a critical role in memory formation, consolidation, and retrieval ( Walker & Stickgold, 2004 ;  McClelland et al., 1995 ). To protect information from erasure, the brain employs synaptic consolidation in which plasticity rates are selectively reduced in proportion to strengthened synapses ( Cichon & Gan, 2015 ). Thus, we study the role and interactions of different mechanisms inspired by the brain in a bio- logically plausible framework in a CL setup. The underlying model constitutes separate popula- Dropout Context k-WTA Max Hebbian Update Dendrite Segments inactive pyramidal cell active pyramidal cell inhibitory cell fully connected (b) (a) learnable weights context vector tions of exclusively excitatory and inhibitory neurons in each layer which adheres to Dale's princi- ple ( Cornford et al., 2020 ) and the excitatory neurons (mimicking pyramidal cells) are augmented with dendrite-like structures for context-dependent processing of information ( Iyer et al., 2021 ). Dendritic segments process an additional context signal encoding task information and subsequently modulate the feedforward activity of the excitatory neuron ( Figure 1 ). We then systematically study the effect of controlling the overlap in representations, employing the \"fire together, wire together\" learning paradigm and employing experience replay and synaptic consolidation. Our empirical study shows that: Our study suggests that employing multiple complementary mechanisms in a biologically plausible architecture, similar to what is believed to exist in the brain, can be effective in enabling CL in ANNs.\n\nSection Title: BIOLOGICALLY PLAUSIBLE FRAMEWORK FOR CL\n  BIOLOGICALLY PLAUSIBLE FRAMEWORK FOR CL We provide details of the biologically plausible framework within which we conduct our study.\n\nSection Title: DALE'S PRINCIPLE\n  DALE'S PRINCIPLE Biological neural networks differ from their artificial counterparts in the complexity of the synapses and the role of individual units. In particular, the majority of neurons in the brain adhere to Dale's principle, which posits that presynaptic neurons can only have an exclusive excitatory or inhibitory impact on their postsynaptic partners ( Strata et al., 1999 ). Several studies show that the balanced dynamics ( Murphy & Miller, 2009 ; Van Vreeswijk & Sompolinsky, 1996) of excitatory and in- hibitory populations provide functional advantages, including efficient predictive coding ( Boerlin et al., 2013 ) and pattern learning ( Ingrosso & Abbott, 2019 ). Furthermore, inhibition is hypoth- esized to play a role in alleviating catastrophic forgetting ( Barron et al., 2017 ). Standard ANNs, however, lack adherence to Dale's principle, as neurons contain both positive and negative output weights, and signs can change while learning.  Cornford et al. (2020)  incorporate Dale's principle into ANNs (referred to as DANNs), which take into account the distinct connectivity patterns of excitatory and inhibitory neurons ( Tremblay et al., 2016 ) and perform comparable to standard ANNs in the benchmark object recognition task. Each layer l comprises of a separate population of excitatory, h l e \u2208 R ne + , and inhibitory h l i \u2208 R ni + neurons, where n e \u226b n i and synaptic weights are strictly non-negative. Similar to biological networks, while both populations receive excitatory projections from the previous layer (h l\u22121 e ), only excitatory neurons project between layers, whereas inhibitory neurons inhibit the activity of excitatory units of the same layer.  Cornford et al. (2020)  characterized these properties by three sets of strictly positive weights: excitatory connections between layers W l ee \u2208 R ne\u00d7ne + , excitatory projection to inhibitory units W l ie \u2208 R ni\u00d7ne + , and inhibitory projections within the layer W l ei \u2208 R ne\u00d7ni + . The output of the excitatory units is impacted by the subtractive inhibition from the inhibitory units: z l = (W l ee \u2212 W l ei W l ie )h l\u22121 e + b l (1) where b l \u2208 R ne is the bias term.  Figure 1  shows the interactions and connectivity between excitatory pyramidal cells (triangle symbol) and inhibitory neurons (denoted by i). We aim to employ DANNs as feedforward neurons to show that they can also learn in a challenging CL setting and performance comparable to standard ANNs and provide a biologically plausible framework for further studying the role of inhibition in alleviating catastrophic forgetting.\n\nSection Title: ACTIVE DENDRITES\n  ACTIVE DENDRITES The brain employs specific structures and mechanisms for context-dependent processing and routing of information. The prefrontal cortex, which plays an important role in cognitive control (Miller & Cohen, 2001), receives sensory inputs as well as contextual information, which allows it to choose the most relevant sensory features for the present task to guide actions ( Mante et al., 2013 ;  Fuster, 2015 ;  Siegel et al., 2015 ;  Zeng et al., 2019 ). Of particular interest are pyramidal cells, which rep- resent the most populous members of the excitatory family of neurons in the brain ( Bekkers, 2011 ). The dendritic spines in pyramid cells exhibit highly nonlinear integrative properties which are con- sidered important for learning task-specific patterns ( Yang et al., 2014 ). Pyramidal cells integrate a range of diverse inputs into multiple independent dendritic segments, whereby contextual inputs in active dendrites can modulate a neuron's response, making it more likely to fire. However, stan- dard ANNs are based on a point neuron model ( Lapique, 1907 ) which is an oversimplified model of biological computations and lacks the sophisticated nonlinear and context-dependent behavior of pyramidal cells.  Iyer et al. (2021)  model these integrative properties of dendrites by augmenting each neuron with a set of dendritic segments. Multiple dendritic segments receive additional contextual information, which is processed using a separate set of weights. The resultant dendritic output modulates the feedforward activation which is computed by a linear weighted sum of the feedforward inputs. This computation results in a neuron where the magnitude of the response to a given stimulus is highly context-dependent. To enable task-specific processing of information, the prototype vector for task \u03c4 is evaluated by taking the element-wise mean of the tasks samples, D \u03c4 at the beginning of the task and then subsequently provided as context during training. During inference, the closest prototype vector to each test sample, x \u2032 , is selected as the context using the Euclidean distance among all task prototypes, C, stored in memory. Following  Iyer et al. (2021) , we augment the excitatory units in each layer with dendritic segments ( Figure 1 (a) ). The feedforward activity of excitatory units is modulated by the dendritic segments which receive a context vector. Given the context vector, each dendritic segment j computes u T j c, given weight u j \u2208 R d and the context vector c \u2208 R d where d is the dimensions of the input image. For excitatory neurons, the dendritic segment with the highest response to the context (maximum absolute value with the sign retained) is selected to modulate output activity, h l e = k-WTA(z l \u00d7 \u03c3(u T \u03ba c)), where \u03ba = arg max j |u T j c| (4) where \u03c3 is the sigmoid function ( Han & Moraga, 1995 ) and k-WTA(.) is the k-Winner-Take-All activation function ( Ahmad & Scheinkman, 2019 ) which propagates only the top k neurons and sets the rest to zero. This provides us with a biologically plausible framework where, similar to biological networks, the feedforward neurons adheres to Dale's principle and the excitatory neurons mimic the integrative properties of active dendrties for context-dependent processing of stimulus.\n\nSection Title: CONTINUAL LEARNING SETTINGS\n  CONTINUAL LEARNING SETTINGS To study the role of different components inspired by the brain in a biologically plausible NN for CL and gauge their roles in the performance and characteristics of the model, we conduct all our experiments under uniform settings. Implementation details and experimental setup are provided in the Appendix. We evaluate the models on two CL scenarios. Domain incremental learning (Domain-IL) refers to the CL scenario in which the classes remain the same in subsequent tasks but the input distribution changes. We consider Rot-MNIST which involves classifying the 10 digits in each task with each digit rotated by an angle between 0 and 180 degrees and Perm-MNIST which applies a fixed random permutation to the pixels for each task. Importantly, there are different vari- ants of Rot-MNIST with varying difficulties. We incrementally rotate the digits to a fixed degree, i.e. {0, 8, ..., (N-1)*8} for task {\u03c4 1 , \u03c4 2 , .., \u03c4 N } which is substantially more challenging than random sampling rotations. Importantly, the Rot-MNIST dataset captures the notion of similarity in subse- quent tasks where the similarity between two tasks is defined by the difference in their degree of rotation, whereas each task in Perm-MNIST is independent. We also consider the challenging Class incremental learning (Class-IL) scenario where new classes are added with each subsequent task and the agent must learn to distinguish not only amongst the classes within the current task but also across all learned tasks. Seq-MNIST divides the MNIST classification into 5 tasks with 2 classes in each task.\n\nSection Title: EMPIRICAL EVALUATION\n  EMPIRICAL EVALUATION To investigate the impact of the different components inspired by the brain, we use the aforemen- tioned biologically plausible framework and study the effect on the performance and characteristics of the model.\n\nSection Title: EFFECT OF INHIBITORY NEURONS\n  EFFECT OF INHIBITORY NEURONS We first study whether feedforward networks with separate populations of excitatory and inhibitory units can work well in the CL setting. Importantly, we note that when learning a sequence of tasks with inhibitory neurons, it is beneficial to take into account the disparities in the degree to which updates to different parameters affect the layer's output distribution ( Cornford et al., 2020 ) and hence forgetting. Specifically, as W l ie and W l ei affect the output distribution to a higher degree than W l ee , we reduce the learning rate for these weights after the first task (see Appendix).  Table 1  shows that models with feedforward neurons adhering to Dale's principle perform as well as the standard neurons and can also further mitigate forgetting in some cases. Note that this gain comes with considerably fewer parameters and context-dependent processing, as we keep the num- ber of neurons in each layer the same, and only the excitatory neurons (\u223c90%) are augmented with dendritic segments. For 20 tasks, Active Dendrite with Dale's principle reduces the parameters from \u223c70M to less than \u223c64M parameters. We hypothesize that having separate populations within a layer enables them to play a specialized role. In particular, inhibitory neurons can selectively inhibit certain excitatory neurons based on stimulus, which can further facilitate the formation of task- specific subnetworks and complement the context-dependent processing of information by dendritic segments.\n\nSection Title: SPARSE ACTIVATIONS FACILITATE THE FORMATION OF SUBNETWORKS\n  SPARSE ACTIVATIONS FACILITATE THE FORMATION OF SUBNETWORKS Neocortical circuits are characterized by high levels of sparsity in neural connectivity and activa- tions ( Barth & Poulet, 2012 ;  Graham & Field, 2006 ). This is in stark contrast to the dense and highly entangled connectivity in standard ANNs. Particularly for CL, sparsity provides several ad- vantages: sparse non-overlapping representations can reduce interference between tasks ( Abbasi et al., 2022 ;  Iyer et al., 2021 ;  Aljundi et al., 2018 ), can lead to the natural emergence of task-specific modules ( Hadsell et al., 2020 ), and sparse connectivity can further ensure fewer task-specific pa- rameters ( Mallya et al., 2018 ). We study the effect of different levels of activation sparsity by varying the ratio of active neurons in k-winner-take-all (k-WTA) activations ( Ahmad & Scheinkman, 2019 ). Each hidden layer of our model has a constant sparsity in its connections (randomly 50% of weights are set to 0 at initial- ization) and propagates only the top-k activations (in  Figure 1 , k-WTA layer).  Table 2  shows that sparsity plays a critical role in enabling CL in DNNs. Sparsity in activations effectively reduces interference by reducing the overlap in representations. Interestingly, the stark difference in the effect of different levels of sparse activations on Rot-MNIST and Perm-MNIST highlights the im- portance of considering task similarity in the design of CL methods. As the tasks in Perm-MNIST are independent of each other, having fewer active neurons (5%) enables the network to learn non- overlapping representations for each task while the high task similarity in Rot-MNIST can benefit from overlapping representation, which allows for the reusability of features across the tasks. The number of tasks the agent has to learn also has an effect on the optimal sparsity level. In Appendix, we show that having different levels of sparsity in different layers can further improve performance. As the earlier layers learn general features, having a higher ratio of active neurons can enable higher resuability and forward transfer. For the later layers, a smaller ratio of active neurons can reduce interference between task-specific features.\n\nSection Title: HETEROGENEOUS DROPOUT FOR NON-OVERLAPPING ACTIVATIONS AND SUBNETWORKS\n  HETEROGENEOUS DROPOUT FOR NON-OVERLAPPING ACTIVATIONS AND SUBNETWORKS The information in the brain is encoded by the strong activation of a relatively small set of neurons, forming sparse coding. A different subset of neurons is utilized to represent different types of stimuli  Graham & Field (2006) . Furthermore, there is evidence of non-overlapping representations in the brain. To miimic this, we employ Heterogeneous dropout ( Abbasi et al., 2022 ) which in conjunction with context-dependent processing of information can effectively reduce the overlap of representations which leads to less interference between the tasks and thereby less forgetting. During training, we track the frequency of activations for each neuron in a layer for a given task, and in the subsequent tasks, the probability of a neuron being dropped is inversely proportional to its activation counts. This encourages the model to learn the new task by utilizing neurons that have been less active for previous tasks.  Figure 1  shows that neurons that have been more active (darker shade) are more likely to be dropped before k-WTA is applied. Specifically, let [a l t ] j denote the activation counter for neuron j in layer l after learning t tasks. For learning task t+1, the probability of this neuron being retained is given by: [p l t+1 ] j = exp( \u2212[a l t ] j max j [a l t ] j \u03c1) (5) where \u03c1 controls the strength of enforcement of non-overlapping representations with larger values leading to less overlap. This provides us with an efficient mechanism for controlling the degree of overlap between the representations of different tasks, and hence the degree of forward transfer and interference based on the task similarities.  Table 3  shows that employing Heterogeneous dropout can further improve the performance of the model. We also analyze the effect of the \u03c1 parameter on the activation counts and the overlap in the representations.  Figure 2  shows that Heterogeneous dropout can facilitate the formation of task- specific subnetworks and  Figure 3  shows the symmetric KL-divergence between the distribution of activation counts on the test set of Task 1 and Task 2 on the model trained with different \u03c1 values on Perm-MNIST with two tasks. As we increase the \u03c1 parameter, the activations in each layer become increasingly dissimilar. Similar to the sparsity in activations, in the Appendix, we show that having different dropout \u03c1 for each layer (with lower \u03c1 for earlier layers to encourage resuability and higher \u03c1 for later layers to reduce interference between task representations) can further improve the performance. Heterogeneous dropout provides a simple mechanism for balancing the reusability and interference of features depending on the similarity of tasks.\n\nSection Title: HEBBIAN LEARNING STRENGTHENS CONTEXT GATING\n  HEBBIAN LEARNING STRENGTHENS CONTEXT GATING For a biologically plausible ANN, it is important not only to incorporate the design elements of biological neurons but also the learning mechanisms it employs. Lifetime plasticity in the brain generally follows the Hebbian principle: a neuron that consistently contributes in making another neuron fire will build a stronger connection to that neuron ( Hebb, 2005 ). Therefore, we follow the approach in  Flesch et al. (2022)  to complement error-based learning with Hebbian update to strengthen the connections between contextual information and dendritic seg- ments (Figure 1(b)). Each supervised parameter update with backpropagation is followed by a Hebbian update step on the dendritic segments to strengthen the connections between the context input and the corresponding dendritic segment which is activated. To constrain the parameters, we use Oja's rule which adds weight decay to Hebbian learning (Oja, 1982), u \u03ba \u2190 u \u03ba + \u03b7 h d(c \u2212 du \u03ba ) (6) where \u03b7 h is the learning rate, \u03ba is the index of the winning dendrite with weight u \u03ba and modulating signal d = u T \u03ba c for the context signal c.  Figure 4  shows that the Hebbian update step increases the magnitude of the modulating signal from the dendrites on the feedforward activity, which can further strengthen context-dependent gating and facilitate the formation of task-specific subnetworks.  Table 1  shows that this results in a consistent improvement in performance.\n\nSection Title: SYNAPTIC CONSOLIDATION FURTHER MITIGATES FORGETTING\n  SYNAPTIC CONSOLIDATION FURTHER MITIGATES FORGETTING In addition to their integrative properties, dendrites also play a key role in retaining information and providing protection against erasure ( Cichon & Gan, 2015 ;  Yang et al., 2009 ). The new spines that are formed on different sets of dendritic branches in response to learning different tasks are protected from being eliminated through mediation of synaptic plasticity and structural changes that persist when learning a new task ( Yang et al., 2009 ). We employ synaptic consolidation by incorporating Synaptic Intelligence ( Zenke et al., 2017 ) (de- tails in Appendix) which maintains an importance estimate of each synapse in an online manner during training and subsequently reduces the plasticity of synapses which are considered important for learned tasks. Notably, we adjust the importance estimate to account for the disparity in the degree to which updates to different parameters affect the output of the layer due to the inhibitory interneuron architecture in the DANN layers ( Cornford et al., 2020 ). The importance estimate of the excitatory connections to the inhibitory units and the intra-layer inhibitory connections are upscaled to further penalize changes to these weights.  Table 1  shows that employing Synaptic Intelligence (+SC) in this manner further mitigates forgetting. Particularly for Rot-MNIST with 20 tasks, it provides considerable performance improvement.\n\nSection Title: EXPERIENCE REPLAY IS ESSENTIAL FOR ENABLING CL IN CHALLENGING SCENARIOS\n  EXPERIENCE REPLAY IS ESSENTIAL FOR ENABLING CL IN CHALLENGING SCENARIOS Replay of past neural activation patterns in the brain is considered to play a critical role in memory formation, consolidation, and retrieval ( Walker & Stickgold, 2004 ;  McClelland et al., 1995 ). The replay mechanism in the hippocampus ( Kumaran et al., 2016 ) has inspired a series of rehearsal-based approaches ( Li & Hoiem, 2017 ;  Chaudhry et al., 2018 ;  Lopez-Paz & Ranzato, 2017 ;  Arani et al., 2022 ) which have proven to be effective in challenging continual learning scenarios ( Farquhar & Gal, 2018 ;  Hadsell et al., 2020 ). Therefore, to replay samples from previous tasks, we utilize a small episodic memory buffer that is maintained through Reservoir sampling ( Vitter, 1985 ). It attempts to approximately match the distribution of the incoming stream by assigning equal probability to each new sample to be represented in the buffer. During training, samples from the current task, (x b , y b ) \u223c D \u03c4 , are interleaved with memory buffer samples, (x m , y m ) \u223c M to approximate the joint distribution of tasks seen so far. Furthermore, to mimic the replay of the activation patterns that accompanied the learning event in the brain, we also save the output logits, z m , across the training trajectory and enforce a consistency loss when replaying the samples from the episodic memory. Concretely, the loss is given by: where f (.; \u03b8) is the model parameterized by \u03b8, L cls is the standard cross-entropy loss, and \u03b1 and \u03b2 controls the strength of interleaved training and consistency constraint, respectively.  Table 1  shows that experience replay (+ER) complements the context-dependent processing of in- formation and enables the model to learn the joint distribution well in varying challenging settings. Particularly, the failure of the model to avoid forgetting in the Class-IL setting (Seq-MNIST) without experience replay suggests that context-dependent processing of information alone does not suffice. and experience replay might be essential. Adding consistency regularization (+CR) further improves performance as the model receives additional relational information about the structural similarity of classes, which facilitates the consolidation of information.\n\nSection Title: COMBINING THE INDIVIDUAL COMPONENTS\n  COMBINING THE INDIVIDUAL COMPONENTS Having shown the individual effect of each of the brain-inspired components in the biologically plausible framework, here we look at their combined effect. The resultant model is referred to as Bio-ANN.  Table 1  shows that the different components complement each other and consistently improve the performance of the model. The empirical results suggest that employing multiple com- plementary components and learning mechanisms, similar to the brain, can be an effective approach to enable continual learning in ANNs.\n\nSection Title: DISCUSSION\n  DISCUSSION We conducted a study on the effect of different brain-inspired mechanisms under a biologically plau- sible framework in the CL setting. The underlying model incorporates several key components of the design principles and learning mechanisms in the brain: each layer constitutes separate populations of exclusively excitatory and inhibitory units, which adheres to Dale's principle, and the excita- tory pyramidal neurons are augmented\n```\n\n======\nYour task:\nCompose a high-quality peer review of an ML paper submitted to a top-tier ML conference on OpenReview.\n\nThe review form is shown below:\n\n1. Summary and contributions: Briefly summarize the paper and its contributions\n\n2. Strengths: Describe the strengths of the work. Typical criteria include: soundness of the claims (theoretical grounding, empirical evaluation), significance and novelty of the contribution, and relevance to the community.\n\n3. Weaknesses: Explain the limitations of this work along the same axes as above.\n\n4. Correctness: Are the claims and method correct? Is the empirical methodology correct?\n\n5. Clarity: Is the paper well written?\n\n6. Relation to prior work: Is it clearly discussed how this work differs from previous contributions?\n\n7. Reproducibility: Are there enough details to reproduce the major results of this work?\n\n8. Additional feedback, comments, suggestions for improvement and questions for the authors\n\n9.  Overall score:\nYou should NOT assume that you were assigned a representative sample of submissions, nor should you adjust your scores to match the overall conference acceptance rates. The \u201cOverall Score\u201d for each submission should reflect your assessment of the submission\u2019s contributions.\n10): Top 5% of accepted papers. Truly groundbreaking work.\n9): Top 15% of accepted NeurIPS papers. An excellent submission; a strong accept.\n8): Top 50% of accepted NeurIPS papers. A very good submission; a clear accept.\n7): A good submission; accept.nI vote for accepting this submission, although I would not be upset if it were rejected.\n6): Marginally above the acceptance threshold. I tend to vote for accepting this submission, but rejecting it would not be that bad.\n5): Marginally below the acceptance threshold. I tend to vote for rejecting this submission, but accepting it would not be that bad.\n4): An okay submission, but not good enough; a reject. I vote for rejecting this submission, although I would not be upset if it were accepted.\n3): A clear reject. I vote and argue for rejecting this submission.\n2): I'm surprised this work was submitted to NeurIPS; a strong reject.\n1): Trivial or wrong or already known.\n\n10.  Confidence score:\n5): You are absolutely certain about your assessment. You are very familiar with the related work.\n4): You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.\n3): You are fairly confident in your assessment.  It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.\n2): You are willing to defend your assessment, but it is quite likely that you did not understand central parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.\n1): Your assessment is an educated guess. The submission is not in your area or the submission was difficult to understand. Math/other details were not carefully checked.\n\n11. Have the authors adequately addressed the broader impact of their work, including potential negative ethical and societal implications of their work?\n\n12. Does the submission raise potential ethical concerns? This includes methods, applications, or data that create or reinforce unfair bias or that have a primary purpose of harm or injury. If so, please explain briefly.\n\n