SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
Authors: Jiayue Liu
Zhongchao Yi
Zhengyang Zhou
Qihe Huang
Kuo Yang
Xu Wang
Yang Wang
Venue: ICML 2025 spotlightposter (2025)
Keywords: NeuroAI
spatiotemporal learning
cross-domain adaptation
Primary area: applications->neuroscience_cognitive_science
TL;DR: This work investigate coupling neuroscience theories with spatiotemporal network to allow elastic model growing and empower efficient cross-domain adaptation, which facilitates the sustainable urban computing.

Abstract: Discovering regularities from spatiotemporal systems can benefit various scientific and social planning. Current spatiotemporal learners usually train an independent model from a specific source data that leads to limited transferability among sources, where even correlated tasks requires new design and training. The key towards increasing cross-domain knowledge is to enable collective intelligence and model evolution. In this paper, inspired by neuroscience theories, we theoretically derive the increased information boundary via learning cross-domain collective intelligence and propose a Synaptic EVOlutional spatiotemporal network, SynEVO, where SynEVO breaks the model independence and enables cross-domain knowledge to be shared and aggregated. Specifically, we first re-order the sample groups to imitate the human curriculum learning, and devise two complementary learners, elastic common container and task-independent extractor to allow model growth and task-wise commonality and personality disentanglement. Then an adaptive dynamic coupler with a new difference metric determines whether the new sample group should be incorporated into common container to achieve model evolution under various domains. Experiments show that SynEVO improves the generalization capacity by at most 42\% under cross-domain scenarios and SynEVO provides a paradigm of NeuroAI for knowledge transfer and adaptation.
Code available at [https://github.com/Rodger-Lau/SynEVO](https://github.com/Rodger-Lau/SynEVO).

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
Jiayue Liu' Zhongchao Yi!
Zhengyang Zhou 12
Qihe Huang
Kuo Yang'
Xu Wang 12
Yang Wang' 12
Abstract
1. Introduction
Discovering regularities from spatiotemporal sys- tems can benefit various scientific and social planning. Current spatiotemporal learners usu- ally train an independent model from a specific source data that leads to limited transferability among sources, where even correlated tasks re- quires new design and training. The key to- wards increasing cross-domain knowledge is enable collective intelligence and model evolu- tion. In this paper, inspired by neuroscience the- ories, we theoretically derive the increased in- formation boundary via learning cross doosin collective intelligence and propose a Synaptic EVOlutional spatiotemporal network, SynEVO, where SynEVO breaks the model independence and enables cross-domain knowledge to be shared and aggregated. Specifically, we first re-order the sample groups to imitate the human curriculum learning, and devise two complementary learners, clastic common container and -ekeindeeenndnt extractor to allow model growth and task wise commonality and personality disentanglement. Then adaptive dynamic coupler with new difference metric determines whether the new sample group should be incorporated into com- mon container to achieve model evolution un- der various domains. Experiments show that SynEVO improves the generalization capacity by at most 42% under cross-domain scenarios and SynEVO provides paradigm of NeuroAl for knowledge transfer and adaptation. Code avail- able at
Spatiotemporal learning aims to predict future urban evo- lution, which facilitates urban management and socioeco- nomic planning. Recently, diverse spatiotemporal forecast- ing solutions have well resolved data sparsity (Zhou et al., 2020), temporal shifts (Zhou et al., 2023), as well as unseen area inferences (Feng et al., 2024). As usual practices, al- most all spatiotemporal learners train independent models from specific sources where both models and data are iso- lated. With the growing of available urban sensors and the diversity of data sources under the rapid urban expansion, the task-specific data-driven learning is inevitably faced with the increased costs of repetitive model designs and computational resources. To this end, an evolvable data- adaptive learner that accommodates cross-domain transfer- ability and adaptivity highly required to facilitate sus- tainable urban computing. There have been a number of efforts that try improve generalization of spatiotemporal learning, where it can be classified as two folds, i.e., coun- tering shifts on the same source domain and across different source domains. On the same source, the initial step is the continuous spatiotemporal learning implemented with experience reply (Chen et al., 2021), and spatiotemporal out- of-distribution issue (OOD) is raised by capturing causal in- variance for confronting temporal shifts (Zhou et al., 2023). After that, a series of models take environments as indicators to guide generalization (Xia et al., 2024; Wang et al., 2024a; Yuan et al., 2023). However, even for different kinds of sources a same system, these independent models fail to share common information for task transfer across domains. For generalization across sources, prompt-empowered universal model for adapting various data sources (Yuan et al., 2024), and a task-level continuous spatiotemporal learner, which actively capture the stable commonality and fine-tune with individual personality for new tasks (Yi et al., 2024) are proposed. Even so, these models still suffer three critical issues for dross-domain transfer and data adaptive model evolution. 1) No theoretical guarantee for collective intelligence facilitating cross-domain transfer. 2) Not all tasks share common patterns, thus uniformly involving all tasks inevitably introduces noise. 3) These models are not elastic to actively evolve when data distribution changes.
'nniversity of Seience and Technology China (USTC), Hefei, China Suzhou Institute for Advanced Research, USTC, Suzhou, China State Key Laboratory of Resources and En- vironmental Information System, Beijing, China. Correspon- dence to: Zhengyang Zhou <zzy0929 w udtc.ed,.en.>, Yang Wang <angyan uust..ed..c.>.
Proceedings of the 42nd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

---

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
Fortunately, with the progress of Neuro-Artificial Intelli- gence (NeuroAD), neural networks are designed to imitate knowledge acquiring for model generalization and coopera- tion (Wang et al., 2022; 2024b), such as neuro- aurooinspieee con- tinuous learning (Wang et al., 2023b)and complementary- based (Kumaran et al., 2016) neural architecture. Consid- ering the similarity between the cross-domain transfer and the way human acquire new skills from prior knowledge, NeuroAI holds great potential to overcome effective eross- domain knowledge transfer. However, given various learn- ing behaviors in human brain, how to couple neuroscience with spatiotemporal network tailored for effective transfer and evolution is still challenging as following aspects, 1) As the collective intelligence cannot be accomplished in an action, how to progressively learn tasks from different domains from easy to difficult, 2) How to imitate human learning process to disentangle commonality and ensure common container elastic with continually receiving infor- mation. 3) How to ensure the common-individue models to quickly accessible for few-shot generalization.
increased generalization capacity with cross-domain intelligence and the facilitated task convergence from progressive learning, and a novel NeuroAl framework is proposed to tackle the cross doos-- challenge and empower model evolution.
By human-machine analogy, we introduce an ST- synapse, and couple curriculum and complementary learning with synapse to realize the progressive learn- ing and commonality disentanglement. The model evo- lution is achieved by elastic common container grow- ing and adaptive sample incorporation.
Extensive experiments show the collective intelligence increases the model generalization capacity under both source and temporal shifts by at 0.5% to 42%, includ- ing few-shot and zero-shot transfer, and empirically validate the convergency of progressive curriculum learning. The extremely reduced memory cost, i.e. only 21.75% memory cost against SOTA on iterative model training and evolution advances urban compul- ing towards sustainable computing paradigm.
Actually, classical neuroscience theories and new advance- ment reveal that, 1) synapse is an important structure con- necting neurons, which takes the role of message sharing and cooperation Van de Ven et al., 2020; Kennedy, 2016; Benfenati, 2007). 2) neuron and synapse are activated by neurotransmitters and action potentials, which is analogous to gradients in artificial neural networks (ANNs), where larger gradient intensity implies larger inconsistency be- tween solidified knowledge and new information (Gulledge al., 2005; Hussain & Al Alili, 2017; Zenke al., 2017). Inspired by observations, we theoretically analyze data- driven knowledge space within neural network can be in- creased with commonality via information entropy. To this end, we propose a Synaptic EVOlutional spatiotemporal network, SynEVO, to enable easy cross-domain transfer. Our SynEVO couples three neuro-inspired structures. First, to learn the tasks progressively, we devise a curriculum- guided sample group re-ordering to monotone increasing learning difficulty via task-level gradient relations. Sec- ond, to disentangle cross-domain commonality, we borrow the cerebral neocortex and hippocampus structures in brain, and design complementary dual learners, an Elastic Com- mon Container with growing capacity as the core synaptic function to receive cross-domain information for elastically collecting new regularities, and a Task independent Person- ality Extractor to characterize individual task features for adaptation. Finally, an adaptive dynamic coupler with dif- ference metric is devised to identify whether the new sample group should be incorporated into common container for enabling model evolution and increasing collective intelli- gence, which reduces the pollution of inappropriate data. The contributions are fhree-fold.
2. Related Works
Spatiotemporal learning and its generalization capacity. Spatiotemporal learning has been investigated enable con- venient urban life (Zhang et al., 2017; 2020; Ye et al., 2019; Zhou et al., 2020; Wu et al., 2019; Liu et al., 2024; 2025a;b; Miao et al., 2024; 2025). However, with urban expansion and economic development, it increases the concerns of distribution shifts as all previous learning models assume independent identical distribution. CauSTG devises causal spatiotemporal learning to explicitly address the temporal domain shifts (Zhou et al., 2023). Concurrently, continu- ous spatiotemporal learning becomes a prevalent topic for model update to counter temporal shifts (Wang et al., 2023a; Chen et al., 2021). Although prosperity, researchers find that data from different sources in a same system tend to share some common patterns. Some pioneering literature exploits unified multi-task spatiotemporal learning to ac- commodate diverse modalities where a prompt-empowered universal model is proposed (Yuan et al., 2024), while Yi, et,al. investigates the task-level continuous learning to it- eratively capture the common and individual patterns (Yi et al., 2024). Even though, these models still ignore three important issues. 1) Fail to unify the source domain and temporal domain for transfer in a same learning architecture where commonality and individuality are well-decoupled. 2) In same system, how to quantify relations among tasks and filtering the related ones to facilitate commonality is not clear. 3) Previous models fail to evolve with distri- bution changes to realize efficient cross-domain few-shot generalization. In contrast, we overcome the cross-domain adaptation through lens of Ne by imitating human
Inspired by neuroscience, we theoretically analyze the
2

---

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
acquiring new knowledge and skills, to achieve evolutional spatiotemporal network.
neuro-inspired cross-domain spatiotemporal learning model as an evolution model M, i.e.,
Neuro-inspired Artificial Intelligence (NeuroAI). Neu- roAI is an emerging research topic which designs ANNs from the inspiration of neuroscience and the way how hu-
Y ( X2 0M)
(I)
where f. denotes the learnall parameters of model M. When the data from new domain Xk+1 comes, we aim to quickly adapt M to M', i.e.,
man acquire new knowledge (Luk & Christodoulou, 2024). ANN is originated from the biological neurons and re- searchers teach machine learners learn like humans (Lind- say, 2020; Zhang& & Zhang, 2018). The literature can be classified as two folds from macroscopic to micro-structures In macro perspective, human brain usually progressively acquires the knowledge from easy to difficult, which inspires curriculum learning in machine intelligence (Wang et al., 2021; Blessing et al., 2024), and complementary learning scheme with hippocampus and neocortex structures (Ku- maran et al., 2016; O'Reilly et al., 2014; Arani et al., 2022). To provide feedback on Al model, reinforcement learning is proposed, and reinforcement learning from human feed- back (RLHF) is incorporated into LLMs for thinking like human (Lee et al., 2023). On the micro aspect, brain neu- ron is activated by neurotransmitters and action potentials with threshold for activation, where the message passing occurs when there is large potential difference (Zhang & Zhang, 2018). Analogously, gradients in ANN are similar to potential difference in brain neurons, and gradient can be viewed as the knowledge gap between new data and trained models, thus gradients can be exploited to interpret the relation between model and sample groups. Synapse is also an essential structure for bridging the message between neurons, where pioneering researches have demonstrated the potential of knowledge transfer by imitating synapse structure (Zenke et al., 2017; Hussain & Al Alili, 2017). Actually, unveiling the relations between brain structures and Al model transfer mechanism can advance the model evolution. Despite prosperity, on cross-domain transfer in spatiotemporal learning, how to investigate the specific mechanism that adapting to brain learning on transfer and generalization is still under-explored.
M' + M(X 0M:)
(2)
where O denotes learnable parameters of updated M'.
Tasporal denall ganupe
C Curriculum- inspir ed sumple groups re-ordering
suggle poupe
Gradbent comperation
supple groupe
Sample Grosps Re-arder bng
Sample groups Te ooqurzzation
+
Task doesals sampli groups
Complemettrry dual
Information
Elatee Commino collaiee
perseddy attrcbe
New demall
extrackrr
Jal de wlatter
Gradect Adaptive dynamk compler
Dinariic
SaaEO
Eilliiee
Figure I. Framework Overview of SyaEVO
Proposition 3.1. Increased information with cross-domain
learning. Given spatiotemporal data observations from dif- ferent sources {X1, X2, Xx...,, and then there must be shared commonality among domain data patterns, i.e.
(3)
then the well-learned information from the cross- coos-domain learning model M is increased by continually receiving domain knowledge, i.e.
Info(M(XI,,.... Xx; 0M)) > Info(M(X).... Xx-1;0m))
xx))00)) >
... > Info(M((x:0M))
3. Preliminaries
(4)
Spatiotemporal Cross-Domaie Observations. In an ur- ban system, data can be collected from different sources. We can model diverse spatiotemporal learning tasks as spatial-temporal graph prediction, and the deterministic ob- servations can be defined as (z)),. which is an element in X x= (X,, X2,..., X E NXTXC The (a²), indicates the value on graph node j at timestamp from e-th data source, where C represents the number of sources. As the data distribution can be shifted across temporal steps, then the task domain can be classified into both different tempo- ral domains with changed distribution and source domains.
where Info is the information encapsulated in M.
The Prop.3. 1 delivers that cross-domain learning with dif- ferent data sources 'in harmony with diversity' can increase the learned information in model M and it can be proved in Appendix. A.
4. Methodology
4.1. Framework Overview
SynEVO constructs a neural synaptic spatiotemporal net- work to share and transfer cross-domain knowledge for
Neuro-Inspired Cross-Domain Learning.
We define
3

---

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
generalization and adaptation. As illustrated in Fig. 1, our synaptic neural structure consists of three components, curriculum-inppiree sample group re-ordering to determine the learning order of sequential samples from easy to diffi- cult, complementary dual common-individual learners in- cluding an Elastic Common Container and lask-independent personality extractor to disentangle the task commonal- ity and personality and an adaptive dynamic coupler to aggregate the dual learners and preserve the individuality of tasks. It is noted that the Spatiotemporal learner backbone is implemented by Graph- WaveNet (GWN) (Wu et al., 2019).
consistency between data and model by,
cate- (empand) (expand)
(expand)]
(6)
n
where V (eapand) denotes the expanded tensor of V, and
denotes the concatenation of tensors.
With obtaining all cat, for input data X.. we identify the minimum value among them by min = arg minsume. which is considered as the compared bench. Next, to re- order other sample groups, we compute the vector difference between cat. and the bench one calmin by,
to adapt shared patterns
de = cate 2 catmin
4.2. Curriculum Guided Task Reordering
where e is the element- minus for vectors. After that, we can reorder the input sample groups X2..... . based on the length of de, i.e., I(dz) in ascending order and get the ordered sequence = (X where k is the number of sample groups. Therefore, we can capture the inner relation between sample groups by gradients, which avoids the isolation of information, and allows the learning process from easy to difficult.
Learning from easy to difficult is common practice for human acquiring knowledge and skills, which is named as curriculum learning (Bengio et al., 2009), e.g., the teach- ing process in our class also follows inculcating knowledge from basic to improved ones. To capture the commonality of data patterns in different domains, we propose curriculum guided task re-ordering. From optimization perspective, directly confronting complex task can lead the model to be caught in poor local optimums with exploding gradients. In contrast, by starting from a simple task and gradually increasing the difficulty, the model can be effectively guided to converge in the direction of global optimum, which en- ables better exploration in parameter space.
4.3. Complementary Dual Common-individual Learners
Inspired by complementary functions in brain memory, we construct a dual common individual learners to respectively accommodate two major knowledge based on three insights, 1) Complementary memory where neocortex remembers long-term and stable skills while hippocampus acquires new and quick knowledge. 2) More neurons are activated with knowledge increasing. 3) Distinguished patterns makes long-standing memory. Overall, our design inherits the com- plementary learning scheme into respective common con- tainer and personality pattern extractor. The common con- tainer is devised accounting for the core synaptic function to receive cross- doss-nd common information with elastically increasing collective intelligence, and task independent personality extractor is to characterize individual task fea- tures for quick adaptation. They cooperate with each other for generalized cross-domain learning.
Gradients can characterize the consistency degree between training model and new sample groups, thus the gradients are exploited to indicate the difficulty of adapting models to samples. We then exploit the gradient to compute the adaptation between new feeding samples and the training model, and determine the learning order via imitating the curriculum learning process. Specifically, we apply the backward of loss to compute the gradient. For each input sample group X, from c-th domain, we initial a trainable model (X (..)) and train Me until the loss function of it converges. Then we backward the final loss to com- pute the gradients of 0. as { 1 2...... Vn). where V; denotes the gradient of the i-th layer of HM and n denotes the the number of the layers of 0y-- After that, compute the sum of squares of the gradients by,
4.3.1. ELASTIC COMMON CONTAINER
Deep learning models iteratively trained with new samples can automatically fuse patterns across all samples. Based on above analysis, the commonality should be expanded when the acquired knowledge is increasing with iteratively feed- ing into new samples. With well-organized task sequences, we are expected to mimic such knowledge expansion in brains for neural networks. In detail, we borrow a couple of simple yet effective strategies in deep learning to empower the model with elastic property. Dropout and L2 Regular- ization with weight decay control the overall complexity of model that potentially avoid over over-fitting by the number of
sumc
(5)
where | .12 denotes the square of the L.2 norm of V.
Then, we concatenate the gradient to denote the overall
As observations in source and temporal domains are organized into sample groups, which can he viewed as various tasks, here interchangeahly utilize sample group and tasks our main text.
4

---

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
active neurons. For Dropout, every neuron can be zero (dropout) with probability p and each weight decay coefficient weight À for L2 controls the importance of L2 item. The smaller probability p and smaller weight decay X, the model is more active. Despite promising, how to quantitatively control the activeness of neurons by prob- ability p and weight decay À still unclear. address
The larger number of non-zero elements in [A] indicates more model activeness and capture more information with decreased probability p.
With above Lemma. 4.1, we further model the dropout factor Pe as,
Pe(de) po(1 e((d))
-o((-elld)ddmma (0 < Po S 1)
(10)
Dermesad wright
where Pe denotes the dropout factor for domain c, Po is a hyperparameter, d, denotes the vector difference for domain с against catmin and dmaw is the maximum length of all gradient vectors de.
Drogout p
Group 1
Group
snte Decreased (2.1)
Expardad brann container
Information Incressing and continee repansion Gasup
Similarly, for the weight decay coefficient of 1.2 regulariza- tion that controls the model in the optimizer, we can re- re-write the dynamic update of weight decay according the variation of gradient as below,
Group
Group
Group 2
Collective Intelligence expanded brain
Croup
Groap
Ae (d.) do -
( ((de) ((de)-dmar dmaa )(0 < do < 1)
(1I)
Figure 2. The process of elastic growth of common container
In Eq. 11, de decreases as l(dz) increases, which realize the elastic growth of the model and improves the generalization of the model. To this end, we consider that controlling the dropout value Pe and weight decay coefficient A with the vector de can efficiently realize the gradual release of model activeness, which has been shown in Fig. 2. With synapse structure, common patterns are iteratively enhanced. As new domain arrives, the overall knowledge boundary and learned parameter space of our common container are expanding. Then can do quick and light-weight adaptation from the existing knowledge space to new domain.
such quantitation challenge, introduce Lemma. 4.1 from neuroscience (Gulledge et al., 2005).
Lemma 4.1. The probability of presynaptic neuro- transmitter release can be described by a propagation model( Bertram et al., 1996; Schneggenburger & Neher, 2000).
P, Po
where P, denotes the probability of neurotransmitter release, Po denotes the basic release probability, is the successive activeness difference between a pre-synaptic neuron and after-synaptic neuron.
4.3.2. TASK INDEPENDENT PERSONALITY EXTRACTOR
Besides commonality, the personalized pattern of each task especially new task is also vital for domain adaptation. For- mally, it is expected to derive an additional personality ex- tractor which transforms the input X. to the output E., i.c., E. = g(Xe: Wg) = Xy Here, we define new criterion D B) to measure the difference between ten- sors A and B, i.e.,
Lemma. 4.1 suggests that the neurotransmitter can induce the activeness neurons and we can exploit the such elec- tric potential difference to mimic the process. Fortunately, earlier we have discussed that gradient sistency between model and new samples, thus we take the second-order difference, the successive gradient variation [d] based on Eq. 7 as the propagation degree T.
indicate the con-
For dropout, we first define the matrix of all learnable param- eter in model M as M, and provide the following definition of activated model parameters.
=
(12)
Inspired by distinguishing patterns in memory for activat- ing the neuron activeness, we explore the contrastive learn- ing (Khosla et al., 2020; Hadsell al., 2006) to implement such personality extractor,
Definition 4.2. Activated model parameters. For parameter matrix [ its activeness matrix is defined as,
0 if [M]a is dropped out, otherwise.
R(E,, E;; Wg) = -DD(E, E,))
(9)
(13)
+(1 f)mar(( - D(E,, E2))
where [A] AA
*M denotes the element of matrix [A],
where R(E,, E2) refers to contrastive objective function between two representations E,, E,. The domain indicator
[M] on position (x. y) respectively. Based on the activeness matrix, the activated model parameters can be updated as,
ÿ = if X, and X, come from the same domain while ÿ = indicates two samples from different domains, and m
M MoA. A, where o is the Hadamard product.
5

---

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
controls the minimum distance between X; and X from different domains. Since the personality extractor 9 updates to approach the minimization objective, the representations E; and E from the same domain become closer and the representations E, and E, from different domains are away from each other.
L.2 norm of O.r and init denotes the new random initial learnable parameters of M'
5. Experiment
5.1. Datasets
Therefore, the commonality and personality can be disen- tangled with the contrastive learning objective. As samples of a new domain comes, the personality extractor is ca- pable of extracting the representation of the new domain,
We collect and process four datasets for our experiments:
1) NYC (New YorkCity, 2016); Include three months of traffic data consisting of four source domains which are CrowdIn, CrowdOut, TaxiPick and TaxiDrop collected from Manhattan in New York City. 2) CHI (CHICAGO, 2023): Consist of three source domains of traffic status, which are Risk, TaxiPick and TaxiDrop collected in the second half of 2023 from Chicago. 3) SIP: Includes three months of traffic data consisting of two source domains which are Flow and Speed collected from Suzhou Industrial Park. 4) SD (Liu et al., 2023): Include traffic flow data collected from San Diego in 2019.
i.c.. E. = gXxe; Wg). and the commonality patterns are learned by the common container for commonality growth, which significantly enhances the ability of the model to comprehend both previous and new knowledge.
4.4. Adaptive Dynamic Coupler
To achieve cross-domain adaptation while maintaining both personality and commonality, we construct an adaptive dy- namic coupler to aggregate the commonality and personality. When the sample group from new domain X comes, the task independent personality extractor first extracts the representation of X by E g(X++1; Wg). Then we preserve a list G which contains the distance between the representation of the new domain and the trained do- mains based on Eq. 12. To be specific, G is defined as, G G D2.... ..... D where k is the number of trained domains in the elastic common container and D; denotes D(E++1, E.). Let Dmin be the minimum D, in G and if 0 < Dmin < K. (where K is a threshold which is hyperpa- rameter), it indicates that the new domain shares potential commonality with the trained domains. Then we can put it into the common container to compute the gradient and dynamically adjust the dropout factor Pk+1 and the weight decay coefficient A At last, the common container trains the new domain based upon the adjusted factors and thus absorb the knowledge from the new domain to realize elastic growth. If minn 2 K, which indicates that the new domain almost shares no commonality with trained domains, then we re- re-instantiate personality extractor by initializing learn- able parameters with previous extractor for quick adapta- tion. The comparison of representations is implemented by gate structure h,
5.2. Evaluation Metries and Baselines
We apply three evaluation metrics in our experiments, which are mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). We exploit seven prevalent baselines for evaluations, includ- ing STGNNs (STGCN (Yu et al., 2017), STGODE (Fang et al., 2021), GWN (Wu et al., 2019)). RNN-based models (AGCRN (Bai et al., 2020)) and attention- -baed models (STTN (Xu et al., 2020). ASTGCN (Guo et al., 2019). CMuST (Yi et al., 2024)).
5.3. Implementation Details
We split the datasets into training, validation and testing sets with the ratio of 7:1:2. Datasets NYC, CHI and SIP are utilized for validation on cross-source and cross-temporal domain tasks, while SD with one attribute but large time span is for tross-temporal domain evaluation. For the first three datasets, on cross-domain evaluation, we leave Tax- iPick, TaxiDrop and Speed as the evaluation domain on respective NYC/CHI/SIP sets, and let other data sources to be trained iteratively. For their cross-temporal domain evaluation, we divide one day into four equal periods, and leave the last period of a day on all source domains for eval- uation. Regarding cross-temporal domain validation on SD, we divide one day into six equal periods and leave the last period for evaluation of temporal domain adaptation. We run each baseline three times and report the averaged results to reduce the influence of randomness issue. For curriculum- guided task reordering, Adam optimizer (Kingma, 2014) is applied with initialized learning rate of 0.01 and weight decay of 0.001 for the initial learnable model M For complementary dual learners, we use the mean square error (MSE) as the criterion D of the personality extractor. For
h(D h(Dmonn Rt) = {1 if 0 < Dmin < K
(14)
otherwise.
To conclude, the overall learning objective of our model M
to the input X can be defined as,
Loss(O = h(Dmon, (M(L(M((XXE)))) Mmi O,r), Ys+1)
+ +d++1 + 0m)
+(1 - (11-h(DDmaee (DDmimn (MM Omni 0,r), Y241)
(15)
where Ys+1 denotes the target value of M'(X++)) ARe1
computed based on Eq. 10, |0 [1 denotes the square of
6

---

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
Table Performance comparison on cross-source adaptation NYC, CHI, STP and cross-temporal adaptation on SD. Best results are bold and the second best results are underlined.
METHODS
MAE
RMSE
МАРЕ
MAE
RMSE
MАPE
MAE
RMSE
МАРE
MAE
RMSE
MAPE
STGCN
6.774
15.853
0.374
1.518
2.804
0.415
0.753
1.492
0.219
12.477
22. 22.055
0.182
STGODE
22.555
0.481
1.543
2.792
0.433
0.732
12,300
10.263
24.335
0.545
1.520
2.873
0.421
0.737
1.47.
0.212
18.890
29.220
STTN
7.962
19.544
0.435
1.494
2.699
0.409
0.732
1.461
0.212
13.092
22. 05054
0.183
AGCRN
8,254
19.301
0.485
1.543
2.805
0.427
0.745
1.469
0.215
12.225
22. 09.
0.179
ASTGEN
25.070
0.519
2.809
13:079
CMuST
6.176
14.954
0.459
1498
2.781
0.388
0.737
1.497
0.219
10.940
0.162
SYNEVO
6.494
14.585
0.358
1.486
2.753
0.361
0.697
1.390
0.205
10.994
18.654
0.157
Table 2. GPU cost comparison between CMuST and SynEVO
Table 3. Detailed comparison on cross-temporal adaptation and cross-source adaptation on NYC, CHI and SIP (cross-source results are from Tab.
METiinn
GPU COST
NYC
CHI
SIP
SD
NYC
CHI
CMUST
4034MB
411SMB
2450MB
19533MB
SYSEVO
2170MB
2210MH
2044MB
4252MB
TEMPORAL
SYNEVO
6.278
1.457
0.683
CMUST MAE
6.457
1,422
0.716
SOURCE
SYNEVO MAR
6.494
1.486
0.697
CMuST MAR
6.576
1.498
0.737
Elastic Common Container, the loss criterion is adopted with widely-used MaskedMAELoss. We run STGODE, STTN, CMuST on SD on NVIDIA A A100-PCIE-40GB and other experiments on Tesla V100-PCIE-106 16GB by adapting the model scale with GPU versions.
less computing resource. 2) Detailed comparison on cross- temporal domain adaptation and cross-source domain adaptation against SOTA. We evaluate the performance of cross-temporal domain adaptation and cross-source do- main adaptation of SynEVO on NYC, CHI and SIP against selected best baseline CMuST, as shown in Tab. 3. Obvi- ously, our SynEVO can outperform CMuST on almost three datasets on MAE, and the improvement on source-domain is more significant than temporal domain, which empiri- cally verifies our NeuroAI-based synapse solution captures the complex commonality across domains and expand the boundary of learning space.
5.4. Performance Comparison
1) Comparison among baselines. Comparison results can be found in Tab. 1, which reports results of cross-source domain adaptation on the NYC, CHI, SIP and results of cross-temporal domain adaptation on the SD dataset. In general, our SynEVO model outperforms other baselines across most metrics on four datasets. Compared with those without a commonality extraction mechanism, the overall adaptation performance of CMuST and SynEVO with com- monality significantly outperforms them, where SynEVO outperforms other baselines except CMuST on average by about 25.0% .0% on NYC, 2.6% on CHI, 5.8% on on SIP and 18.3% on SD. These results indicate the effectiveness of our elastic common container, which allows dynamically grow in appropriate area and capture the inner relation of different domains. At the same time, our SynEVO outperforms the vanilla backbone GWN by about 35.6% on average of four datasets. It's worth noting that on NYC and SD, GWN seems to be trapped in a local opti- mum, and it further demonstrates that the adaptive structure enhances the model's transfer and adaptation. Although attention based model CMuST achieves satisfactory per- formance, CMuST exactly costs much more computing re- source than our SynEVO as shown in Tab. 2, especially the 4.59 times of computation cost of SynEVO on large dataset SD. It shows that the the NeuroAl structure can enable light- weight adaptation which ensures superior performance with
5.5. Ablation Study
In order to uncover the significance each module to the success of SynEVO, we perform an ablation study on cross- temporal domain adaptation via removing each module on the four datasets. The ablated variants are follows. 1) SynEVO-REO Remove the module of curriculum-guica sample group reordering. 2) SynEVO-EL. VO-Ela: Remove the dynamic adjustment of dropout factor p and weight decay coefficient with a static value, e.g. = 0.1, X = 0.001. 3) SynE Without relation comparison on individual and commonality, any domain even unrelated sample groups can be fed into the model without judgment gate.
model to
Tab.4 shows the results of ablation studies. In general, re- moving abovementioned modules leads to the consistent performance drops. When removing the module of elas- tic growth, the performance drops the most by 44.2% on MAE, indicating the most important structure of elastic com-
7

---

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
Table 4. Ablation studies of SynEVO doing cross-temporal domain adaptation on four datasets
NYC
CHI
SIP
SD
METHODS
MAE
RMSE
МАРE
MAE
RMSE
MAPE
MAE
RMSE
МАРE
MAE
RMSE
MAPE
SYSEVO
6.278
2.596
SYNEVO-REO
7,032
17.228
0.383
1.785
3.210
0.495
0.712
1,400
0.214
14,604
22.665
0.166
SYAEVO-ELA
8.384
19.142
0.386
1.907
3.414
0.471
0.745
1.418
0.223
17.481
27.204
0.245
SYNEVO-PE
7.451
0.382
3.098
0.479
0.732
1.444
24.276
mon container for model evolution and adaptation. More specifically, when removing sample re-ordering, the perfor- mance drops by about 18. %c, while personality extractor is discarded, the performance drops by about 19.1%, which shows the personality and re-instantiate mechanism is also critical for ensuring model robustness and consistency.
crda kii
COORO Cenarnatin Cros
cyco loa
Common container
5.6. Detailed Analysis
Uncovered sample group sequences for curriculum learn- ing, In curriculum guided task reordering, the reordered the input group samples are illustrated in Fig. 3(a) based on the gradients. In our experiments, we find that domains from the same source not necessarily next to each other in the ordered sequence S, which verifies that SynEVO has successfully uncovered hidden correlation information between domains. Moreover, it is demonstrated that reorder- ing our input sample groups can learn certain commonal- ity, empirically providing evidence for the effectiveness of cross-domain learning.
spec)
46
30
(4) Chemee Airw leurnig with on 30155 SMACEE Sak CRI Subsot
@30 Loaning loss betourec coos tomperal docaal talk dD Grazd
Figure 3. Training order CHI and training loss behavior SD
Table S. Comparison of zero-shot cross-temporal domain adapta- tion performance
NYC
CHI
SIP
SD
1.995
16 981
SYNEVO
3.769
MAPE
0.668
0.369
0.217
0,214
Observed quick adaptation via loss behavior. In elastic common container, the sample groups are periodically fed into models. We let SynEVO elastically grow to absorb the commonality by iteratively feeding sample groups, and visualize the training loss of two consecutive learning cycles on SD Fig. 3b). The blue curve denotes the first training cycle while the red one denotes the second. These two cycles share the same training data and order. Every mutation in the curve represents the input of new domain. Obviously, the loss of the second cycle is much lower than the former one and the loss drops quickly after new domain input, which shows the quick adaptation by constructing learning tasks in cycled and elastic manner.
MAE
17.091
4.109
1.039
21 446
GWN
RMSE
40,802
8.385
2.056
30 736
MAPE
0.856
0.742
0.264
0.271
ments of 1) H2E: reordering tasks from hard to easy to emphasize the importance of training order. 2) SynEVO-IL: eliminating iterative learning for commonality, i.c., only training and testing ferent neural expansion rate Pe. e.g. Pe(de) Pu/l(de). Results are shown in Tab.6. The reversed order falls into inferior performances, which emphasizes the significance of reordering the tasks from easy to difficult our design. Moreover, hard-to-easy performances are better than ran- dom ordering of SynEVO-REO. which may be attributed to common relations between neighboring tasks as they are ordered even the reverse one. Based on the experimental results, we can conclude our commonality learner and the setting of are reasonable and empirically justified.
dataset. 3) DER: setting dif-
Effective zero-shot adaptation. To further evaluate the adaptation performance of SynEVO, conduct zero-shot cross-temporal domain adaptation, i.e., testing without train- ing, and make comparisons with the backbone GWN, where results are shown in Tab.5. Obviously, SynEVO outperforms better than GWN by averagely 29.8% on the four datasets, and numerically proves that SynEVO has captured the hid- den commonality of various input data so that it can achieve effective and superior performance zero-shot tasks.
5.7. Hy Hyperparameter Sensitivity Analysis
We varied Dropout Po from {0.1, 0.3, 0.5, 0.7, 1). weight decay coefficient do from {0.01, 0.03, 0.05, 0.07, 0.1}. and distance threshold x from {1 x 103, 10 10 1 x 10 108}. Results shown in Fig.4-Fig.7 indicate that the optimal settings are x 103 on all datasets, Po =
More empirical analysis on task reordering and design of commonality extraction. We supplement the experi-
8

---

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
Table 6. More empirical analysis on task reordering and design of commonality extraction
NYC
CHI
SIP
SD
METHODS
MAE
RMSE
МАРE
MAB
RMSE
MAPE
MAE
RMSE
MАРE
MAE
RMSE
MAPE
2.217
0:705
SYNEVO-IL
8.201
19.090
0.423
1.554
2.887
0.385
0.711
1.412
0.216
13.128
20.890
0.220
DER
7.213
16.310
0.422
1,550
2,765
0.367
0.705
1,399
0.207
11.944
19.599
0.168
0.5, Au 0.05 on NYC and SIP, Po = 1, 0.1 on CHI and Po = 0.7, Àg 0.07 on SD. For K, if the threshold is extremely small, it means no new domain is allowed in, which will violate the principle of hharmony with diversity for collective intelligence' then we can observe that with increasing and more relaxed condition for fusion, more noise will be introduced to reduce the performances. Thus the trade-off between commonality and individual feature extraction should be obtained during model design.
Impact Statement
Our work focuses on the research on the aggregation of data in different fields in cities, which has strong practical significance for the deployment of various data-driven urban applications and the improvement of the convenience of urban life. Although our work is inspired by Neuroscience, the theories and data we use are publicly available and do not collect information from any human, animal, or private individuals, so there are no ethical issues.
6. Conclusion
References
In this paper, we propose a novel NeuroAI framework SynEVO enable cross-domain spatiotemporal learning for few-shot domain adaptation. From neuroscience theories, ourriculum-guider sample group re-ordering, couple of complementary dual learners which includes an elastic common container, and a task independent personality ex- tractor are proposed to capture commonality in an elastic manner. The adaptive dynamic coupler determines whether the new feeding samples can be aggregated into SynEVO for achieving model evolution. Extensive experiments both cross-source and cross-temporal domains validate the 0.5% to 42% improvements against baselines. For future work, we plan to mine the more inner mechanism of human brain to facilitate the generalization of general AI models. Moreover, our model can be generally nested within other neural networks. In other areas, it is applicable utilize our evolvable 'data-model' collaboration to decouple the invariant and variable patterns, and reconstruct the OOD distribution with new patterns.
Arani, E., Sarfraz, F., and Zonooz, B. Learning fast, learning slow: A general continual learning method based on complementary learning system. arXiv preprint 2rX1::2201.1266 2022.
Bai, I.,, Yao, L.,, Li, C., Wang, X., and Wang, C. Adaptive graph convolutional recurrent network for traffic forecast- ing. Advances in neural information processing systems, 15-17844-78155 2020.
Benfenati, Synaptic plasticity and the neurobiology of learning and memory. Acta Biomed, 78(Suppl 1):58-66, 2007.
Bengio, Y., Louradour, J., Collobert, R., and Weston, J. Curriculum learning. In Proceedings of the 26th annual international conference on machine learning, pp. 41-48, 2009.
Bertram, R., Sherman, A., and Stanley, E. F. Single- domain/boune calcium hypothesis of transmitter release and facilitation. Journal of Neurophysiology. 75(5):1919- 1931, 1996.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 12227901), Natural Science Foun- dation of Jiangsu Province (BK. 20244000, BK.20240461) the grant from State Key Laboratory of Resources and En- vironmental Information System. The Al-driven experi- ments, simulations and model training were performed on the robotic Al-Scientist platform of Chinese Academy of Science.
Blessing, D., Celik, O., Jia, X., Reuss, M., Li, M., Lioutikov, R., and Neumann, G. Information maximizing curricu- lum: A aurrculuu--asse approach for learning versatile skills. Advances in Neural Information Processing Sys- tems, 36, 2024.
Chen, X., Wang, J., and Xie, K. Trafficstream: A stream- ing traffic flow forecasting framework based on graph neural networks and continual learning. arXiv preprint arXiv:2106.06273. 2021.

---

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
CHICAGO. Chi dataset. Website, 2023. https://datas cityofchicago. http:/orgg////eeeeooeoo
Liu, C., Yang, S., Xu, Q. Li, Z., Long, C.,, Li, Z., and Zhao, R. Sparial-temporal large language model for traffic prediction. In 25th IEEE International Conference Mobile Data Management, pp. 31-40, 2024.
Fang, Z., Long, Q., Song. G., and Xie, K. Spatial-temporal graph ODE networks for traffic flow forecasting. CoRR, abs/2106.12931, 2021. URL 0tt/22006.1993)
Liu, C.,, Miao, H., Xu, Q. Zhou, S., Long, C., Zhao, Y., Li, Z., and Zhao, R. Efficient multivariate time series forecasting via calibrated language models with privi- leged knowledge distillation. In 41th IEEE International Conference on Data Engineering. 2025a.
Feng, Y., Wang, Q., Xia, Y., Huang, J., Zhong, S., Wang, K., Cheng, S., and Liang, Y. Spatio-temporal field neu- ral networks for air quality inference. arXiv preprint vrXiv:2403.02354 2024.
Liu, C., Xu, Q. Miao, H., Yang, S., Zhang, I.., Long, C., Li, Z., and Zhao, R. TimeCMA: Towards Ilm-empowered multivariate time series forecasting via cross- moos-mto alignment. Thirty-Nineth AAAI Conference on Artifi- cial Intelligence 2025b.
Gulledge, A. T., Kampa, B. M., and Stuart, G. J. Synaptic integration in dendritic trees. Journal of neurobiology, 64 (1):75-90, 2005.
Guo, S., Lin, Y., Feng, N., Song, C., and Wan, H. Attention based spatial-tempores graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI con- ference on artificial intelligence, volume 33, pp. 922-929, 2019.
Liu, X., Xia, Y., Liang, Y., Hu, J., Wang, Y., Bai, L., Huang, C., Liu, Z. Hooi, B., and Zimmermann, R. Largest: A benchmark dataset for large-scale traffic forecasting. In Advances in Neural Information Processing Systems, 2023.
Hadsell, R., Chopra, S., and LeCun, Y. Dimensionality reduction by learning an invariant mapping. In 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR'06) volume 2, pp. 1735- 1742. IEEE, 2006.
Luk, G. and Christodoulou, J. Cognitive neuroscience and education. Handbook of educational psychology, pp. 383 404. Routledge, 2024.
Miao, H., Liu, Z., Zhao, Y., Guo, C., Yang, B., Zheng, K., and Jensen, C. S. Less is more: Efficient time se- ries dataset condensation via two-fold modal matching. PVLDB, 18(2):226-238, 2024.
Hussain, S. and Al Alili, A pruning approach to optimize synaptic connections and select relevant input parameters for neural network modelling of solar radiation. Applied Soft Computing, 52:898-908 2017.
Miao, H., Xu, R., Zhao, Y., Wang, S., Wang. J., Yu, P. S.. and Jensen, C. S. parameter-efficien federated frame- work for streaming time series anomaly detection via lightweight adaptation. TMC, 2025.
Kennedy, M. B. Synaptic signaling in learning and mem- ory. Cold Spring Harbor perspectives in biology, 8(2): a016824, 2016.
Khosla, P., Teterwak, P., Wang, C., Samna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., and Krishnan, D. Supervised contrastive learning. CoRR, abs/2004.11362 2020. URL https: /ttp://aavvvv/.e http://wwwv....raaa.....e.. 220.../00044..666
New YorkCity. Nyc dataset.
Website, 2016.
attps://www http://wwww.......oooo http://wwwww..p/////ttt///////// tlc-trip- page.
O'Reilly, R. C., Bhattacharyya, R., Howard, M. D., and Ketz, N. Complementary learning systems. Cognitive 3cie (ieee 2014.
Kingma, D. P. Adam: A method for stochastic optimization. arXiv preprint arXiv: 1666:990,26 2014.
Kumaran, D., Hassabis, D., and McClelland, J. L. What learning systems do intelligent agents need? complemen- tary learning systems theory updated. Trends in cognitive sciences, 20(7):512-534, 2016.
Schneggenburger, R. and Neher, E. Intracellular calcium dependence of transmitter release rates at a fast central synapse. Nature, 406(6798):899-893 2000.
Lee, H., Phatale, S., Mansoor, H., Lu, K. R., Mesnard, T., Ferret, J., Bishop, C., Hall, E., Carbune, V., and Rastogi, A. Rlaif: Scaling reinforcement learning from human feedback with feedback. 2023.
Tishby, N., Pereira, C., and Bialek, W. The informa- tion bottleneck method. arXiv preprint physics/0004057, 2000.
Van de Ven, G. M., Siegelmann, H. T., and Tolias, A. S. Brain-inspired replay for continual learning with artificial neural networks. Nature communications, 11(1):4069, 2020.
Lindsay, G. W. Attention in psychology, neuroscience, and machine learning. Frontiers in computational neuro- science, 14:516985, 2020.
10

---

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
Wang, B., Zhang, Y., Wang, X., Wang, P., Zhou, Z., Bai, L.,, and Wang. Y. Pattern expansion and consolidation evolving graphs for continual traffic prediction. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2223-2232, 2023a.
Yu, B., Yin, H., and Zhu, Z. Spatio-temporal graph convo- lutional networks: A deep learning framework for traffic forecasting, arXiv preprint arXiv: 009704 2017.
Yuan, H., Sun, Q. Fu, X., Zhang, Z., Ji, C., Peng. H., and
Li, J. Environment-awee dynamic graph learning for out-of-distribution generalization. Advances in Neural Information Processing Systems, 36, 2023.
Wang. B., Ma, J., Wang, P., Wang, X., Zhang, Y. Zhou, Z., and Wang, Y. Stone: A spatio-temporal ood learn- ing framework kills both spatial and temporal shifts. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2948-2959, 2024a.
Yuan, Y., Ding, J., Feng, J., Jin, D., and Li, Y. Unist: A prompt-empowered universal model for urban spatio- temporal prediction. In Proceedings of the 30th ACM SIGKDD Conference Knowledge Discovery and Data 2024.
Mining, pp.
Wang, L.,, Zhang, X., Li, Q. Zhu, J., and Zhong, Y. Coscl: Cooperation of small continual learners is stronger than big one. In European Conference on Computer Vision, pp. 271. Springer, 2022.
Zenke, F., Poole, B., and Ganguli, S. Continual learning through synaptic intelligence. In International conference machine learning, pp. 3987-3995, PMLR, 2017.
Wang, L., Zhang, X., Li, Q., Zhang, M., Su, H., Zhu, J., and Zhong, Y. Incorporating neuro-inspired adaptability for continual learning in artificial intelligence. Nature Machine Intelligence, (112):356-1336 2023b.
Zhang, C., Zhu, F., Wang, X., Sun, L.,, Tang, H., and Lv, Y.
Taxi demand prediction using parallel multi-task learning model. IEEE Transactions on Intelligent Transportation Systems, 23(2):794 803, 2020.
Wang, L., Zhang, X., Su, H., and Zhu, A comprehen- sive survey of continual learning: theory, method and application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024b.
Zhang. J., Zheng, Y., and Qi, D. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proceedings of the AAAI conference on artificial intelli- gence, volume 31, 2017.
Wang, X., Chen, Y., and Zhu, W. A survey on curriculum learning. IEEE transactions pattern analysis and machine intelligence, 44(9):4555-4576 2021.
Zhang. Z. and Zhang, Z. Artificial neural network. Multi- variate time series analysis in climate and environmental research, pp. 1-35, 2018.
Wu, Z., Pan, S., Long. G., Jiang. J., and Zhang, C. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv: 1906:19000000 00121, 2019.
Zhou, Z., Wang, Y., Xie, X., Chen, L., and Liu, H. Riskora- cle: A minute-level citywide traffic accident forecasting framework. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pp. 1258-1265, 2020.
Xia, Y., Liang. Y., Wen, H., Liu, X., Wang, K., Zhou, Z.,, and Zimmermann, R. Deciphering spatio-temporal graph forecasting: A causal lens and treatment. Advances in Neural Information Processing Systems, 36, 2024.
Zhou, Z., Huang, Q., Yang, K., Wang, K., Wang, X., Zhang, Y.. Liang, Y., and Wang, Y. Maintaining the status quo: Capturing invariant relations for ood spatiotemporal learn- ing. In Proceedings of the 29th ACM SIGKDD Con- ference on Knowledge Discovery and Data Mining, pp. 3603-3614, 2023.
Xu, M., Dai, W., Liu, C., Gao, X., Lin, W., Qi, G.-J,, and Xiong, H. Spatial-temporal transformer networks for traffic flow forecasting, arXiv preprint urXiv:2001.02908 2020.
Ye, J., Sun, L., Du, B., Fu, Y., Tong, X., and Xiong, H. Co-prediction of multiple transportation demands based on deep ppatioltempora neural network. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 305-313, 2019.
Yi, 7., Zhou, Z., Huang, Q. Chen, Y., Yu, L., Wang, X., and Wang, Y. Get rid of isolation: A continuous multi-task spatio-temporal learning framework. In The Thirty-eightl Annual Conference on Neural Information Processing Systems, 2024.

---

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
In the Appendix, we provide the necessary proof our proposition and supplementary experiments for model evaluation.
A. Proof of Proposition 3.1
Cross-domain learning can increase the learned information.
Proposition A.1. Increased information with cross pros-ddomain learning. Given spatiotemporal data observations from different
sources 1x, X2, X3, .... Xi) and the there must share commonality among domain data patterns, i.e.,
Vi,j(1 si<j [X;
(16)
then the well-learned information from the cross pross-domain learning model M is increased by continually receiving domain knowledge, i.e..
Info(M(Xi,, Xk-1; > Info(M(Xi;0M))
(17)
where Info is the knowledge information encapsulated in model M.
Proof. Here we provide the proof from the perspective of information theory. We first define the mutual information / as,
I(x - (X H(Y (X))
(18)
H H(Y) H(X,Y)
where denotes the information entropy.
In our learning scheme, by denoting the input observations X and predicted output Y, we transfer the learning objective of MAE into the following equation via the theory of Information Bottleneck (Tishby et al., 2000),
L(X, Y, Z) [(x; Z) 31(Z; Y)
(19)
where 9 is hyperparameter controlling the balance between the mutual information 1(X, Z) and 1(2, Y). Minimizing [(x; Z) aims to reduce the information redundancy of input X while maximizing [(Z; Y) aims to make the representation Z keeps enough information of output Y.
Considering the relationship between information entropy and mutual information, we have,
T(X; Y) H(X) - H(X
(20)
we can transform above Eq.20 into,
Y(X = H(X) - I(X;Y)
(21)
Then, Vi(1 < k), we can compute H(X; X1, X 1; as,
H(X:Xx, X2.....
..... X H(x;) T(X;; X1, X2,..., X;-1)
(22)
Similarly,
H(X: x+1 X2, X,) = H(+++))++ - I(X :+1; X X2....,
(23)
Actually, for any two datasets, the initial learning uncertainties of corresponding datasets are equilibrated, and denoted as,
Vi,j(1 S ((( H(X,)
(24)
Then continue derivation into,
H(X,) H(X++)
(25)
Then we have assumed the involved datasets are sharing common patterns, with vi Sikj ()< can obtain,
0, we
I(X;; X X X < I(1+1: X1, X X,...., X,)
(26)
Therefore, based on Eq.22, Eq.23, E5, 26,26, we can conclude that,
X². H (X, X.-1) > H H(x++1|1, X2, .......2. X,)
(27)
which means the model is expanding the boundary of learning. Moreover, according to Eq.20, can further derive that, H XX) < H(X))
(28)
which means the commonality between input data contributes to reducing the uncertainty of the tasks.
12

---

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
72
304E
3152
70
14.3
300
15.0
37-
044
145
MAA skogge
MANP lagg
185
215-
44.0
265
6.4
160
15
Ogga
laps
141
$240)
15.
0.2
15.0
144
Figure 4. Hyperparameter sensitivity on NYC
B. Hyperparameter sensitivity visualization
To determine the best hyprraarameters of our SynEVO and support the completeness our experiments, we varied base Dropout factor Po in 10 from 10.1, 0.3, 0.5, 0.7, 1), base weight decay coefficient in Eq. from (0.01, 0.03, 0.05, 0.07, J. and distance threshold from {1 x 103, 1 x 10a, 1 x 10 1 10%3. The experimental results are displayed in Fig.4- 1) Fig.4 shows that on NYC, the performance of SynEVO firstly increases with the increase of po and Åo while drops when Po 2 0.5, Ao 2 0.05. This result demonstrates that on NYC, if po and Åo are too small, the model is initially too complex so that it fails to learn from easy to hard, thus trapped in local optimum. If Po and Ao are too large, the model will fail to be sufficiently evolved and cannot capture enough commonality of input data. 2) For the distance threshold K, if it's extremely small, it means no new domain is allowed into SynEVO, which will violate the principle of 'harmony with diversity for collective intelligence'. If too large, then much noise will be introduced reduce the performance of SynEVO, polluting the commonality of the trained data.
13

---

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
5440
1.5
135
135
1.52
2.3
04
05
0.4
10
34
12
MAS
6- 10VT
11522
1.50
3.1%
0.5
MAA
2475
1.1-
242
3.4
2.725
GM4
3.50
636
0.50
Figure Hyperparameter sensitivity on CHI
14

---

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
1444
130
640
2632
6000
1553
0400
8.
04
05
0.4
8.4
16
14
MAS
4- 10V7
11522
210
3.2
1.2
3.80
1.0
MAA
1.42
20.6
204-
1220
1.77
15.5
1350
154
0.4
3.50
6.6
1.50
Figure Hyperparameter sensitivity on SIP
15

---

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
36.5
20.0
100
2973
36.2
15.54
8.
04
055
0.4
0.4
16
34
12
MAS
1007
11:22
21.5
11.0
MAA
13.30
23.50
14.0
11.5
14.4
13.50
16.0
GM4
3.50
6.6
0.50
Figure 7. Hyperparameter sensitivity
16


Reviews:
[{"review_id": "omDc8GrahC", "reviewer": "Reviewer_Nb7e", "summary": "This paper introduces SynEVO, an interesting neuro-inspired spatiotemporal evolutional framework designed for cross-domain adaptation in spatiotemporal learning. The core idea is to enhance knowledge transfer and model evolution by mimicking synaptic plasticity and neurotransmitter mechanisms from neuroscience.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear reviewer Nb7e,\n\nThanks for your encouraging comments!\n\n**Relations between SynEVO and transfer learning, optimizer design, evolutionary algorithms**\n\n**Transfer learning** is also a freeze-finetune mechanism where finetune is varied and specific to the problem itself. It does not involve active evolutions under different data distributions, while for SynEVO, it actively and continuously evolves by updating parameters when new data comes.\n\n**Evolutionary algorithm** tends to evolve by adaptive fusion among peer models. It can be viewed as a passive evolution, while SynEVO updates spontaneously according to data. \n\n**Optimizer design** includes various gradient-based adaptiveness and regularizations. In our work, we take gradients to measure correlations between model and data. We let it reorder sample groups to imitate curriculum learning of brain. Besides, we enable the regularization coefficient $\\lambda$ to change dynamically with increasing learning capacity. These schemes are devised for easy optimization in our evolution contexts.\n\n**Detailed empirical analysis and training efficiency**\n\n**(1) More empirical analysis on task reordering.** \n\nWe supplement the experiments of reordering tasks from **hard to easy** to emphasize the importance of training order. The results of errors are listed in the order of MAE/RMSE/MAPE.\n\nNYC: 7.217/18.807/0.415, CHI: 1.632/3.066/0.405;\n\nSIP: 0.705/1.394/0.208, SD: 11.636/19.789/0.163.\n\nThe reversed order falls into inferior performances, which emphasizes the significance of reordering the tasks from easy to difficult in our design. Moreover, above hard-to-easy performances are better than random ordering of SynEVO-REO, which may be attributed to common relations between neighboring tasks as they are ordered even the reverse one.\n\n**(2) Design of commonality extraction.** \n\na)\tIf iterative learning for commonality removed, only train and test on one dataset. Results are listed in order of MAE/RMSE/MAPE:\n\nNYC: 8.201/19.090/0.423, CHI: 1.554/2.887/0.385;\n\nSIP: 0.711/1.412/0.216, SD: 13.128/20.890/0.220.\n\nb) We set a different neural expansion rate $p_c$, e.g., $p_c=p_0/l(d_c)$ \n\nNYC: 7.213/16.310/0.422, CHI:1.550/2.765/0.367; SIP: 0.705/1.399/0.207, SD:11.944/19.599/0.168.\n\nWith above results, we can conclude our commonality learner and the setting of p are reasonable and empirically justified. \n\n**(3) Training efficiency.**\n\nOur training process can be 4 parts. I. Model warm-up with existing data. II. Complementary dual learners with elastic common container.  III. Contrastive learning to obtain distinguished patterns. IV. Couple elastic common container and personality extractor to train on new data.\n\n**Comparison.** On NYC, the above 4 parts cost about 800s, 795s, 545s and 111s respectively and the total time cost is about 2251s. The total time costs of baselines: AGCRN:1896s, ASTGCN:1884s, GWN:2233s, STGCN:823s, STGODE:3515s, STTN:1910s, CMuST: 2817s. Given increased generalization capacity (MAE reduced 42% at most) and inference efficiency (GPU cost reduced 78% at most), thus training costs are tolerable. Specifically, in our ablation studies, we can further confirm the importance of each module as shown in Tab.4. \n\n**Further theoretical analysis**\n\n**(1) Information theory.** Based on Appendix A, collective intelligence within a system increases with the input of data, i.e.,\n$H(X_i|X_1,X_2,\\ldots,X_{i-1})> H(X_{i+1}|X_1,X_2,\\ldots,X_i)$\nThus, we can take full advantage of the collective intelligence in the system to empower task generalization.\n\n**(2) Synaptic structure** is the medium of information transmission in human brain. The probability of synaptic neurotransmitter release can be defined as, $P_r=P_0(1-e^{-\\tau})$, $\\tau$ is the successive activeness difference between apre-synaptic-neuron and after-synaptic-neuron.\n\nSince the probability is exponentially correlated with the parameter $\\tau$, we envisage placing the gradient in an exponential position to map it. In task reordering, the tasks are ordered by gradients from small to large. Considering that decreasing dropout factor $p$ increases model activeness and enhancing its ability to cope with complex tasks, we further deduce Eq.(10). Meanwhile, we let the exponent be $l(d_c)-d_{max}$ to ensure $0<p_c(d_c)\\le1$. \n\n**Broader application.** \n\nOur model can be generally nested within other neural networks. For example, in geological prospecting, the available shallow-layer data and precious deep-layer information share both commonality and personalization. It is applicable to utilize our evolvable \"data-model\" collaboration to decouple the invariant and variable patterns, and reconstruct the OOD distribution with new patterns. More broadly, our solution can also be extended to dynamic systems such as molecular interactions, agent collaborations, with necessary adaptation.\n\nThanks again for your advice and we will include additional results and discussions into our next version. \n\nAuthors of Paper 5506", "claims_and_evidence": "Most claims are well-supported by theoretical and empirical evidence:\n1. Cross-domain learning increases information capacity – Supported by information-theoretic proof. \n2. SynEVO improves generalization – supported by experiments on four datasets, though results may be dataset-dependent.\n3. Superior efficiency (21.75% memory cost) – GPU usage comparisons validate this. \n4. Neuro-inspired synaptic evolution – Conceptually compelling and very intersting.", "methods_and_evaluation_criteria": "1. Evaluation is robust, utilizing four real-world datasets (NYC, CHI, SIP, SD) and standard metrics (MAE, RMSE, MAPE) with comparisons against strong baselines. \n2. Ablation studies validate key components, but broader application and more analysis of training efficiency would strengthen the evaluation.", "theoretical_claims": "The author presents an information-theoretic perspective, which is both interesting and novel.", "experimental_designs_or_analyses": "The use of four real-world datasets (NYC, CHI, SIP, SD) provides a diverse and realistic evaluation.\nMetrics such as MAE, RMSE, and MAPE are standard for assessing spatiotemporal prediction accuracy.\nAblation studies effectively isolate contributions of key components.\nHowever, given the general nature of the approach, incorporating additional tasks could further strengthen the study. \nMoreover, related areas such as transfer learning, optimizer design, and even evolutionary algorithms offer valuable directions for discussion. A more thorough theoretical and empirical analysis could enhance the justification for the paper's design choices. Nevertheless, the overall contribution is good.", "relation_to_broader_scientific_literature": "This paper is inspired by neuroscience, particularly ideas related to how the brain gradually learns and adapts by evolving its connections over time. Similar principles have been explored in studies on how humans accumulate knowledge and transfer learning across different tasks. The proposed approach also relates to continual learning in LLM/VLM, especially with the rise of large models. As models are increasingly required to adapt to new tasks without forgetting previous knowledge, frameworks like SynEVO offer a potential way to improve long-term adaptation and efficient knowledge retention.", "essential_references_not_discussed": "NA", "other_strengths_and_weaknesses": "see above", "other_comments_or_suggestions": "see above", "questions_for_authors": "NA", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "cPhZ7nEMhA", "reviewer": "Reviewer_esVG", "summary": "Drawing from neuroscience, this paper presents a theoretical investigation into methodologies for expanding information boundaries via cross-domain collective intelligence learning. The authors propose SynEVO, a synaptic evolutionary spatiotemporal network architecture. The framework employs a sample order reorganization strategy to emulate curriculum learning processes observed in human cognition, coupled with the implementation of two synergistic learning components, where these modules can cooperate with each other to enable model evolution and maintain a clear separation among domain-specific characteristics. The experiments are conducted to verify the effectiveness of the proposal.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear reviewer esVG,\n\nThanks for your constructive comments on our research.\n\n**W1. Why not use LLM?**\n\nLLM is popular to empower diverse applications but it is more specific to language processing and generation tasks. The reasons for not using LLM in this research are two-fold.\n\n(1) The core of this research is data-adaptive model evolution, which is essentially a new training paradigm. Our model can be coupled with other deep learning models to achieve efficient data-adaptive model updates. This research is orthogonal to the study of large models.\n\n(2) In fact, studies have demonstrated that smaller models are more suitable for numerical series-level predictions [1], while LLMs tend to excel in language generation tasks including question&answer [2] and planning [3].\n\nIn the future, it is interesting to take LLM as an agent to facilitate the learning process and then guide and schedule the adaptation and evolution process. Thanks again for your insightful question. \n\n**W2. Why skip cross-spatial domain adaptation?**\n\nThanks for your valuable question. We list the reasons in the **W1&Q1 response to Reviewer ci9Q**\n\n**W3&Q3 How to derive Eq.(8) and from Eq.(8) to Eq.(10)**\n\nOur analysis is based Ref.[4] and [5]. \nRef.[4] proposes an exponential model of the transmitter release probability:\n        $P_r=1-e^{[Ca^{2+}]^n/K}$\nwhere K combines parameters such as calcium binding rate and vesicle fusion efficiency, which affect information absorption in synapses. This formula explains the randomness of the combination of calcium ions with synaptotagmin.  \n\nAfter that, Ref.[5] directly measured the relationship between calcium concentration and transmitter release rate, and verified the applicability of the above exponential model.  Finally, we reduce ${{[{Ca}^{2+}]}^n/K}$ to a variable $\\tau$ to obtain Eq(8).\n\nIn our task reordering module, tasks are ordered from easy to difficult, by ranking the gradient from small to large. Since the smaller the dropout factor p, the more active the model is and the easier to achieve difficult tasks, our dropout factor p needs to decrease as the gradient gets larger. Hence, we can achieve Eq.(10). We let the exponent be $l(d_c)-d_{max}$ to make $0<p_c(d_c)\\le1$.\n\n**Q1.Why gradients determine the order?**\n\nFor each parameter w in the neural network, each step of parameter update is related to its own gradient, i.e. \n$W_k=W_{k-1}-\\eta\\nabla_k W=W_{k-1}-\\eta∂E/∂W$\nwhere $W_k$ is the parameter of k-th step, $\\eta$ is the learning rate, $\\eta∂E/∂W$ is the gradient of training loss to W.\n\nFrom the equation above, we can see that the closeness of the model to learning samples (data) can be described by the gradient of each iteration. Larger gradients indicate the current model is farther from the real function which perfectly fits the data, then the learning process is harder. To this end, gradients can determine the training order of sample groups.\n\n**Q2. Relationship between neuroscience and transfer learning**\n\nThe goal of transfer learning is to effectively transfer the knowledge of model A to a relevant task B. During the process, similarity counts. Based on theories of curriculum learning, humans master skills better when learning from easy to hard. During the learning process, similarity also exists between tasks. From the perspective of human brain evolution, information is transmitted through synapses in the human brain. This information passes through the synapse and forms a long-time stable memory. When facing similar new tasks, the brain releases presynaptic neurotransmitters based on the similarity between tasks, transferring the original knowledge to the new task. During the process, memory and learning can complement with each other, i.e., new well-learned knowledge can be expanded into memory, while existing memory can be retrieved when new knowledge is being learned.\n\nWe are truly grateful for your meticulous review and constructive comments on our manuscript. They have significantly contributed to enhancing the clarity and rigor of our work. We will add above deeper discussions into our next version.\n\n[1] Tan M, et al. Are language models actually useful for time series forecasting?[J]. Advances in Neural Information Processing Systems, 2024, 37: 60162-60191.\n\n[2] Feng P,  et al. AGILE: A Novel Reinforcement Learning Framework of LLM Agents[C]//The Thirty-eighth Annual Conference on Neural Information Processing Systems.\n\n[3] Ni H, et al. Planning, Living and Judging: A Multi-agent LLM-based Framework for Cyclical Urban Planning[J]. arXiv preprint arXiv:2412.20505, 2024.\n\n[4] Bertram, Richard, Arthur Sherman, and ELIS F. Stanley. \"Single-domain/bound calcium hypothesis of transmitter release and facilitation.\" Journal of Neurophysiology 75.5 (1996): 1919-1931.\n\n[5] Schneggenburger, Ralf, and Erwin Neher. \"Intracellular calcium dependence of transmitter release rates at a fast central synapse.\" Nature 406.6798 (2000): 889-893.\n\nAuthors of Paper 5506", "claims_and_evidence": "Yes. Most of them are clear. But in Lemma 1 (Eq.(8)), are there any references to support such Lemma, and then how to derive the Eq.(10)?", "methods_and_evaluation_criteria": "Yes. The proposed method seems make sense and resolve the issue of gaining collective intelligence in cross-domain transfer tasks for urban prediction.", "theoretical_claims": "Yes. The author proves Proposition 3.1 shows that the collective intelligence can be obtained by deriving the larger information entropy via mutual information computation.", "experimental_designs_or_analyses": "Yes. \n1) The authors make comparison among baselines like STGCN, STGODE. \n2) The author performs ablation studies to uncover the significance of each module (REO, Ela, PE) and display the results in Table 4. \n3) The author makes some detailed analyses, including analyses of sample group sequences, observed quick adaptation via loss behavior and effective zero-shot adaptation.", "relation_to_broader_scientific_literature": "This research involves the neuro-related learning into spatiotemporal forecasting, which I think is related to brain-inspired continual learning and complementary learning systems for collecting and gaining collective intelligence. The related researches can be found as below,\n1)Van de Ven G M, Siegelmann H T, Tolias A S. Brain-inspired replay for continual learning with artificial neural networks[J]. Nature communications, 2020, 11(1): 4069.\n2)Wang L, Zhang X, Su H, et al. A comprehensive survey of continual learning: Theory, method and application[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.\n3)Wang L, Zhang X, Yang K, et al. Memory replay with data compression for continual learning[J]. arXiv preprint arXiv:2202.06592, 2022.", "essential_references_not_discussed": "There must be some references for citation associated with Lemma 1.", "other_strengths_and_weaknesses": "Overall, this research is somewhat novel with key insights of both neural network designs and how to learn from human knowledge acquiring mechanism. In detail, I found the detailed technical strengths as below, \n1.The authors devise a sample group learning order via gradients similarity, diving into the essence of how the deep network updates, thus imitating the learning process from easy to difficult.\n2.The authors imitate neuron learning scheme to catch the shared commonality and information among data contents which is essential to cross-domain adaptation.\n3.The constructed dual learners (elastic common container and personality extractor) cooperate with each other are relatively novel, where it includes a common container to catch the commonality and a personality extractor to judge the difference, reducing the pollution of inappropriate data. This structure reflects the idea of cooperation and complementary. \nWeaknesses\n1.LLM is popular nowadays, why not try to applicate it to empower your model?\n2.Spatiotemporal data can be divided into spatial data and temporal data. However, I didn’t see the analyses or experiments of the term of spatial. For example, cross-spatial domain adaptation ought to be performed.\n3.Some confused equations, e.g., How can you derive Eq (8), is there any supports for it?", "other_comments_or_suggestions": "-Please show more explanations or analyses of the relationship between neuroscience and transfer learning in your paper since it’s not clearly clarified in the submission.\n-How can you derive Eq (8), is there any supports for it?", "questions_for_authors": "1.Why gradients can determine the training order? Please show some more reasons.\n2.Please show more explanations or analyses of the relationship between neuroscience and transfer learning in your paper since it’s not clearly clarified in the submission.\n3.How can you derive Eq (8), is there any supports for it?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KtobACEKHz", "reviewer": "Reviewer_n3PZ", "summary": "This paper, theoretically examines strategies for increasing information boundaries through cross-domain collective intelligence learning and introduces SynEVO, a synaptic evolutionary spatiotemporal network designed to enable cross-domain knowledge sharing and aggregation by addressing model independence constraints. The comprehensive experiments including ablation studies and detailed analysis are provided.\n\n## update after rebuttal\nI appreciate the authors' response. I will maintain my positive score.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear reviewer n3PZ,\n\nThanks for your constructive feedback of our research. Your valuable advice contributes a lot to our work.\n\n**W1&Q1(1). Explanations of 'harmony with diversity'.**\n\nThe concept of 'harmony with diversity' in our paper means that in order to build a generic spatiotemporal learning framework, there should be a certain degree of similarity (correlation) between sample groups or tasks, but they are not completely the same. To this end, a commonality learning container can be trained to capture the common patterns and enable distinguished representation learning from personal patterns. Then our learning framework can quickly transfer this commonality patterns to new tasks and obtain rapid generalization. We will supplement more clarification to improve the conciseness of such phrase.\n\n**W2&Q1(2). Explanations of ‘domains from the same source are not necessarily next to each other in the ordered sequence S’ in section 5.6.**\n\nThis sentence means that the sample groups from the same source (intra-source sample groups) do not necessarily show greater similarity than sample groups of different sources (inter-source sample groups). This sentence is the interpretation and further discussion of Fig.3(a) in our paper. In the figure, we find sample groups [2,0], [2,1], [2,2] and [2,3] from the second source domain are between the first source domain samples [1,0] and [1,3], which indicates above phenomenon.\n\n**Q2. Elaborate how diverse aspects of human learning facilitate the deep neural network for cross-domain learning and transfers requires clarification in a holistic perspective.**\n\nNeuroscience reveals how brain structure impacts human cognitive and behaviors. The core of this paper is to design the learning process of cross-domain knowledge generalization by imitating neuroscience mechanisms from human brain. Specifically, our paper is divided into two aspects: **(1) curriculum learning** and **(2) synapse structure with elastic neural networks**.\n\n**Curriculum learning** reveals the rule of human brain learning. People usually start to learn from simple tasks, and then learn to master more complex skills with increasing difficulty of learning tasks. Actually, the learning efficiency of such practice is higher than directly resolving new difficult problems [1,2]. To this end, task ordering from easy to difficult and the similarity between tasks are important to facilitate the transfer, which is emphasized in Sec.4.2 of our paper.\n\n**Synapse structure.** From the perspective of human brain evolution, information in the human brain is transmitted through the **synapse structure**. This information passes through the synapse and forms an effective memory. At this time, if a new task needs to be learned, the stored information (i.e., memory in brain) becomes active, the presynaptic neurotransmitter will be released according to the correlation between tasks, so as to enable the original learned knowledge to be effectively transferred to learning process of new task. Thus, the long-term stable memory and new knowledge learning can supplement with each other, i.e., memory is transferred to empower learning while learning (mastering knowledge) expands and enriches the content of memory.\n\nThanks again for taking time to review our paper and we will incorporate above deeper analysis and explanations of concepts into our paper.\n\n[1] Wang X, Chen Y, Zhu W. A survey on curriculum learning[J]. IEEE transactions on pattern analysis and machine intelligence, 2021, 44(9): 4555-4576.\n\n[2] Blessing D, Celik O, Jia X, et al. Information maximizing curriculum: A curriculum-based approach for learning versatile skills[J]. Advances in Neural Information Processing Systems, 2023, 36: 51536-51561.\n\nAuthors of Paper 5506", "claims_and_evidence": "Yes. The claims are well-supported with both theory principles and empirical experiments.", "methods_and_evaluation_criteria": "Yes. This proposal makes sense based on neuroscience and information theory of information bottleneck, which contributes to transfer learning and multi-task learning.", "theoretical_claims": "Yes. In Appendix A, the author applicates the theory of mutual information and information entropy to measure the information in order to prove that the information is increasing during cross-domain adaptation which is claimed in Proposition 3.1. It seems OK for such proposition.", "experimental_designs_or_analyses": "Yes. This research benefits from their thoughtful experimental designs on three folds. 1) Extensive dataset collection. The authors have collected spatiotemporal data with various domains from four cities, especially a large temporal scale set on SD from LargeST. 2) The cross-domain transfer design for model verification on its claims. 3) The detailed analysis on three aspects supplements more information on how the model operate in a micro and case-by-case perspective.", "relation_to_broader_scientific_literature": "This paper falls onto the application of both neuroscience and improvement of deep neural networks, which is inherited by complementary learning [1], and curriculum learning [2].\n[1] McClelland J L, McNaughton B L, Lampinen A K. Integration of new information in memory: new insights from a complementary learning systems perspective[J]. Philosophical Transactions of the Royal Society B, 2020, 375(1799): 20190637.\n[2] Matiisen T, Oliver A, Cohen T, et al. Teacher–student curriculum learning[J]. IEEE transactions on neural networks and learning systems, 2019, 31(9): 3732-3740.\nBut this work inherits such idea and improve it to spatiotemporal learning in several insights.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "Strengths: \n1. This research paper is novel at new task definition on spatiotemporal cross-domain transfer among both temporal domain and source domain. \n2. This paper is well-organized with clear problem, challenges and solutions, along with good figure illustration and detailed experiment designs.  \n3. The proposed techniques is overall novel and provide new insights into deep neural network designs with mechanisms of human brains.\n\nWeaknesses\n1. In line 149, the authors have mentioned ‘in harmony with diversity’, but this concept lacks explanations. I think you are required to elaborate this phrase to moderate the whole sentence and context. \n2. In Section 5.6, the authors have mentioned that ‘domains from the same source are not necessarily next to each other in the ordered sequence S’, but this sentence is quite confused and requires efforts with further explanation.", "other_comments_or_suggestions": "Actually, in Introduction part, how diverse aspects of human learning facilitate the deep neural network for cross-domain learning and transfers requires clarification in a holistic perspective.", "questions_for_authors": "1. The authors should clarify more on ‘in harmony with diversity’ and ‘domains from the same source are not necessarily next to each other in the ordered sequence S’. \n2. Please elaborate how diverse aspects of human learning facilitate the deep neural network for cross-domain learning and transfers requires clarification in a holistic perspective.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nP6kbkO7br", "reviewer": "Reviewer_ci9Q", "summary": "This work mainly focuses on observation, i.e., deep learning can imitate the neuroscience mechanism to expand the information boundaries through theoretically and empirically cross-domain collective intelligence learning. Then, drawing from neuroscience, this research introduces a synapse-inspired evolutional spatiotemporal network, which facilitates cross-domain knowledge sharing and aggregation. This approach involves three submodules: curriculum learning-based sample group ordering, complementary dual learners, and an adaptive dynamic coupler to capture common intelligence and cross-domain task-dependent patterns. Experiments show that collective intelligence increases the model's generalization capacity under both source and temporal shifts by at 0.5% to 42%, including few-shot and zero-shot transfer.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear reviewer ci9Q,\n\nWe deeply appreciate the time and efforts you have invested in reviewing our paper. We are honored to receive your recognition of the novelty and theoretical contribution. Your comprehensive feedback is helpful in guiding our revisions.\n\n**W1. Why skip spatial adaptation**\n\nUrban space is relatively static. Over a long period of time, data distribution and data sources will switch and change, but urban space will remain relatively invariant. The change of urban space can be divided into two aspects: 1) the land use of urban space, e.g., the urban land increases. 2) the model transfer among cities. Actually, our research paper focuses on the same urban system, and the scenarios of urban space expansion and cross-city model transfer may be out of the research scope of this work. Instead, we mostly focus on the collective intelligence in the same system, i.e., the data temporal domain distribution shift or the source domain shift, which is more usual in real-world practices. We study the problem of how spatiotemporal learning models adaptively evolve with data by capturing commonality and transfer the regularities to new scenario, so as to achieve the ability of learning whenever new data arrives. For abovementioned urban expansion or cross-city transfer scenarios, we can still take the core idea of this work, reordering new sample groups and make the elastic container grow based on SynEVO to quickly optimize and fine-tune existing models, realizing rapid transfer across different spaces.\n\n**W2&Q1Q2. A list of typos**\n\nThanks for your careful check of our paper and we have thoroughly corrected the spelling mistakes and typos. The detailed correction are listed as below.\n \n1) Line 133 and 557, 'spatiotemoporal' -> 'spatiotemporal';\n\n2) Line 134-135, 'the there must shre' -> 'there must be shared';\n\n3) Line 380-381, 'signifcant' -> 'significant'.\n\nThanks again for your expert review of our paper and we will incorporate the discussions and supplement more close literature to facilitate the validness of our paper.  \n\nAuthors of Paper 5506", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes. The effectiveness of the proposed solution has been demonstrated by information theories and neuroscience principles with theoretical guarantee. Also, the extensive comparison and ablation studies show improvements against baselines and ablative variants on various datasets.", "theoretical_claims": "Yes. In Appendix A, the author proves the correctness of Proposition 3.1 in section 3 based on the theories of information such as information entropy and mutual information, and I think that is roughly reasonable for the claim of Prop 3.1 (cross-domain learning to gain collective intelligence).", "experimental_designs_or_analyses": "Yes. I think the experiment design can be the highlight of this research. The authors design various experiments to verify the effectiveness of such a framework, including designs on cross-source and cross-temporal domain learning for verifying whether enhancing collective intelligence, the comparison against mainstream existing models, and ablation studies for component disentanglements. In addition, three detailed analyses via three typical cases during training are also provided to better understand the framework working flows.", "relation_to_broader_scientific_literature": "This paper contributes to the inter-disciplinary neuroscience, deep learning, and urban science fields. It develops the new techniques of an evolutionary deep learning framework to facilitate gaining collective intelligence among diverse urban data via a set of neuroscience principles, which is associated with the following literature.\n\na) Xu F, Zhang J, Gao C, et al. Urban generative intelligence (ugi): A foundational platform for agents in embodied city environment[J]. arXiv preprint arXiv:2312.11813, 2023.\n\nb) Feng J, Du Y, Liu T, et al. Citygpt: Empowering urban spatial cognition of large language models[J]. arXiv preprint arXiv:2406.13948, 2024.\n\nc) Bassett D S, Sporns O. Network neuroscience[J]. Nature neuroscience, 2017, 20(3): 353-364.", "essential_references_not_discussed": "I have not found any further references to be included.", "other_strengths_and_weaknesses": "Strengths:\n1.\tA new and pioneering research problem for evolutional learning framework. This research provides a fresh perspective from neuroscience to facilitate the deep model generalization.\n2.\tValid techniques and solutions. The proposal focused on imitating the human learning process via various principles and improving the collective intelligence from different domains where similar but different sample groups are sequentially fed into the learning pipeline. \n3.\tGood structure and experiments. This paper is well-structured with intuitive figure illustrations. The ingenious experiment designs can illustrate the proposal’s effectiveness. \n\nWeakness:\n1.\tIn your experiment, since you use spatiotemporal data, why do you only design temporal adaptation and source adaptation while skipping spatial adaptation?\n2.\tSeveral typos to be corrected, please see my ‘Other comments or suggestions’.", "other_comments_or_suggestions": "1.\tIn Proposition 3.1, line 133 and Appendix A, line 557, there is a typo: ‘spatiotemporal’ instead of ‘spatiotemoporal’.\n\n2.\tIn Proposition 3.1, line 134-135, there is a typo: ’there must be shared’ instead of ‘the there must share’", "questions_for_authors": "1.\tSince you use spatiotemporal data in your experiment, why do you only design temporal and source adaptation while skipping spatial adaptation?\n\n2.\tSeveral typos to be corrected, please see my ‘Other comments or suggestions’.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_concerns": "No ethical concerns."}}]

Author rebuttal:

Meta-review: This paper proposes SynEVO, a neuro-inspired framework for spatiotemporal cross-domain adaptation. Drawing from neuroscience concepts like curriculum learning and synaptic evolution, the framework enables dynamic model growth and knowledge transfer across domains. It combines task reordering, dual learners for disentangling common and domain-specific representations, and an adaptive coupler for continual evolution.

The paper’s strengths lie in its novelty, theoretical grounding, and comprehensive empirical evaluation. It demonstrates consistent improvements across multiple datasets and scenarios, with up to 42% gains in generalization. Ablation studies and runtime comparisons support the design, and the authors provide thoughtful rebuttals that clarify theoretical points and address reviewer concerns effectively.

The main weaknesses are minor. Some mathematical derivations could use more justification, and the paper does not evaluate spatial adaptation explicitly. However, the authors justify this focus and explain the broader applicability of the method.

Overall, this is a creative and well-executed contribution that connects neuroscience principles with machine learning and offers a useful direction for adaptive spatiotemporal modeling. I recommend acceptance.
Decision: Accept (spotlight poster)