Spike-based Digital Brain: a novel fundamental model for brain activity analysis
Authors: Shaolong Wei
Qiyu Sun
Mingliang Wang
Liang Sun
Weiping Ding
Jiashuang Huang
Venue: ICLR 2026 Poster (2026)
Keywords: Brain activity
Fundamental model
Spike computing
fMRI prediction
Brain diseases.
Primary area: applications to neuroscience & cognitive science
TL;DR: We propose the Spike-based Digital Brain, which enables high accuracy brain activity prediction and assists in brain disease classification, abnormal brain region detection, and effective connectivity inference.

Abstract: Modeling the temporal dynamics of the human brain remains a core challenge in computational neuroscience and artificial intelligence. Traditional methods often ignore the biological spike characteristics of brain activity and find it difficult to reveal the dynamic dependencies and causal interactions between brain regions, limiting their effectiveness in brain function research and clinical applications. To address this issue, we propose a Spike-based Digital Brain (Spike-DB), a novel fundamental model that introduces the spike computing paradigm into brain time series modeling. Spike-DB encodes fMRI signals as spike trains and learns the temporal driving relationships between anchor and target regions to achieve high-precision prediction of brain activity and reveal underlying causal dependencies and dynamic relationship characteristics. Based on Spike-DB, we further conducted downstream tasks including brain disease classification, abnormal brain region identification, and effective connectivity inference. Experimental results on real-world epilepsy datasets and the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that Spike-DB outperforms existing mainstream methods in both prediction accuracy and downstream tasks, demonstrating its broad potential in clinical applications and brain science research. Our code is available at https://github.com/UAIBC-Brain/Spike-DB.

Published as a conference paper at ICLR 2026
SPIKE-BASED DIGITAL BRAIN: A NOVEL FUNDAMEN- TAL MODEL FOR BRAIN ACTIVITY ANALYSIS
Shaolong Wei', Qiyu Sun', Mingliang Wang",, Liang Sun', Weiping Ding' Jiashuang Huang
School of Artificial Intelligence and Computer Science, Nantong University
2 School of Computer Science, Nanjing University of Information Science and Technology
Cooleege of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics wei shaol 2ong30ggma ill.il... com, sunqiyu02 0010001100 63. com,
[wm1489 489, sunl] ) @nuaa enu (dwp hjsddmm) @ 63. com
ABSTRACT
Modeling the temporal dynamics of the human brain remains a core challenge computational neuroscience and artificial intelligence. Traditional methods often ignore the biological spike characteristics
brain activity and find difficult to reveal the dynamic dependencies and causal interactions between brain regions, limiting their effectiveness in brain function research and clinical applications. To address this issue, we propose Spike-based Digital Brain (Spike-DB). novel fundamental model that introduces the spike computing paradigm into brain time series modeling. Spike-DB encodes fMRI signals as spike trains and learns the temporal driving relationships between anchor and target regions to achieve high- precision prediction of brain activity and reveal underlying causal dependencies and dynamic relationship characteristics. Based on Spike-DB, we further con- ducted downstream tasks including brain disease classification, abnormal brain region identification, and effective connectivity inference. Experimental results on real-world epilepsy datasets and the Alzheimer's Disease Neuroimaging Initia- tive (ADNI) dataset show that Spike- DB outperforms existing mainstream meth- ods in both prediction accuracy and downstream tasks, demonstrating its broad potential in clinical applications and brain science research. Our code is available at httttps:///itthhm www./aIC///-//.../ - Stoii-ieeea///n--D-e
INTRODUCTION
The human brain is one of the most complex dynamic systems, and its activity patterns underlie cognition, emotion, and behavior (Bassett Gazzaniga, 201I). Therefore, in-depth analysis of brain activity is of central importance in neuroscience and clinical medicine (Poldrack & Farah 2013). Functional magnetic resonance imaging (fMRI), currently the most widely used non-invasive imaging technique, reflect the spatiotemporal dynamics of neural activity at brain-wide scale by measuring temporal variations in the blood oxygenation level-dependent (BOLD) signal (Kim) & Bandettini 2023). However, fMRI signals typically have low signal-to-noise ratio and susceptible to interference from factors such as physiological noise and scanning conditions (Zhu al. 2023; Misaki & Bodurka 2021 Vizioli al. (002). This poses significant challenge for extracting reliable and stable neural activity signatures from complex brain network data. To address this issue, brain activity analysis urgently requires high-precisto modeling methods, multimodal
information fusion, and advanced computational techniques (Tong & Pratte 2012
Lee et al. 2024
Yuan et al. 2022
Recently, various fundamental models have been proposed for analyzing brain activity, driven by advances neuroscience and artificial intelligence. Early methods relied primarily on statistical modeling and signal decomposition techniques, such as independent component analysis (ICA) [Bekmann et al. 2005) and dynamic causal modeling (DCM) (Friston et al.) (2003). Currently, deep learning (DL) methods have gained increasing attention. Frameworks such as graph neural network (GNN) Narrdi et al., 2024). Transformer (Bhatti et al. 2024), and contrastive learning (Zong et al.
"Corresponding author.

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Figure 1: The proposed Spike-based Digital Brain (Spike-DB) architecture. (1) The input fMRI data is encoded a spike train. (2) subset of brain regions are randomly sampled as anchor regions, and the remaining regions are selected as target regions, and are fed to the anchor and target encoders, respectively. (3) The predictor takes as input the output of the anchor encoder and the mask token of each target region be predicted, and predicts the representation of the target region conditioned on the position embedding. (4) The representation of the target region corresponds to
the output of the target encoder, and its weights are updated each iteration by the Exponential Moving Average (EMA) of the anchor encoder weights. (5) Spike decoding restores the embedding space features generated by the predictor to the original brain region space.
2024) are now widely used to capture the high-dimensional spatiotemporal dependencies between brain regions. Benefiting from the advances fundamental models, the neuroscience community has begun exploring scage pre- pre-training strategies. For example, models such as BrainLM (Caro al. (023). BrianMass (Yang et al. 2024), and Brain-JEPA (Dong al. (2024) employ predictive or generative learning mechanisms to learn universal and robust representations from large-scale neu- roimaging data, demonstrating excellent generalization capabilities multi-task and cross-modal
brain activity analysis.
Inspired by the above fundamental models, digital brain models have emerged, offering novel in- sights for modeling neural activities at the whole-brain scale. Concepts such as the virtual brain (Jirsa et al., 2023), digital twin brain (Lu et al.] 2023), and virtual twin brain (Wang et al. 2024), as well as more advanced neural activity-based models (Wang et al. 2025), have been proposed to able individualized brain digital mapping through the integration of multimodal imaging and neural dynamics modeling. However, these digital brain models often built statistical or continuous- valued neural network frameworks. Although they can capture macroscopic temporal patterns and functional connectivity (Beckmann et al. 20055,, they struggle reflect the discrete firing charac- teristics and biological constraints in brain activity (Gao & Wu (2166), thus limiting their ability to interpret the mechanisms of real-world neural activity. Therefore, developing brain activity mod- eling frameworks that are more closely aligned with biological neuroscience has become an urgent need.
To address these challenges, we propose the Spike- based Digital Brain Model (Spike-DB), novel fundamental model for brain activity analysis. This framework introduces the spike computing paradigm into brain time series modeling, enabling the digital brain to reproduce dynamic char- acteristics that are closer to the biological nervous system. Spike-DB can achieve high-precision prediction of brain activity by learning the temporal driving relationships of some brain regions to other brain regions, while revealing potential abnormal patterns and effective connectivity in brain diseases. The schematic diagram of our proposed method is shown in Figure Specifically, first encode the fMRI time series into a spike train, and randomly divide the anchor brain region and the target region in the brain region. By learning the temporal driving relationship of the anchor
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namepee
Prediction
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Figure 2: Left: The SNN model simulated by the IIR filter shows the connection structure composed of two biological neurons and presents the neural simulation process of the two operations Azon(-) and (.).).. Right: Example of anchor and target masking strategy. Given four brain regions (A, B, C, D), we randomly sample two anchor regions (B, C) and use the remaining regions (A, D) as target regions. We use Spike-DB to predict fMRT data of regions A and D. The prediction results can be applied to three downstream tasks: effective connectivity (EC) quantification, abnormal brain regions detection, and brain disease classification.
region to the target region, we then obtain a dynamic prediction of the target region. Finally, the prediction results restored to the original brain region space through spike decoding, achieving accurate modeling and prediction of brain activity. In this work, our contributions are summarized follows:
We propose Spike-based Digital Brain (Spike-DB), which introduces the spike computing paradigm into brain time series modeling, thereby achieving high-precision prediction of brain activity.
Based on Spike-DB, we further explore downstream tasks such as brain disease classifi- cation, abnormal brain region identification, and effective connectivity inference, demon- strating its broad potential in clinical applications and brain science research.
We evaluate the performance of our proposed method using a real-world epilepsy dataset and the public Alzheimer's 's's Disease Neuroimaging Initiative (ADNI) dataset, and the results show that the proposed method outperforms existing methods.
2 METHOD
2.1 SPIKING NEURON MODEL.
The traditional Leaky Integrate-and-Fire (LIF) neuron (Gerstner & Kistler 2002 mainly focuses on the dynamics of the membrane potential while ignoring the synaptic dynamic characteristics and the internal signal filtering mechanism neurons. Therefore, it has limitations in characterizing the high- and low-frequency components and the long-term dependencies across brain regions that are reflected in the BOLD signals of fMRI data. To overcome these limitations, we introduce LIF neurons formulated with Infinite Impulse Response (IIR) filter (Fang et al. 2020), leveraging their recursive structure to capture the temporal dynamic features fMRI data.
We denote the input spike train Et - t/), where t! denotes the arrival time of the jth spike from the in input synapse. Sim- ilarly, the output spike train can be defined as (tt) = E 5(t - t¹), tf E {tf : u(t') = Vetre)- Thus, the SNN model is expressed as network of IIR filters with nonlinear neurons:
sequence of time-shifted Dirac delta functions: 2;
v[[] Vihrer (4] + Σ wi
=1
r[| -. r([ - 1] + 01 1]
(2)
011 = Hea(v(() Vihre)
(3)
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(4) where / is the time, v[[] is the neuron membrane potential, Vitre is the threshold potential, wi the associated weight of the in synapse, M is the total number of synapses, r[4] is the reset filter, Tr controls the decay speed of the reset impulse, fi[t] is the second-order IIR filter of the 4th synapse, +T,
2] fittl aifilt a 2 Brra -
-1 01 = e Tm + 02 c Ts , B = c Tme To Im and are time constants, and Hea(-) is the Heaviside step function, if x 2 O, Hea(x) = 1, otherwise Hea(x) =
-1 -1
Let Azon(.) represent the operation of transmitting spike to the next neuron, which can be sim- ulated by a second-order exponential IR filter in Eq. 4 Let S.)(.) represent the behavior of the neuron updating the membrane potential and firing spikes, as shown in Eq. [ to Therefore, the SNN model simulated by the IIR filter can be divided into two operations, Azon(. and SN(-), as illustrated on the left in Figure 2]
2.2 SPIKE-BASED DIGITAL BRAIN
We now introduce the proposed Spike- Based Digital Brain (Spike-DB), non-generative framework for self-supervised learning in the space of spike representations, as illustrated in Figure The core idea of Spike-DB to randomly extract subset of brain regions from the full fMRI time series as anchor regions and predict the fMRI time series of the remaining target regions. We use the Spike Transformer (Yao et al. 2023) as the underlying architecture for the anchor encoder, target encoder, and predictor. Operating in the spike space, the Spike Transformer effectively encodes fMRI time series into event-driven spike trains, preserving their dynamic information while reducing redundancy and noise (Ye et al. 2023).
Spike encoding of fMRI time series. We employ an IIR filter-emulated SNN model capture temporal features in fMRI. Each subject's fMRI time series data is represented as A E R NxT, where N denotes the number of brain regions and T denotes the length of the time series. To this end, we can obtain the spike-based representation of the fMRI time series after Aron(-) and SN(() operations with the input A
(5) where / is the layer index, Xo = AF, and the final IMRI spike train is represented as X E NR,
X = (Ni(Arro((xx-i), (= I,,,,.., L
Anchor brain regions. Spike-DB aims to predict the representations of - K target regions based on the representations of K anchor regions. For input fMRI spike train X E RRNT, anchor regions Xane = {I1,.... Dis ..., xk} E IR KxT are obtained by randomly sampling brain regions in the range {1, ..., N). Next, Xanc is fed into the anchor encoder fo to generate the corresponding region-level representation Tanc:
Tanc = (T
(6)
where Tane E I KDD. D is the embedding dimension of each brain region, Pane represents the mask associated with the anchor brain region Xanc> Tz. is the representation of the i-th (i = 1,2, K) brain region, and the parameter 0 is updated by gradient descent.
Target brain regions. In Spike-DB, the target brain region X E (N)K corresponds to the fMRI spike train representation obtained from the remaining N - K brain regions, i.e., Xtar (Z1,..., 2N ).) We feed this into the target encoder fa to obtain the corresponding brain region-level representation rlar:
riar traz jEri..
where r/ E IRIDD Play represents the mask associated with the target brain region Ty, and rzy is the representation of the j-th (j = 1,2, .... N K) brain region. In particular, the target brain region obtained by masking the output representation of the target encoder rather than the input, and the parameter 0 is updated using an Exponential Moving Average (EMA) to smooth the training process and reduce noise, thereby obtaining more stable representation (Assran al., 2023).
Prediction. Given the output Fone of the anchor encoder, we aim to predict N - K target region _NNKK
representations rlars ?2 22ar...., far Therefore, for a given target region P'ar and its corresponding target mask Plar. the predictor --) takes as the input Fane and the mask token {my} (my)jeria. for
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each target region, and outputs each target region prediction Play:
Far ffz, jeptar 2 (Tancs {m JEplor)
where P E RIID and the parameters @ are updated via gradient descent. The mask tokens are parameterized by shared learnable vector and represented in conjunction with positional embed- dings. Since predictions need to be made for each of the .N - target regions, we apply the predictor N K times, each time conditioned on the mask tokens corresponding to the target brain region location, to obtain predictions :tar: larr ...; - in sequence. To facilitate understanding, we pro- vide simple example of predicting the target brain region based the anchor brain region and its applicable downstream tasks in the right part of Figure 2 Furthermore, during training, the training loss Ly is defined as the mean squared error (MSE) between rlar and its corresponding prediction Plar in terms of spike firing rates:
N-K K
L, N
Σ
(i
R
K
where Ríaa +1 Σ Flar Mar - - Plan. which represent the average spike firing rates of rlar and Plar in embedding dimension D respectively.
Spike Decoding. To restore the embedding features generated by the predictor the original brain region space, we add a spike decoder at the end of the model. We first concatenate the predicted values rerr ri.r...., _.N-K of the target brain region according to the brain region index to form the overall target embedding matrix Ptar:
Ptor concat (Ftar F2
tar PN-K
(10)
where Ftar E ( Essentially, our task is a regression task, where we reduce the spike train to the predicted fMRI time series Y R(N-K)xT by applying fully connected layer, represented as Y Linear Linear(ftar).
EXPERIMENTS AND RESULTS
This section evaluates the effectiveness of our proposed Spike Digital Brain Model (Spike-DB) with extensive experiments. Our goal is to address the following research questions:
RQ1. How does Spike-DB compare to various state-of-the-ard models?
RQ2. Can Spike-DB identify and explain abnormal brain regions or effective connectivity associated with neurological disorders such as epilepsy and AD, and whether its findings is consistent with clinical observations and existing medical research?
RQ3. Do the Spike mechanism and brain region selection strategy play signifi- cant role in the model's performance?
3.1 EXPERIMENT SETTINGS
Implementation details. In this study, conduct experiments using the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset1 and a collected epilepsy dataset. The datasets are described in detail in the Appendix A.I The epilepsy and ADNI datasets are divided into four discase datasets and two healthy datasets: FLE (T = 240). TLE (T = 240), SMC 240), and NC-A (7 197). For each dataset, 80% the
(T= 197), EMCI (T = 197), NC-E data is allocated for pre-training and 20% for testing prediction accuracy and downstream tasks (ab- normal brain region detection and quantification of effective connectivity). In all tasks, the division of the training and test sets is based on the subjects rather than the data. Each dataset corresponds to a pre-trained Spike-DB model: ÓFLE. 8TLE, 85MC. θEMCI, 8NC-E and ôNC-A- For details on the implementation of the downstream brain disease classification task, see Appendix A.5) The pre-tree pre-training parameter settings are shown in Appendix(A.2] Notably, we fix the number anchor re- gions K to 89 in the pre-trained models above. This leaves only target region, and the fMRI time
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Table 1: Results of fMRI time series prediction task achieved by all methods on six data types (i.e., FLE, TLE, NC-E, EMCI, SMC, and NC-A), averaged over five independent runs. to the higher, the better; the lower, the better. The best results are in bold, underlined indicate significant
improvements over previous methods (p < 0.05).
Method
Metric
FLE
TLE
NC-E
EMCI
SMC
NC-A
R²1
0.922z043
0,948
0,9342088
0.907.023
0.902,.443
0,936
BrainLM
RSE
0.2791015
0.2661051
0.2581035
0.305
0.313100
0.254.022
R²1 f
0.938 0.95
0.9582004
0.930..038
0.929
0.957
BrainMass
RSB
0.249
0.239
0,204
0.265
0.267=017
0.206
R²1 t
0.952a.02 0.964
0.967
0.938a.039
0.943
0,9651022
Brain-JEPA
RSE
0.219-
0.217
O. 081
0.249.u035
0.240
0, 0.1
R²1
0.9 0.969+040
0,974+014
0,947+.034
0.948+037
0,975+026
BrainSymphony
RSE
0. 0.1922.013
0,2022.012
0.1622.055
1..29
0.228a.000
0.1602017
R²1 f
0.972
0.979.
0.983
0.954 0339
0.957
0.981 0:66
Spike-DB
RSE
0.168
0.184
0.131
0.2131.02
0.207
0.137 026
series for all regions are predicted recurrently. Experimental results show that our model achieves optimal performance under this setting. Related ablation studies are described in Section 3.6
Metries. For the fMRI time series prediction task, we employ the root relative squared error (RSE) and the coefficient of determination (R²). For the brain disease classification task, we employ the accuracy (ACC) and Fl score. The evaluation metries are detailed in Appendix A.3 A.3
3.2 MAIN RESULTS (RQ1)
We compared Spike-DB with range of state-of-the-ar models to evaluate its performance. The lected baselines cover a variety of architectural methods, from specialized deep learning models to large-scale fundamental models: 1) BrainLM (Caro et al. 2023): A large-scale fundamental model that learns fMRI representations using masked autoencoding strategy for time series data. 2) Brain- Mass (Yang et al. 2024)): A brain network-bass based model learned through large-scale self self-super learning, using masked ROI modeling and representation alignment to pre-train a Transformer en- coder. 3) Brain-JEPA (Dong et al. 2024): Leveraging the Joint Embedding Prediction Architecture (JEPA) to learn powerful representations by predicting masked data in latent space. 4) BrainSym- phony (Khajehnejad et al. 2025): A lightweight Transformer- based model that processes fMRI time series through parallel spatial and temporal streams and unifies their representation with perceptrons.
Table repoort the results of all methods on the IMRI time series prediction task. From Table D we can observe that our Spike-DB achieves state-of-the-art performance across all datasets. Compared to the best baseline model (BrainSymphony), our Spike-DB DDB achieves comparable or even better performance. For example, on the epilepsy and ADNI datasets, BrainSymphony and Spike-DB -DB achieve an average improvement of 1 1% and 0.8% on R², and 14.9% and 1 11.4% on RSE, respectively. Furthermore, on the relatively more challenging EMCI and SMC data, Spike-DB maintains state-of- the-art prediction performance. Spike-DB also achieves the highest R² and lowest RSE in the normal control groups NC-E and NC-A. These results demonstrate that spike-based digital brain modeling can better capture the temporal dependencies and nonlinear characteristics of brain regions.
3.3 CLASSIFICATION PERFORMANCE (RQ1)
To comprehensively evaluate the effectiveness of Spike-DB in brain disease classification, we further introduced models designed specifically for brain disease classification in recent years: Starformer (Dogg et al. 2025) and STCAL (Liu et al. 2023), as comparison methods. Table Summariee the results of different methods in four brain disease classification tasks: FLE vs. NC-E, TLE vs. NC-E, EMCI vs. NC-A, and SMC vs. NC-A. It is not difficult to see that BrainLM. STCAL, BrainMass, and Brain-JEPA perform well overall, but lag behind BrainSymphony and Starformer in terms of ACC and F1 multiple tasks. Starformer achieves high ACC the FLE vs. NC-E and TLE

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Table 2: Results of brain disease classification achieved by all methods on four binary classification tasks (i.c., FLE vs. NC-E, TLE vs. NC-E, EMCI vs. NC-A, and SMC vs. NC-A), averaged over five independent runs. The best results are in bold, underlined indicate significant improvements
over previous methods (p < 0.05).
FLE vs. NC-E
TLE NC-E
EMCI vs. NC-A
SMC vs. NC-A
Method
ACC(S))
FI(%)
ACC(S))
F1(%)
ACC(%)
FI(%)
ACC(%)
F1(%)
BrainLM
84.26x3.6
82.11
83.17
77.92s4.0
78.79s435
70.83x3.20
80.56x335
75.29.2.08
STCAL
1
14
15..5
81.93+2.44
81,8222.00
81.48+30r 46.00
75.61
BrainMass
87.04x2.9
87.76x3
142.76
82.50+2.94
83.3333.44
78.43x307
81.69x3.89
82.35x304
Brain-JEPA
87,96x2.22
46.59x3.4
83.10
83.95s4.04
84,85x2.7
81.48
83.40
81.82
BrainSpmphony
88.8942
87.76x382
88. 12a3.32
85.37a3.55
85, 85,8883445
86.21
84.51a3.21
83,58
Starformer
19
88
89. 1
86. 36.75
86,36+1
83. 83.20a3as
85,92a243
85. 85.71
Spike-DB
91.67a274 91.9042.37
90.10a2.94
88.09
87,88 11
85.1943.11
86.95.264
87.32.1.26
vs. NC-E tasks, while BrainSymphony performs exceptionally well on the EMCI vs. NC-A task. However, Spike-DB achieves the best performance overall on all four tasks, achieving 86.95% ACC and 87.32% Fl on the more challenging SMC vs. NC-A task. These results further validate the effectiveness and robustness of Spike-DB in brain disease classification tasks.
3.4 MAPPING OF ABNORMAL BRAIN REGIONS DRIVEN BY SPIKE- DD (RQ2)
In this experiment, apply the trained Spike-DB DD model of the NC group (ÓNC-E, 5NC-A) to the data of FLE, TLE, EMCI, and SMC patients to predict their fMRI signals under normal constraints. For the patient's 's predicted results and original IMRI data, perform group averaging processing in the dimension of the brain region. That is, for each sample, we calculate its mean fMRI signal within each brain region obtain group-level average representation N x T. Subsequently, we compared and analyzed the original average IMRI data of the patient group with the predicted data of the model. To further identify abnormal brain regions, we perform statistical tests on the differences between the original and predicted data after group averaging. Specifically, we use paired f-tests to assess the significance of the differences between the two in each brain region. This method allows us to screen brain regions that show significant differences in the patient population compared to the model predictions, thereby inferring potential pathological abnormalities.
Statistical test results show that for the four disease data sets (FLE, TLE, EMCI, and SMC), small number of brain regions exhibit significant differences (p < 0.05) between the original and predicted data. Specifically, we observe significant differences in four brain regions in the FLE data, three in the TLE data, five in the EMCI data, and two in the SMC data. We plot violin plots for the significantly abnormal brain regions to illustrate the distribution differences between the original data and the predicted results. These abnormal brain regions and their corresponding violin plots appear in Figure 3 The results indicate that significant deviations between the predicted and true signals exist in regions such as HIP, THA, and PCG, suggesting possible pathological abnormalities in these regions.
Notably, some of the identified brain regions are highly consistent with previous studies on epilepsy and ADNI. For example, studies have shown that patients with FLE often exhibit abnormal neural activity in the SMA region (Hong et al. 2023) and significantly increased functional connectivity in the HIP region (Englot et al. 2016). The pathogenesis of most TLE cases closely linked to the HIP region (Ttum IV. 2012)... and TLE patients exhibit higher functional connectivity in the SPG region than those with NC (Li et al. 2023). The PHG region is the first brain region to show structural and functional abnormalitien Mitiiaatiiisii in patients with EMCI and is directly associated with memory decline (van de Mortel et al., 2021). Reduced functional connectivity in the PCG region is one of the earliest indicators of abnormality in EMCI (He et al.) 2021). and EMCI patients also show significantly reduced functional connectivity in the PCUN region (Xu et al., 2021). The HIP region is also one of the first brain regions to atrophy in patients with SMC (Smith et al. 2012). ) and the pathological changes of this disease are often accompanied by a decrease in bilateral 'HA volume. In summary, the abnormal brain regions identified by our Spike-DB model not only statistically
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FLEE
MILLE
PAOGR
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SMEA
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ANGLE
2.40
0.224
2,75-
PCUNI
2.35
0.220
2.70
Pred
Orig
Pred
Orig
Pred
Orig
Brain region
Figure 3: Visual distribution results of significantly abnormal brain regions in FLF, TLE, EMCI, and SMC patient data. Each brain region corresponds set of violin plots, showing the distribution differences between the patient's original data and the model-predicted data in these brain regions, and these differences statistically significant (p 0.05).
significant but also well-supported by existing clinical and imaging evidence, further validating the model's 's reliability in identifying abnormal brain regions.
3.5
PERTURBING THE TRAINED SPIKE-DB INFER EFFECTIVE CONNECTIVITY (RQ2)
3.5.1 GAUSSIAN PULSE-BASHE BASED PERTURBATION STRATEGY
After training, we perturb each input region of Spike-DB to infer global effective connectivity (EC) (Luo et al.) 2025). Perturbation involves selectively increasing the signal of specific region at time [0, 7J while leaving other regions unperturhed. The EC from region quantified the difference in model predictions between the perturbed and unperturbed conditions:
other regions
EC; 8(X A e.) 8(X)
(11)
where e; unit vector, with value of the i-th brain region and elsewhere, representing the perturbation in the i-th region. FC; > 0 represents excitatory connectivity, and the opposite rep- resents inhibitory connectivity. represents the perturbation strength. Because Spike-DB operates on the spike space, conventional perturbation methods difficult to apply directly. To this end, we designed a perturbation strategy based on Gaussian pulses (Breakspear) 2017). This introduces local, smooth, and amplitude-conture Gaussian pulse signal into the fMRI spike time series to simulate the impact of instantaneous perturbations on brain activity. Its mathematical form is:
(T-to)²
= le exp(
(12)
2a
where &s is the disturbance amplitude (set to 1), to is the pulse center time (set to F) and a controls the pulse width (set 20).
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FLE
TLE
NC-E
EMCI
SMC
NC-A
Visualization of the ECs inferred from six pre-trained models (ŌFLE, ÔTLF. òSMC. δEMCI, ÔNC-E. and ANC-A), with one representative subject randomly selected from each dataset. Red lines indicate excitatory connections, and blue lines indicate inhibitory connections.
Figure 4:
3.5.2 PERTURBATION-DRIVEN EC ANALYSIS
In this experiment, we visualize the ECs inferred from various data types using six pre-trained models (ÓFLE, ÖTLE, 8SMC, ÕEMCI, ōNC-E, and ôxc-A). Figure [ displays the ECs of one representative subject randomly selected from each data type. Observations are follows.
First, both the epilepsy and ADNI datasets, the patient groups (FLE, TLE, EMCI, and SMC) exhibit significantly fewer ECs than their corresponding normal controls (NC-E and NC-A). Second, in the normal controls, the number of excitatory connections significantly outnumbered inhibitory connections, whereas in the patient groups, the reverse pattern observed. We speculate that these differences may reflect significant changes in the functional connectivity patterns of brain networks, particularly imbalance in the ratio of excitatory to inhibitory connections. Notably, this imbalance may indicate a decrease the efficiency and stability of neural network information processing. Related research supports this view. For example, McCormick Contreras (2001) suggested that an imbalance between excitatory and inhibitory influences may cause epileptic seizures. Using multiple learning algorithms, Ji et al. (202I) and [Huagg et al. (2022) quantify ECs and show that the number of connections in the NC group is significanlly greater than that in the Alzheimer's s disease (AD) group. These experimental results demonstrate that our Spike-DB can not only identify effective connectivity patterns, but also further verify the close relationship between the imbalance of rectation-inhibition balance brain connections and the occurrence and development of diseases.
3.6 ABLATION STUDY (RQ3)
To validate the effectiveness of the key components of the proposed Spike-DB model, design and conduct two sets of ablation experiments on the six data types. Specifically, we remove the spiking neuron layer from all components, which not only eliminates the spiking coding mechanism but also degenerates the Spike Transformer into regular Transformer. The results are shown in Figures First, we compare the model's performance with and without the spike mechanism. The results show that removing the spike mechanism significantly degrades the prediction performance of Sp in both R² and RSE. This demonstrates that spike representation plays key role in capturing fMRI temporal dependencies (Lv et al., 2024) and dynamic brain activity patterns (Shibo al. 2025), and is essential for the model to learn functional brain features effectively.
Next, we gradually reduce the number of anchor regions K to assess its impact on model perfor- mance. Experimental results show that as K decreases, overall model performance declines. In particular, when the number is less than half (i.e., K < 45), model performance deteriorates signifi- cantly, with a substantial decrease in R and notable increase in RSE. worth noting that when K = 70, the average value of R is 0.949, and the average value of RSE is 0.214. A comparison with Table I shows that Spike-DB still outperforms BrainLM and BrainMass. This result suggests that appropriate number of anchor regions is crucial for effectively constraining the model's 's learning process, and too few anchor regions weaken the model's 's ability to capture interactions between brain regions.

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14-
84
RI
RSE.
44-
6.2-
Nonder secioor regines
1
20
1T.
TTE
NC-E
ENICE
SMC
NC-A
11.
NCE
EMCT
SMC
NCAA
TL
SCE
EMGC
nik
sc
Spikec
Sysker
Figure 5: Left: Comparison of model performance when retaining the spike mechanism (v) and removing the spike mechanism (x). Right: Effect of different numbers of anchor brain regions K on model performance.
4 CONCLUSION
In this work, we develop the Spike-Based Digital Brain (Spike-DB), a novel fundamental model for brain activity analysis. Spike-DB introduces the spike computing paradigm to brain time series modeling to simulate the dynamic characteristics of biological neural systems. This model not only enables highly accurate predictions of brain activity but also assists in brain disease classification, identifying abnormal brain regions, and inferring effective connectivity. Through extensive exper- iments on epilepsy and ADNI datasets, we validate Spike-DB's advantages over existing methods and demonstrate its excellent generalization across a variety of tasks. Our method promises that spike-based digital brain modeling will open new avenues for clinical diagnosis and brain science research.
ETHICS STATEMENT
The experiments on the epilepsy dataset are approved by the Ethics Committee Nanjing Uni- versity Jinling Hospital. All experiments are conducted in accordance with relevant guidelines and regulations. All participants provide written informed consent before participation.
ACKNOWLEDGMENTS
This study supported in part by the National Natural Science Foundation of China (No. 62471259, 62472228, 62376123), and the Natural Science Foundation of Jiangsu Higher Educa- tion Institutions of China (No. 24KJB520032).
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A APPENDIX
A.1 SUBJECTS AND DATA PREPROCESSING
In this study, we conduct experiments using the publicly available Alzheimer's Disease Neuroimag- ing Initiative (ADNI) d and a collected epilepsy dataset. The two datasets are described below: ADNI dataset: We download IMRI data of 203 subjects from ADNI 2 and ADNI 3 datasets, comprising normal controls (NC), patients with significant memory concern (SMC), and 60 patients with early mild cognitive impairment (EMCI). Epilepsy dataset: This dataset contains 103 patients with frontal lobe epilepsy (FLE), 89 patients with temporal lobe epilepsy (TLE), and 114 NCs. All subjects collected using a Siemens Trio MRI scanner. fMRI scans are acquired using single-shot, gradient-echo planar imaging sequence. Scan parameters
recruited at Jinling Hospital, Nanjing University Medical School. Data
follows: repetition time 2000 ms, echo time = 30 ms. flip angle = 90", 30 transverse slices, field of view (FOV) 240 x 240 mm? slice thickness mm, interstice gap 0.4 mm, voxel size 3.75 x 3.75 3.75 mmm
We use SPM12 version 2.0 of the DPARSF toolkit to preprocess all fMRI data in the ADNI and epilepsy datasets. First, the initial functional time series slice-time acquisition corrected, re- aligned, and normalized to the echo-planar imaging (EPI) template. Subsequently, detrending is performed to eliminate sources of spurious variance (i.e., six head motion parameters, averaged signals from cerebrospinal fluid and white matter, and global brain signal), followed by bandpass filtering (0.01 to 0.08 Hz) on the time series data. Finally, the data are divided into 90 regions of interest (ROIs) based the automatic anatomical Labeling (AAL) template. The functional data for each subject in the epilepsy dataset contain 240 time points, and the functional data for each subject in the ADNI dataset contain 197 time points.
A.2 PARAMETER SETTINGS
The proposed model and algorithm implemented based on PyTorch and snnTorch (Eshraghian et al., (023), and trained on a single GPÚ (NVIDIA RTX 4090 with 24GB memory). The pre- training parameter settings shown in Table
Table Pre-training settings.
Config
Value
training epoch
100
threshold potential Var
decay speed Tr
0.9
time constant Ti..
time constant Tx dropout
0.1
embedding dimension D
64
multi-head attention
encoder layer
6
predictor layer
layer index L optimizer
3
AdamW
8 = 0.9
optimizer momentum
82 = 0.999
learning rate
1 10
EMA schedule
linear
EMA start momentum
0.996
EMA final momentum
hhups://admi. www..oss..cc.o usc..du/
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Table 4: Results of fMRI time series prediction task achieved by all methods the additional AMRD dataset (i.e., MD and NC-M data types), averaged over five independent runs. 1: the higher, the better; the lower, the better. The best results are in bold, underlined indicate significant improvements over previous methods (p 0.05).
Method
Metric
MD
NC-M
R²
0,936
0.931
BrainLM
RSE
0.2531027
0.262a.031
R3
0.945
0,9462.036
BrainMass
RSE 4
0.234
0.232a.052
R3 1
0.95 1388
0.9591.026
Brain-JEPA
RSE +
0.222+.012
0. 0.203+.027
R2
0,957
0.966=.017
Brainnymphony
RSE
0.205
08183..056
R² f
0.973 0.964
Spike-DB
RSE
0.164..010 0.1901.00
A.3 EVALUATION METRICS
For the evaluation of the fMRI time series prediction task, we use the relative squared error (RSE) and the coefficient of determination (R3), calculated as shown in Eq [3 and [4] For the brain disease 2TP
TP-TN classification task, we report the accuracy (ACC = TP-TN-FP-EY and Fl score (F1 = 2TP+FP+FN ) where FP, TP, FN and TN represent false positive, true positive, false negative, and true negative values, respectively.
M
1m
m=
RSE
(13)
m=
M |
Y112
M C 1
(Y Yom (Y Y.,)²
n² MCL m==
ΣΣΣ 1=1
(14)
In these equations, M represents the number of samples in the test set, C' is the number of channels, and L is the prediction length. The true values for the m-th sample are denoted by y". and their average over all samples is Y. Specifically, Y denotes the I-th future value of the c-th variable for the m-th sample, Ye, is the average of Y across all samples. The predicted values corresponding to these true values are denoted by ÿmm and ym respectively.
Compared with the mean square error (MSE) and mean absolute error (MAE), RSE and Ra are more robust outliers or values with large absolute values in the dataset, and therefore widely used in time series forecasting tasks.
A.4 RESULTS OF ADDITIONAL DATASETS
To further verify the robustness of Spike-DB, we expand the experiment by incorporating larger dataset. This dataset is sourced from the Decoding Neurofeedback Project organized by the Agency for Medical Research and Development (AMRD) in Japan, encompassing 3T MRI data (resting- state fMRI EPI images, corrupted T1-weighted images, and field maps) from 1410 subjects (620 patients and 790 normal controls). We extract resting-state IMRI data from this dataset, covering 230 time points, and uniformly categorize the subjects into discase-related and disease- free groups. Ultimately, the dataset includes 849 subjects, of whom 384 are multi-disease (MD) patients and 465 are normal controls (NC-M). Data preprocessing is consistent with the epilepsy and ADNI datasets.
Table 4 reports the results of Spike-DB on the AMRD dataset for fMRI time series prediction. As shown in Table 4 Spike-DB maintains strong predictive power on large-scale datasets, with R val- ues of 0.964 and 0)973, and significantly lower RSE value than other methods. Secondly, Table 5
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Table 5: Results of brain disease classification achieved by all methods the additional AMRD dataset (i.e.. MD vs. NC-M), averaged over five independent runs. The best results are in bold, underlined indicate significant improvements over previous methods (p 0.05).
MD vs. NC-M
Method
ACC(%)
FI(%)
BrainLM
74.69#3.39
73.89+284
STCAL
77.7813/10
72.731197
BrainMass
80.8624.91
74.1412.99
Brain-JEPA
82.08=262
77.37+2.06
BrainSpmphony
83.33=1A1
78.05s4.17
Starformer
83.8242.59
81.0823.09
Spike-DB
84.09s2.64
85.12
reports the results of Spike-DB on the AMRD dataset for brain disease classification. Specifically, Spike-DB achieved an accuracy 84.09% and an Fl score of 85.12%. both superior other meth- ods. This indicates that Spike-DB not only performs excellently in fMRI time series prediction but also achieves leading position in brain disease classification, demonstrating its superior ability and consistency in handling large-scale datasets. This further validates the broad applicability and efficiency of Spike-DB in multiple brain disease tasks.
Output neurons
Neuron index
Predicted class
6
11
2
0
3
L0.
arg max, 9=1
Figure 6: Classification method in spike space.
A.5 DETAILS DOWNSTREAM CLASSIFICATION TASKS
Unlike the main fMRI prediction task, which divides data into six data types, the training data used in the downstream task mainly includes four groups: FLE vs. NC-E, TLE vs. NC-E, EMCI vs. NC-A, and SMC vs. NC-A. To transition from a regression task to classification task, we modified the model architecture, using the same encoder part and adding classification head on top of it. Specifically, fMRI data is first encoded into spike trains using spiking neuron layer, then pre-trained using an encoder (anchor, target, and predictor), and used as a feature extractor for the downstream task. Finally, these extracted spike features are input into the classifier for brain disease classification.
Regarding the choice of classifier, since the main framework is in the spike space, traditional classi- fiers are no longer applicable. We will describe a vectorized implementation method for determining the predicted class based on the output spike sequence. Specifically, let ž E R represent the spike fired by each output neuron time, where is the number of output classes. Let ē R be the spike count for each output neuron, which can be obtained by summing Z|t] over T time steps:
T
(15)
t=0
the index of with the maximum count corresponds the predicted class ÿ:
G = arg max es
(16)
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To facilitate understanding, provide example in Figure 6 Neuron fires 6 spikes, Neuron 2 fires spikes, and Neuron 3 fires 1 spike. Compared to Neurons and 3, Neuron fires the most spikes, corresponding to predicted class of 1. For the binary classification task, we used the classification cross-entropy loss function Le:
Le - log + (1 1)) log(1 - y))
(17)
where y the true class.
A.6 MODEL COMPLEXITY COMPARISON
As shown in Table 6 the Spike-DB method demonstrates significant advantages in terms of pa- rameter count training time by introducing spiking mechanism. Compared to other methods, Spike-DB has only 2.7 M parameters, far lower than Brain-JEPA (22.0 M) and BrainMass (14.4 M), while the training time per epoch is only 96.73 s, the shortest of all methods. This is thanks the introduction of the spiking mechanism, which effectively reduces computational complexity and memory consumption by simulating the spiking firing pattern neurons, allowing Spike-DB to significantly improve training efficiency while maintaining performance.
Table 6: The total number of parameters for all methods and their average training time per epoch (based on the average of six data types: FLE, TLE, NC-E, EMCI, SMC, and NC-A).
Method
Parameters (M)
Training time (s)
BrainLM
13.0
163.59
BrainMass
14.4
284.65
Brain-JEPA
22.0
127.81
BrainSymphony
2.0
114.18
Spike-Di DB
2.7
96.73
17


Reviews:
[{"review_id": "C9JJFBNHUv", "reviewer": "Reviewer_UykD", "summary": "This paper proposes Spike-Based Digital Brain (Spike-DB), a novel computational framework designed to model brain activity by integrating spike-based neural computation with fMRI time-series prediction. The method encodes continuous fMRI signals into discrete spike trains using an IIR-based spiking neuron model, aiming to capture temporal dependencies more effectively and in a biologically inspired manner. Spike-DB learns to predict the activity of target brain regions from selected anchor regions, enabling a self-supervised learning paradigm that mimics information flow in the brain. The trained model is further applied to several downstream tasks, including brain disease classification, abnormal brain region identification, and effective connectivity inference.", "questions": "1. Biological Implausibility of Spike Conversion:\nThe transformation of low-temporal-resolution fMRI BOLD signals into high-frequency spike trains is not physiologically realistic. Since fMRI measures slow hemodynamic responses rather than actual neural spiking activity, this conversion undermines the biological credibility of the proposed “spike-based” framework and weakens the paper’s neuroscientific claims.\n\n2. Limited Reproducibility:\nThe paper lacks publicly available code, pretrained models, and detailed data preprocessing information. Without these resources, the reported results cannot be independently verified, which significantly limits the work’s transparency and scientific reproducibility.\n\n3. Restricted Evaluation Scope:\nThe experiments are confined to two relatively small datasets (epilepsy and ADNI), without cross-dataset or large-scale validation. This narrow evaluation raises concerns about the model’s robustness, risk of overfitting, and generalization to broader neuroimaging domains.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper presents an interesting idea of using spike-based computation for modeling brain activity, which offers a fresh perspective compared to standard fMRI analysis methods. The proposed framework, Spike-DB, is technically sound and reasonably well explained, with clear figures and equations that make the approach understandable. The authors also provide experimental results showing small but consistent improvements over existing baselines, suggesting that the method has potential.", "weaknesses": "The main weaknesses of this paper stem from issues of biological plausibility, reproducibility, and evaluation scope. Most notably, the conversion of low-temporal-resolution fMRI BOLD signals into high-frequency spike trains is conceptually problematic, as fMRI data reflect slow, indirect hemodynamic responses rather than true neural spiking activity. This makes the biological interpretation of the model questionable and undermines its claim of being a biologically inspired “digital brain.” Additionally, the study lacks reproducibility, since no implementation code, pretrained models, or detailed preprocessing procedures are provided, making it difficult for others to verify the reported results. Finally, the experiments are confined to two relatively small datasets (epilepsy and ADNI), limiting the model’s generalizability and raising concerns about potential overfitting and the robustness of the findings.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "TNJJrCPKcc", "reviewer": "Reviewer_us19", "summary": "This paper presents a new fundamental model, the Spike-based Digital Brain (Spike-DB), which addresses the limitations of continuous-valued frameworks in brain modeling. A key contribution of this paper is the first-time application of the spike computing paradigm to fMRI time series analysis. The model converts blood oxygenation level-dependent signals into spike trains using an IIR filter-based SNN and is trained via a self-supervised, predictive mechanism (learning from 'anchor' to 'target' regions) on a Spike Transformer architecture.\n\nThe authors validate Spike-DB using epilepsy and ADNI datasets, demonstrating state-of-the-art (SOTA) performance in both fMRI time series prediction and brain disease classification when compared against several leading fundamental and specialized models. The model's clinical utility is further explored through downstream tasks, where it successfully identified abnormal brain regions and inferred effective connectivity patterns that were consistent with existing medical research.", "questions": "- Q1. How can we be certain that the model's superior performance truly stems from being 'closer to the biological nervous system', and not simply from the IIR filter-based SNN acting as a highly effective non-linear feature extractor for the specific temporal dynamics of BOLD data? I'm curious about the authors' opinions.\n- Q2. To better assess the practical trade-offs, could the authors provide a comparison of computational costs (e.g., training time, resource usage) against the baselines? This information would be a valuable addition for evaluating the model's real-world applicability.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is well-written, making it easy to follow and understand.\n- The authors define clear research questions (RQs) and validate claims thoroughly via SOTA comparison (RQ1), clinical interpretation (RQ2), and ablation studies (RQ3).\n- The paper's novel contribution, being the first to apply spike computing to fMRI modeling, is not merely a conceptual claim. The authors successfully validate this approach by demonstrating that their proposed paradigm, when practically embedded in the Spike-DB model, leads directly to new SOTA performance over existing methods.\n- The model shows meaningful clinical utility by identifying abnormal brain regions and connectivity patterns that are well-supported by existing medical research.", "weaknesses": "- The idea of converting slow, hemodynamic fMRI signals into discrete, fast neural spikes may lack sufficient biological justification and could be conceptually problematic.\n- The model's claimed flexibility (implied in Fig. 1) is contradicted by its reliance on an extreme parameter setting. Optimal performance is achieved when $K$ = 89 out of 90 total ROIs, leaving 'only one target region'. This suggests the model is highly optimized for a very specific 'predict-one-from-all' task, not a general masking strategy. This high sensitivity is confirmed by the ablation study, which shows performance 'deteriorates significantly' as K decreases ($K$ < 45, as shown in subsection 3.6), raising concerns about the model's practical flexibility.\n- To fully understand the practical trade-offs of the proposed method, an analysis of the computational overhead is required. The Spike Transformer and SNN layers are typically resource-intensive when simulated on standard GPUs (like the RTX 4090 used) compared to the standard Transformer baselines. A discussion or comparison of training time and resource usage was not included. This analysis would be valuable in future work, as it would provide a more complete picture of the costs associated with the SOTA performance reported in Tables 1 & 2.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "zn6rUz91bC", "reviewer": "Reviewer_94Co", "summary": "This paper introduces Spike-DB, a framework that converts fMRI time series into discrete “spike” events and trains spike Transformer with a masked-region prediction objective. The learned embeddings are then used for downstream tasks such as detecting abnormal brain regions and classifying neurological diseases. Experiments on two disease datasets show improvements over selected baselines, and ablations aim to assess the contribution of the spike encoding were performed.", "questions": "* Is the train/test spilt subject-based or data-based? What was the training data used the downstream classifiers? What is the training procedure for downstream tasks? If the same training data was used in the pretrain and decoding, how does an end-to-end model trained directly for the downstream task (without the spike pretext stage) perform relative to Spike-DB?\n\n* How exactly was the “no spiking” ablation implemented? Did you remove only the spike encoding while keeping the Spike Transformer?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "Framing fMRI analysis as digital spiking signal is a fresh and interesting direction.", "weaknesses": "* Because fMRI measures BOLD signals rather than direct neural spiking, the biological interpretability of the discrete spikes remains unclear. It would be valuable to connect the event representation to known hemodynamic dynamics or evaluate on a modality closer to neural spiking (e.g., iEEG).\n\n* The need to train separate embedding models for frontal lobe epilepsy, temporal lobe epilepsy, different levels of cognitive impairment, and healthy controls for each dataset suggests limited cross-condition generalization. This raises concerns about whether the method captures broadly generalizable biological features versus dataset- or subtype-specific cues.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "N1JeOT9BXN", "reviewer": "Reviewer_dTth", "summary": "This paper proposes **Spike-DB**, a spike-based computing framework for modeling fMRI data. The model encodes fMRI signals into spike trains through a differentiable integrate-and-fire mechanism, applies a masked region prediction strategy to learn inter-regional dependencies, and decodes the resulting spike representations for reconstruction and analysis. Spike-DB achieves better performance than the compared state-of-the-art methods on data prediction and brain disease classification tasks. It also provides interpretability analyses, including abnormal region detection and effective connectivity mapping. The framework represents an attempt to unify spiking neural computation with brain modeling. However, its biological justification for applying spike encoding to slow fMRI signals is limited, and its evaluation on only two datasets constrains generalization.", "questions": "1.How do you justify applying spike-based encoding to slow fMRI BOLD signals ( around 1 Hz)? Why is this paradigm appropriate for fMRI rather than higher-temporal-resolution modalities like EEG or iEEG? \n\n2.How were τ_m, τ_s, and threshold values chosen? Do they have any neurophysiological grounding or sensitivity analysis?  \n\n3.How does Spike-DB differ fundamentally from earlier fMRI–SNN works (e.g., NeuCube, Kasabov et al., 2016)? \n \n4.Will fix 89 anchor regions and predict only one be affected by fMRI’ high autocorrelation of near region?\n\n5.Please report standard deviations and significance tests to confirm the 1–2% improvements.  \n\n6.What classifier (MLP, linear, etc.) was used? Was the backbone frozen or fine-tuned? What loss and evaluation protocol were applied? \n \n7.Please evaluate Spike-DB on additional public datasets to demonstrate robustness and effectiveness.\n\n8.How does your perturbation-based EC relate to established causal models (e.g., DCM, Granger)? Any validation against known connections?  \n\n9.Will you release code, model weights, and preprocessing scripts to enable replication?\n\nI will raise my score if these issues are well addressed.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. While more biological justification is needed, the idea of using SNN-like processing for brain activity modeling represents an attempt to bridge computational neuroscience and machine learning.  \n\n2. The proposed Spike-DB framework is clearly structured and includes explicit mathematical formulations, making it easy to follow.  \n\n3. The manuscript is well organized and features high-quality figures.  \n\n4. The proposed Spike-DB model has been tested on diverse tasks.", "weaknesses": "1. The paper’s core claim is to “encode fMRI signals as spike trains.” However, fMRI BOLD signals are slow hemodynamic measures (around 1 Hz), not neural spikes (around 10-1000 Hz). Compared with fMRI, which has low temporal resolution, spike-based computing is more suitable for modalities with high temporal resolution, such as iEEG or EEG. How do the authors justify the biological meaning of such a design when applied to fMRI data?  \n\n2. The model uses generic spike encoders and decoders without linking parameters (τ_m, τ_s, thresholds) to realistic neural biophysics or cortical circuitry. Claims of “biological interpretability” are therefore not well supported.\n\n3. The authors claim that they are the first to introduce the spike computing paradigm into brain time-series modeling. However, earlier works have treated fMRI data as spike trains, though in slightly different ways, for example:  \n   [a] Kasabov, Nikola K., Maryam Gholami Doborjeh, and Zohreh Gholami Doborjeh. *“Mapping, learning, visualization, classification, and understanding of fMRI data in the NeuCube evolving spatiotemporal data machine of spiking neural networks.”* IEEE Transactions on Neural Networks and Learning Systems 28.4 (2016): 887–899.  \n   Hence, this claim should be softened.  \n\n4. The authors fix the number of anchor regions to 89 out of 90 total, leaving only one region to predict. This setup trivializes the prediction task, as each region is almost self-predictive given its high autocorrelation.  \n\n5. Minor differences (about 1-2%) are presented as “state of the art,” which is not statistically justified. Standard deviations should also be reported to assess robustness across random seeds.  \n\n6. The paper reports disease classification results based on the learned spike representations, but the implementation details of this task are not clearly described. It is unclear whether a multi-layer perceptron, a linear classifier, or another model was used, and whether the SNN backbone was frozen or fine-tuned during training. The paper also omits details such as the loss function, optimization setup, and evaluation protocol. Clarifying these aspects and reporting variance across runs would strengthen the reproducibility and reliability of the classification results.  \n\n7. Only one public dataset and one collected dataset are used for evaluation, making it difficult to demonstrate the method’s generalization and reliability. More publicly available datasets should be included to evaluate the proposed approach.  \n\n8. The authors replace causal modeling with simple perturbation differences. Whether these results correlate with true causal effects needs to be carefully justified.  \n\n9. Finally, no code, model weights, or preprocessing scripts are provided, which harms reproducibility.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": {}}]

Author rebuttal:

Decision: Accept (Poster)