A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing
Authors: Yu Chen
Jing Lian
Zhaofei Yu
Jizhao Liu
Jisheng Dang
Gang Wang
Venue: ICML 2025 poster (2025)
Keywords: Event Camera
Object Classification
Continuous-coupled Neural Network.
Primary area: deep_learning->other_representation_learning

Abstract: Event cameras are bio-inspired vision sensors that encode visual information with high dynamic range, high temporal resolution, and low latency. Current state-of-the-art event stream processing methods rely on end-to-end deep learning techniques. However, these models are heavily dependent on data structures, limiting their stability and generalization capabilities across tasks, thereby hindering their deployment in real-world scenarios. To address this issue, we propose a chaotic dynamics event signal processing framework inspired by the dorsal visual pathway of the brain. Specifically, we utilize Continuous-coupled Neural Network (CCNN) to encode the event stream. CCNN encodes polarity-invariant event sequences as periodic signals and polarity-changing event sequences as chaotic signals. We then use continuous wavelet transforms to analyze the dynamical states of CCNN neurons and establish the high-order mappings of the event stream. The effectiveness of our method is validated through integration with conventional classification networks, achieving state-of-the-art classification accuracy on the N-Caltech101 and N-CARS datasets, with results of 84.3% and 99.9%, respectively. Our method improves the accuracy of event camera-based object classification while significantly enhancing the generalization and stability of event representation.

A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing
Yu Chen 12 Jing Lian Zhaofei Yu*
Jizhao Liu"!
Jisheng Dang"
Gang Wang"
Abstract
1. Introduction
Event cameras are bio-inspired vision sensors that encode visual information with high dynamic range, high temporal resolution, and low latency. Current state-of-te satte-of-thearre art event stream processing methods rely on end-to-end deep learning tech- niques. However, these models are heavily de- pendent on data structures, limiting their stabil- ity and generalization capabilities across tasks, thereby hindering their deployment in real-world scenarios. To address this issue, we propose a chaotic dynamics event signal processing frame- work inspired by the dorsal visual pathway of the brain. Specifically, we utilize Continuous- coupled Neural Network (CCNN) to encode the event stream. CCNN encodes polarity-invariaal event sequences as periodic signals and polarity- changing event sequences as chaotic signals. We then use continuous wavelet transforms to ana- lyze the dynamical states of CCNN neurons and establish the high-order mappings of the event stream. The effectiveness of our method is vali- dated through integration with conventional classi- fication networks, achieving state-of-the-ar clas- sification accuracy on the N-Caltech101 and N- CARS datasets, with results of 84.3% and 99.9%, respectively. Our method improves the accuracy of event camera-basee based object classification while significantly enhancing the generalization and sta- bility of event representation. Our code is avail- able in https://gghuub..c//cheenna))) ACCDD.
Event cameras, inspired by the three-layer structure of the peripheral retina in primates, are neuromorphic sensors de- signed for silicon-based vision. Each pixel of the event sensor operates independently and continuously to detect changes in light intensity within a scene. When the change exceeds a preset threshold, an event signal is triggered. The event signal encodes spatiotemporal information, including a timestamp, spatial coordinates, and polarity. The unique sampling mechanism enables event stream data with the advantages of high temporal resolution, low redundancy, wide dynamic range, and minimal latency. However, frame- based vision algorithms designed for image sequences are not directly applicable to event data (Gallego et al., 2020).
To address this challenge, event streams are compressed event into frames, producing 2D event frame images via fre- quency accumulation methods (Gallego et al., 2018; Stoffre- gen & Kleeman, 2019; Gallego al., 2019; Almatrafi et al., 2020; Brebion et al., 2021; Hagenaars et al., 2021; Paredes- Vallés & De Croon, 2021; Shiba et al., 2022b). Similarly, a timestamp-based event representation method, known as event surfaces (Mueggler et al., 2017; Lagorce et al., 2017: Sironi al., 2018), updates the latest timestamp information to capture motion changes in the spatial positions of objects. To more effectively leverage the spatiotemporal informa- tion within event streams, spatiotemporal histogram- representation was proposed, called voxel grids (Zhu et al., 2018; Deng et al., 2022; Xie et al., 2022; Baldwin et al., 2022). This method discretizes the time domain and em- plays linearly weighted accumulation to allocate events into corresponding voxels.
"ccooo of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China PNAIVE Lab, Brain Re- search Center, Beijing Institute of Basic Medical Sciences, Bei- jing 100850, China 'School of Electronics and Information En- gineering. Lanzhou Jiaotong University, Lanzhou 730070, China "School of Institute for Artificial Intelligence. Peking Univer- sity, Beijing 100850, China. Correspondence to: Jizhao Liu <li- ujz@ www.ecu..nn...... Jisheng Dang <dangjsh@ www.llsysu.edu.cn... Gang Wang <g_wang @ foxmail.com>>.
With advancements in deep learning and large-scale com- putation, data-driven, end-to-end neural networks (Gehrig et al., 2019; Sekikawa et al., 2019; Wang et al., 2019; Bi et al., 2019; Cannici et al., 2020; Yang et al., 2019; Bi et al., 2020; Deng et al., 2021; Schaefer et al., 2022: Sabater et al.,
2022; Wang et al., 2022) have gained increasing popularity. These methods efficiently exploit the asynchronous spa- tiotemporal characteristics of event streams. Additionally, bio-inspired spiking neural networks (Fang et al., 2021; Li et al., 2022; Shen et al., 2023; Wang et al., 2024) simulate biological spike signal processing mechanisms, integrat-
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing
ing to diverse stimuli and experimental conditions (Groen et al., 2022; Gong et al., 2023). The experimental evidence strongly supports the brain's remarkable ability to maintain stable and generalizable visual processing across different datasets and conditions. By effectively hamesssing the neu- ral processing mechanisms of the visual cortex, we can advance the development of event representation methods with improved generalization and stability.
time
x
Event data
ANN based model
Event image
y
time
In this work, we introduce continuous- coupled neuron network (CCNN) inspired by the primary visual cortex (Liu et al., 2022). The CCNN exhibits electrophysiological prop- erties similar to those of mammalian neuron clusters, gen- erating periodic sequence outputs in response to constant input signals and chaotic sequence outputs in response to varying signals. This input-output behavior enables the network to effectively distinguish between stable events, characterized by constant polarity patterns, and dynamic events, characterized by varying polarity patterns. Lever- aging the unique characteristics of the CCNN, we separate constant-polanee events from varying peyyaittpllarty events within the same sampling period. The separated event sequences are then processed using continuous wavelet transforms (CWT) to extract spatiotemporal information, establishing a high-order mapping from the event stream to event frames. The overall architecture and performance of the framework are shown in Figure 1. To further enhance the accuracy of motion extraction, the framework integrates a deep neural network to achieve precise localization and recognition of moving objects, as shown in Figure
CCNN
CWT
LPF
Event data
Chaotic Dynamics Framework
Event image
(b)
95
86
Accuracy
80
Nethods
EST
EVAGENN
- TORE
M-LSTM
MVF-Net
TOKEN
70
EVI
Ours
65
RG-CNNS
N-Callech101
N-NNIST
ASL-DVS
N-CARS
Dalasets
Figure 1. The architecture and performance of the proposed framework. (a) The chaotic dynamic framework, where event stream data are input into CCNN neurons in the form of coor- dinates. The dynamic characteristics of neurons are analyzed using CWT. and the event representation is subsequently obtained through L.F.. (b) Comparative evaluation of the model's 's classifi- cation performance multiple datasets. The proposed method achieves superior classification accuracy across diverse datasets, demonstrating its strong generalization capability and robust adapt- ability in processing event-basee data.
The main contributions of this work are summarized as follows:
ing pulse signals and sampling events based on their firing times once a predefined threshold is exceeded. These event representation methods are shown in Figure 2.
We propose an event stream processing framework inspired by the brain's 's dorsal visual pathway. We intro- duce the spatial anism of the brain's is dorsal pathway, also known as the "where" pathway, into the event stream data process- ing framework, effectively establishing a high-order mapping from event streams to event frames.
information encoding mech-
However, the above event representation methods exhibit significant limitations in terms of generalization and sta- bility, particularly in their inconsistent performance across datasets, lack of robustness, and strong dependency on spe- cific representational structures. Specifically, these methods often struggle with scenarios involving sparse data or high dynamic range, highlighting their lack of robustness and adaptability. Moreover, most event representation meth- ods are designed to specific representational frameworks, such as spatiotemporal voxel grids or local features, making them less adaptable to diverse event data. These obstacles hinder the widespread applicability of existing methods in real-world scenarios.
This framework utilizes CCNN to encode constant- polarity event sequences as periodic signals and varying-polarinn event sequences as chaotic signals, ef- fectively achieving robust event representation. When combined with traditional deep neural networks, the framework successfully performs object classification for event cameras.
The proposed framework is evaluated multiple datasets, achieving state-of-the-art accuracy on specific benchmarks. It also demonstrates competitive perfor- mance across a variety of datasets. The results demon- strate the framework's 's strong generalization across dif- ferent data structures.
Drawing inspiration from the mechanisms of the brain for processing visual information offers a promising ap- proach to overcoming current obstacles. Recent experimen- tal studies have demonstrated that neural responses in visual processing exhibit consistent patterns, even while adapt-
2

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A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing
2. Related Work
functions affecting event alignment, exploring the impact of different evaluation methods recovering sharp event frames and their performance across various applications (Gallego et al., 2019) (Stoffregen & Kleeman, 2019). Be- yond generating aligned event images, contrast maximiza- tion has also been employed in deep learning as a form of supervision. For instance, The contrast maximization-base self-supervised learning framework has achieved competi- tive results in optical flow estimation (Hagenaars et al., 2021; Paredes-Vale & De Croon, 2021; Shiba et al., 2022b). However, the contrast objective (variance) may overfit the events, which can push the events to accumulate in too few pixels (event collapse (Shiba et al., 2022a)).
(g) Existing mathematics bentmmtttii---sdedeeeee event processing
Event camera
Event data
Math besed method
Event image
h
Existing mod--to-uee and event processing
Event camera
Event data
ANN based model
Event image
id
Brain: inspired ev ext processing (Our
Real scenes
2.3. The Deep Learning-based Methods
time
With the increase in computility, deep learning has rapidly become the dominant approach in asynchronous event data representation. The Event Spike Tensor (EST) is the first data-driven, end-to-end event representation method. It uti- lizes multilayer perceptron (MLP) learn the optimal mapping function, thereby maximizing task performance (Gehrig et al., 2019). Matrix-LSSTM replaces the MLP with an LSTM, leveraging temporally accumulated pixel informa- tion to construct a 2D event representation, thereby further optimizing the learning framework (Cannici et al., 2020). To fully leverage the sparsity and asynchronicity of event data, graph-based representation methods utilizing graph neural networks have been proposed (Xu et al., 2018; Bi et al., 2020; Schaefer et al., 2022; Deng et al., 2022; Wang et al., 2024). These methods process event data in the form of a temporally evolving graph, efficiently maintaining both sparsity and high temporal resolution. Dense event repre- sentations based on convolutional neural networks (CNNs) achieve superior task performance; however, they are com- putationally intensive, which limits their practical deploy- ment. In contrast, sparse event representations leveraging graph neural networks (GNNs) enhance efficiency by ex- ploiting the spatiotemporal characteristics of asynchronous events, although they typically exhibit lower accuracy and are constrained terms of application scope.
camer
Event data
Brain-insjired network
Event image
Figure The review of representation of asynchronous events. Existing methods can be classified into three categories: mathematical-bban methods, end-to-end processing models based on ANNs, and brain iain-inspeeed networks.
2.1. The Frequency Accumulation Methods
Prior event representations were primarily task-specific methods based on mathematical models. For instance, the surface of active events (SAE) encodes linensiimenion event streams into frame images based on timestamps to capture event motion trajectories, demonstrating promising results in corner detection (Mueggler et al., 2017). Simi- larly, the concept of the "distance surface" was introduced, where pixel intensities are derived as proxies based on the distances of event points to motion edges, and applied to optical flow estimation (Almatrafi et al., 2020). Building on the "distance surface," " inverse exponential calculations were incorporated, resulting in novel dense "inverse ponential distance surface" representation that addresses noise sensitivity and unbounded influence regions (Brebion et al., 2021). However, these event representations typically adopt a frequency accumulation approach, often resulting in blurred event edges. Therefore, event alignment required during the process of converting event streams into event frames.
3. Methods
3.1. Event Field
2.2. The Contrast Maximization Methods
The event streams generated by event cameras can be con- sidered as point sets in three-dimensional space, where each event point is represented as four-dimensional data. The event point consists of spatial coordinates x and y, polarity, and timestamps. Inspired by (Gehrig et al., 2019), this point set is represented by the following equation:
Contrast maximization serves as an effective method ad- dress image blurring. It maximizes an evaluation function to assess the alignment of event edges caused by object mo- tion, resulting in clear event frame images through motion estimation. Based on this, a unifying contrast maximiza- tion framework is proposed for motion, depth, and optical flow estimation with event cameras. (Gallego et al., 2018). Subsequently, researchers classified and investigated reward
E(I,V.P.
Σ 8(1-Inn Inn -- Un Pm (o))((tt,)) (1)
e, E6
3

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A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing
Event stream
CCNN
Dorsal Stream
Time
t1
t2
n-2
t n-
Ventral Stream
Classification
airplane
lamp
pyramid
watch
Dense
Avg Pool
Conv2D
Max Pool
Figure 3. The human brain's visual cortex recognizes moving objects through the dorsal and ventral pathways. Event cameras mimic three-layer structure the peripheral retina humans, encoding moving objects into event stream data. utilize chaotic dynamics framework, based CCNN, to map the event stream data into event representations inspired by the dorsal stream. Subsequently, the event representations are sent from the MT area to the IT area, where recognition of moving objects is achieved through multiple layers of neural networks.
The event point set continuously represented by the func- tion Exx, y, p, t). Each event point in the event stream is represented by 8(-) to capture its spatiotemporal informa- tion and polarity. In finee-dimensional space E, event point (In, Un Po, (n) generates Dirac impulse when its spatiotemporal information and polarity match. This indicates the occurrence of the event.
where x; E 10,,,,,.......... the resolution of the event frame, Ps {-1, denotes the polarity of the event. ta E {to +N At)]. where to is the starting time, N is the number of event bins, At is the time interval, and is the adjustment factor. For the fixed sampling method, remains constant, whereas for adaptive sampling, is adjusted dynamically based on specific requirements.
E (0, 1,2, .. represent
3.2. Dorsal Pathway-Inspired Event Representation
Each event point in the event stream data contains the po- sition coordinates (2r, y) of the moving object, timestamp t, and polarity p. In this work, the coordinate information is designated as the key, while the polarity sequence corre- sponding to the same coordinate serves as the value. After aligning all values, they are uniformly input into the CCNN. Different polarity variation sequences result in different types of output signals.
Sampling. When processing three-dimensional event stream data, sampling is the primary step and can gener- ally be categorized into two categories: fixed sampling and adaptive sampling. The sampled event bins are expressed as follows:
El [14, Uý
te] = Σ 8(1; In. Uj Pn.Pk Pr.)
V(en.) F ((xr,1);)))
= {pp, tx) [x,, My Pk. til E
(3)
8(1 tn),
where F(.) represents the mapping function, (a,, yy) de-

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A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing
notes the spatial coordinates used as keys, and (ee)) repre- sents the polarity timestamp sequence corresponding to the coordinates, used as values.
Anglhede
Amplied
200
500 Steretiees
200
343 Excentioon O3
300 Elerntioon 60
99
Sgrol
20
530
10
333
50
50
Auppitdd
0.7-
Amplitdde
04
25
0.5
0.5
0-
1
11
0.25
60
69
Figure S. Waveform and phase space plot CCNN neuron. (a) Waveform of U. (b) Waveform of E. (c) Waveform of Y. (d) Phase space plot of U-F plane. (e) Phase space plot of U-Y plane. (f)
Time
(A)
(b)
Figure 4. The input-output characteristics of the CCNN. (a) When stimulated by event signals with changing polarity, the CCNN generates chaotic sequenees as output. (b) When stim- ulated by event signals with constant polarity, the CCNN produces periodic sequences as output.
Phase space plot of E-Y plane.
According to equation (4), each update of E(k) is influ- enced by (A), making it impossible to represent using a general mathematical equation. Consequently, the stimula- tion of periodic signals induces unique dynamic behavior in the CCNN model. Figure 5 illustrates the waveforms and phase space plots of the CCNN neuron under square wave signal stimulation, demonstrating its complex dynamic characteristics.
Continuous-couples Neural Network. When inputting all mapped polarity sequences within the same sampling period into the CCNN, a non-coupled CCNN is chosen for simplicity. Its mathematical model is described as shown in the following equation:
(kk) - UKk - 1) + V(ex)
Nonlinear analysis methods are subsequently utilized to investigate the dynamic behavior of the CCNN, with the equilibrium point of the model defined as follows:
1
Y(k) (U(k)
(4)
E(k) = - -c--aEEk-1 1) + VgY(k-1) 1),
E(k + 1) = E(k)
where U is an independent variable influenced solely by the external input V. which in this work corresponds to the polarity sequence at a specific coordinate.
E(K)) (1+e-(00-- -FA)))
VE
(8)
c-
Using the Taylor series expansion of e", the above equation is simplified, resulting in the following simplified equation (9):
When the polarity varies uniformly, the general term equa- tion of (k) is expressed as:
Ukk) = V 1 a
1 e-e-kay
(5)
E(K) - - -)){())) - 1- = 0.
VE
(9)
Through derivation, the expression for period is obtained as:
Since VE > 0, ae > 0, and 4VE(1 - E Ea) > 0, the discriminant A = (U(k) 2) + AVE(1 E E > 0. In this case, E(k) can be expressed Es:
k=1+ 1 In as V- (1---1))) - In
V
[I-E de E 1))
Vi
Since (kk is an inde- pendent variable, equation (4) represents a two-dimensional discrete dynamic system. Using nonlinear analytical meth- ods, it derived that the system has two equilibrium points, which can be expressed as:
U(k)-
(6)
Thus, CCNN neurons output a periodic sequence Ykk) un- der constant stimulation, with the frequency of the period determined by the intensity of the input stimulus.
U(k)= = e - sin (kT) - are kas cos ( (k-T)
When the polarity varies periodically, V(ex) =
{0, 1, 1, 1.. 0,1} = (+((-)) = sin (KT)), The general term equation for (k) is given as:
1 a3
2
E(k) U(k) - ((U(K) 22) + (1- (10)
2
e ko sin (xT))
aje kas cos (KT)))
((k)= =
2
1 1+ a3
(7)
Y(k) = 1 -
5

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A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing
Therefore, the CCNN neuron generates chaotic sequence (k)) under periodie stimulation. While the processing of event signals by the CCNN is illustrated as Figure 4.
where Siy represents the sum of the real parts of all elements in the coefficient matrix.
Low-pass Filter. The constant polarity sequence and the changing polarity sequence are processed through the CCNN and CWT, resulting in values distributed on either side of the zero point. To accurately reflect the position of the moving object, a linear low-pass filter is used to extract effective event points as the pixel points of the event frame. Its expression is given by the following equation:
Continuous Wavelet Transform. In the CWT, after extensive experimentation, the Gaussian wavelet is chosen as the basis function. It is derived from the translation and scaling of the Gaussian function, as shown in equation (11);
1 da = V
b
(11)
255, -100 0,
S <<
where (.) is the Gaussian function, t is the input signal, and is the standard deviation controlling the function's 's width. (t) denotes the Gaussian wavelet, with a as the scale parameter and b as the translation parameter determining its position.
H())
10
(14)
f> 0.
We set the coordinate points less than zero to 255 and those greater than zero to O. This processing approach not only filters out irrelevant information but also preserves the most critical dynamic changes in the event stream, resulting in a clearer and more accurate event frame F(x.y)
DCNN Amp Sigral
Amp AMwwwwwAA
10
M N
F(x,y) = Σ
Sij H(J)
(15)
4. Experiment
40.
10
20
30
10
20
34
Dataset. We validate the stability and generalization of the proposed event representation method on four object classification datasets: N-MNIST (Orchard et al., 2015), N-Caltech101 (Orchard et al., 2015), N-CARS (Sironi et al., 2018), and ASL-DVS (Bi et al., 2019). Among these, N-MNIST dataset, a spiking version of the Frame-based MNIST, contains 60000 training and 10000 testing sam- ples (28x28 pixels). It was generated by capturing event streams with an ATIS sensor mounted a motorized pan- tilt unit. N-Caltech 101 dataset, derived from Caltech101, includes 8677 samples across 101 categories, with each cat- egory containing 40~~800 samples (approximately 300x200 pixels). N-CARS dataset is a real-world event-based car classification dataset with 12336 cars and 11693 non-car samples, recorded using an ATIS camera capturing 100 ms events. ASL-DVS dataset comprises 100800 samples across 24 ASL letters (excluding J), with each 100 ms sample recorded using DAVIS240c event camera in a controlled office environment.
Time (4)
Time (b)
Figure 6. The heatmap of the real part of the CWT matrix. (a) The real part the CWT matrix corresponding to the chaotic sequence. (b) The real part of the CWT matrix corresponding to the periodic sequence.
In the selection of the scale parameter for wavelet transform, we monotonically increased the scale parameter starting from 1, and ultimately chose a scale range of 10.
CWT(a, b) = (1) @ (1a,(())
1
/ Y(1e
2 20 dt,
(12)
V2TaO
where (() represents the output of the event polarity se- quence processed by the CCNN, represents the convolu- tion operation.
An empirical analysis reveals that for periodic sequences, the real parts of the wavelet coefficients are predominantly negative, whereas for chaotic sequences, they exhibit the opposite trend. The results, shown in Figure 6, demon- strate that summing the real parts of all wavelet coefficients effectively distinguishes between sequences with polarity changes and those without.
Experimental Details. For each dataset, we employed a ResNet-34 architecture pre-trained on the ImageNet dataset. The data was split into training. validation, and test sets in a ratio of 3:1:1, with the random seed set to 2024. The model was trained for five epochs with batch size of 16. During optimization, we used the cross-entropy loss function and the Adam optimizer with an initial learning rate of 1e 4. To mitigate overfitting, we introduced a dropout layer before the fully connected layer and incorporated an carly stopping mechanism during training. When the validation loss ceased
10 k
S
Re(cwt(a;; b,)).
(13)
6

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A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing
75-
25-
-25
-55
-75
-75
-100
-75
-50
-2
0
25
50
75
-75
-50
25
25
75
100
(a)
(b)
Figure 7. Semantic distance map. (a) Semantic 2D vector distribution of N-MNIST. (b) Semantic 2D vector distribution of ASL-DVS.
Table
Classification accuracy on various datasets. Spike-
Table 2. Model complexity of different methods on object clas-
based, Voxel-based, V Frame-based, * Ours.
sification. Here, we report average inference time on N-Cars.
Method
N-M
N-Cal
N-Cars
ASL
Method
Params(M) MACs(G) Time(ms)
NDA (Li al.)
78.2
PointNet++ (Qi et al.)
103.9
VPT-STS (Shen et al.)
79.2
95.8
RG-CNNs (Bi et al.)
19.5
0.8
GIN (Xu et al.)
75.4
47.6
84.6
51.4
EV-VGCNN (Deng et al.)
0.8
0.7
7.1
EventNet (Sekikawa et al.)
75.2
42.5
75.9
83.3
VMV-GCN (Xie al.)
0.8
1.3
6.3
RG-CNNs (Bi et al.)
65.7
91.4
EST (Gehrig et al.)
21.4
4.3
6.4
EV-VGCNN (Deng et al.)
99.4
74.8
95.3
98.3
M-LSTM (Cannici et al.)
21.4
4.3
6.4
VMV-GCN (Xie et al.)
99.5
77.8
93.2
98.9
MVF-Net (Deng al.)
33.6
5.6
10.1
TORE (Baldwin al.)
99.4
79.8
94.5
99.9
Ours
21.9
3.7
2.1
HATS (Sironi al.)
99.1
64.2
90.2
EST (Gehrig et a
99.0
75.3
91.9
97.9
demonstrating its capability to handle complex datasets with high intra-class variation. In contrast, handcrafted representations performed poorly such datasets due to their inability to fully exploit temporal and spatial features. On the N-MNIST dataset, our framework achieved an accu- racy of 97. 97.4% comparable to the state-of-the-ar methods, EST and HATS. This indicates our framework's robustness even on datasets with lower complexity. For the ASL-DVS dataset, TORE achieved the highest accuracy of 99.9%. Our framework achieved comparable performance with curacy of 99.2%, demonstrating its effectiveness in handling complex gesture recognition tasks. The semantic 2D vector distribution maps of N-MNIST and ASL-DVS are shown in Figure 7.
AMAE (Deng et al.)
98.3
69.4
93.6
98.4
M-LSTM (Cannici al.)
98.6
73.8
92.7
98.0
MVF-Net (Deng et al.)
98.1
68.7
92.7
97.1
EvT (Sabater et al.)
98.3
61.3
89.6
99.9
DVS- ViT (Wang et al.)
98.1
63.3
90.7
96.9
TOKEN (Jiang et al.)
99.9
81.6
95.4
99.9
Ours
97.4
84.4
99.9
99.2
decrease, training was terminated early ensure robust model performance.
Results. As listed in Table 1, our framework outper- formed all competing methods on the N-Caltech 101 and N-CARS datasets and achieved competitive results on the N-MNIST and ASL-DVS datasets. On the N-CARS dataset, our framework achieved near-perfect accuracy of 99.9%. surpassing the current best-performing TOKEN method by 4.5% and TORE by 5.4%. Even advanced approaches such as EST and MVF-Net showed inferior performance com- pared to our framework. This underscores our framework's 's ability to effectively leverage temporal and polarity infor- mation. On the N-Caltech101 dataset, our framework's 's accuracy exceeded HATS by 17.5% and EST by 9.05%,
Complexity Analysis. Table 2 lists the model complexity of different methods of object classification. We evalu- ate the model complexity comprehensively by three met- rics: the number of trainable parameters, the number of multiply-accumulate operations (MACs), and average in- ference time. Our framework achieves superior accuracy on N-Caltech101 while keeping the moderate model com- plexity(3.67G MACs), demonstrating the efficiency of our

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Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing
RGB Frame
Event Count
Time Surface
LIF
0 Ours
Figure 8. Visualization of different event representation methods on the N-Caltech101 dataset. Green boxes ground truth, while red boxes are the minimum enclosing rectangle.
framework event-based representation learning. We fur- ther measure the average inference time of our framework on N-Cars using workstation (CPU: Intel Core 19, GPU: NVIDIA RTX 4060, RAM: 16GB). Our framework takes 2.12 ms to recognize a sample equivalent to throughput of 472 samples per second, showing the practical potential in high-speed scenarios.
at 30000 events, our framework showed substantial improve- ment the number of events increased, reaching an IoU of 0.6450 100000 events. These results suggest that our framework is more effective in extracting and utilizing spa- tiotemporal information from event streams, particularly as higher event counts enhance object shapes and features. The observed improvement further underscores the robustness of the model and highlights the superiority of the proposed approach. The visualization of different representation methods on the N-Caltech 101 dataset is shown in Figure 8.
Table IoU of different event representations on the N- Caltech101 dataset.
Event Representation
loU
IoU
IoU
IoU
5. Conclusion
(30000)
(50000)
(70000)
(100000)
Event Count (Miao al.)
0.4276
0.5640
0.5896
0.5937
In this work, we propose a chaotic dynamics framework inspired by the dorsal stream event signal processing, which generates generalized and stable event representa- tions. Then the framework is integrated with image- algorithms for event-based object classification, achieving high accuracy across multiple datasets. Furthermore, our framework demonstrates significant efficiency in sample inference, processing 472 samples per second. In sum- mary, we propose method for event cameras, combining robustness with computational efficiency, and demonstrating promising application potential in real environments.
Time Surface (Miao al.)
0.4845
0.5976
0.6089
0.6198
LIF (Miao al.)
0.2056
0.2162
0.3722
0.4022
Ours
0.4879
0.6087
0.6357
0.6450
IoU Comparison.
The experimental results on the N-
Caltech101 dataset shows the Intersection over Union (IoU) performance of our framework and other methods. The methods were evaluated under different numbers of events (30000, 50000, 70000, and 100000). As listed Table 3, our framework shows superior performances than all other methods at each event count. Starting with an loU 0.4879
8

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A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing
Acknowledgement
Deng, Y., Chen, H., Liu, H., and Li, Y. voxel graph cnn for object classification with event cameras. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition. pp. 1 1172-1181, 2022.
This work is sponsored by Beijing Nova Program (2022038, 20240484703). Some experiments are supported by the Supercomputing Center of Lanzhou University. Additional support was provided in part by the Gansu Computing Cen- ter.
Fang, W., Yu, Z., Chen, Y., Masquelier, T., Huang, T., and Tian, Y. Incorporating learnable membrane time con- stant to enhance learning of spiking neural networks. In Proceedings of the IEEE international conference on com- puter vision, pp. 2661-2677 2021.
Impact Statement
This paper presents work whose goal advance the field of Machine Learning. There are many potential societal consequences of our work, none which feel must be specifically highlighted here.
Gallego, G., Rebecq, H., and Scaramuzza, D. A unifying contrast maximization framework for event cameras, with applications to motion, depth, and optical flow estima- tion. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3867-3876, 2018.
References
Gallego, G., Gehrig, M., and Scaramuzza, D. Focus is all you need: Loss functions for event-based vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12280-12289, 2019.
Almatrafi, M., Baldwin, R., Aizawa, K., and Hirakawa, K. Distance surface for event-based optical flow. IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 42 (7):1547-1556, 2020.
Gallego, G., Delbrück, T., Orchard, G., Bartolozzi, C.,, Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Danillidis, K., and Scaramuzza, D. Event-based vision: A survey. IEEE transactions on pattern analysis and machine intelligence, 44(1):154-180, 2020.
Baldwin, W., Liu, R., Almatrafi, M., Asari, V., and Hi- rakawa, K. Time-ordered recent event (tore) volumes for event cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2):2519-2532, 2022.
Bi, Y., Chadha, A., Abbas, A., Bourtsoulatze, E.,, and An- dreopoulos, Y. Graph-based object classification for neu- romorphic vision sensing. In Proceedings of the IEEE international conference on computer vision, pp. 491- 501,2019 2019.
Gehrig, D.,, Loquercio, A., Derpanis, K. G., and Scara- muzza, D. End-to-end learning of representations for asynchronous event-based data. In Proceedings of the IEEE International Conference on Computer Vision, pp. 5633-5643. 2019.
Bi, Y., Chadha, A., Abbas, A., Bourtsoulatze, E., and An- feature learn-
Gong, Z., Zhou, M., Dai, Y., Wen, Y., Liu, Y., and Zhen, Z. A large-scale fmri dataset for the visual processing of naturalistic scenes. Scientific Data, 10(1):559, 2023.
dreopoulos, Y. Graph-based ing for neuromorphic vision sensing. IEEE Transactions on Image Processing, 29:9084-9099 2020.
Brebion, V., Moreau, J., and Davoine, Real-time op- tical flow for vehicular perception with low-and high- resolution event cameras. IEEE Transactions on Intelli- gent Transportation Systems, 23(9):15066-1577) 2021.
Groen, I., Piantoni, G., Montenegro, S., Flinker, A., Devore, S.. Devinsky, O., Doyle, W., Dugan, P., Friedman, D., Ramsey, N., Petridou, N., and Winawer, J. Temporal dynamics of neural responses in human visual cortex. Journal of Neuroscience, 42(40))756--7580, 2022.
Cannici, M.. Ciccone, M., Romanoni. A., and Matteucci, M. A differentiable recurrent surface for asynchronous event- based data. In Proceedings of the IEEE Conference on European Conference on Computer Vision, pp. 136-152. Springer, 2020.
Hagenaars, J., Paredes- F., and Croon, G. D. Self- supervised learning of event- optical flow with spik- ing neural networks. Advances in Neural Information Processing Systems, 34:7167-7177 2021.
Deng, Y., Li, Y., and Chen, H. Amae: Adaptive motion- agnostic encoder for event-based object classification. IEEE Robotics and Automation Letters, 5(3):4596 2020.
Jiang, B., Li, Z., Asif, M. S., Cao, X., and Ma, Z. Token- based spatiotemporal representation of the events. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5240-5244, 2024.
Deng, Y. Chen, H., and Li, Y. Mvf-net: A multi-view fusion network for event- event-based object classification. JEEE Trans- actions on Circuits and Systems for Video Technology, 32 (12):8275-822 2021.
Lagorce, X., Orchard, G., Galluppi, F., Shi, B. E., and Benosman, R. B. Hots: a hierarchy of event-based time- surfaces for pattern recognition. IEEE Transactions on

---

A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing
Pattern Analysis and Machine Intelligence, 39(7):1346 1359, 2017.
Shiba, S., Aoki, Y., and Gallego, G. Event collapse in
contrast maximization frameworks. Sensors, 22(14):5190, 2022a.
Li, Y., Kim, Y., Park, H., Geller, T., and Panda, P. Neuro- morphic data augmentation for training spiking neural networks, In Proceedings of the IEEE Conference European Conference on Computer Vision, pp. 631-649. Springer, 2022.
Shiba, S., Aoki, Y., and Gallego, G. Secrets of event-based optical flow. In Proceedings of the IEEE Conference on Computer Vision and Puttern Recognition, pp. 028-645. Springer, 2022b.
Liu, J., Lian, J., Sprott, J. C., Liu, Q., and Ma, Y. The but- terfly effect in primary visual cortex. IEEE Transactions on Computers, 71(11):2803-2815, 2022.
Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., and Benosman, R. Hats: Histograms of averaged time sur- faces for robust event-based object classification. In Pro- ceedings of the IEEE Conference Computer Vision and Pattern Recognition, pp. 1731-1740, 2018.
Miao, S., Chen, G., Ning, X., Zi, Y., Ren, K., Bing, Z., and Knoll, Neuromorphic vision datasets for pedestrian detection, action recognition, and fall detection. Frontiers in neurorobotics, 13:38, 2019.
Stoffregen, T. and Kleeman, L. Event cameras, contrast maximization and reward functions: An analysis. In Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pp. 12300-12308, 2019.
Mueggler, E., Bartolozzi, C., and Scaramuzza, D. Fast event-based corner detection. IEEE Journal of British Machine Vision Conference, 40,3):1-8,201
Wang. Q., Zhang, Y., Yuan, J., and Lu, Y. Space-time event clouds for gesture recognition: From rgb cameras to event cameras. In Proceedings of the IEEE Conference Winter Conference on Applications of Computer Vision, pp. 1826-1835, IEEE, 2019.
Orchard, G., Jayawant, A., Cohen, G., and Thakor, N. Con- verting static image datasets to spiking neuromorphic datasets using saccades. Frontiers in neuroscience, 9:437, 2015.
Paredes-Vallee F. and De Croon, G. C. Back to event ba- sics: Self-supervised learning of image reconstruction for event cameras via photometric constancy. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3446-3455, 2021.
Wang. Z., Hu, Y., and Liu, S. C. Exploiting spatial spar- sity for event cameras with visual transformers. In Pro- ceedings of the IEEE International Conference on Image Processing, pp. 411-415. IEEE, 2022.
Wang, Z., Wang, Z., Li, H., Qin, L.,, Jiang, R., Ma. D., and Tang, H. Eas-snn: End-to-end adaptive sampling and representation for event-based detection with recurrent spiking neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 310-328. Springer, 2024.
Qi. C. R., Yi, L., Su, H., and Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in met- ric space. Advances in neural information processing systems, 30, 2017.
Sabater, A., Montesano, L., and Murillo, A. C. Event transformer. sparse-aware solution for efficient event data processing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2677- 2686, 2022.
Xic, B., Deng, Y., Shao, Z., Liu, H., and Li, Y. Vmv-gen: Volumetric multi-view based graph enn for event stream classification.. IEEE Robotics and Automation Letters, 7 (2):1976-1983, 2022.
Schaefer, S., Gehrig, D., and Scaramuzza, D. Aegnn: Asyn- chronous event-based graph neural networks. In Pro- ceedings of the IEEE conference on computer vision and pattern recognition, pp. 12371-1238 2022.
Xu, K., Hu, W., Leskovec, J.. and Jegelka, S. How powerful are graph neural networks? arXiv preprint arXiv: N0Gi:18100008880 2018.
Yang, J., Zhang, Q. Ni, B., Li, Liu, J., Zhou, M., and Tian, Q. Modeling point clouds with self-attention and gumbel subset sampling. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.
Sekikawa, Y., Hara, K., and Saito, H. Eventnet: Asyn- chronous recursive event processing. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 3887-3896, 2019.
2019.
Shen, H., Xiao, J., Luo, Y., Cao, X., Zhang, L., and Wang, T. Training robust spiking neural networks with viewpoint transform and spatiotemporal stretching. In Proceedings of the IEEE Conference on Acoustics, Speech and Signal Processing, pp- 1-5, 2023.
Zhu, A. Z., Yuan, L., Chaney, K., and Danilidiis, K. Ev- flownet: Self-supervised optical flow estimation for event- based cameras. arXiv preprint arXiv:1802 1808,08888, 2018
10


Reviews:
[{"review_id": "DhE8W7Jtoq", "reviewer": "Reviewer_rvoE", "summary": "This paper proposes a chaotic dynamics framework inspired by the dorsal visual pathway of the brain for processing event camera signals. By encoding event streams into periodic or chaotic signals using Continuous Coupled Neural Networks (CCNN) and analyzing dynamic states via Continuous Wavelet Transform (CWT), the framework integrates traditional classification networks for object recognition. Experiments demonstrate state-of-the-art classification accuracy on datasets such as N-Caltech101 (84.3%) and N-CARS (99.9%), with high inference efficiency (472 samples/sec). The authors emphasize the framework’s generalization and stability advantages and have open-sourced the code.\n\n## update after rebuttal\nAuthors' rebuttal have solved my concerns. I think previous score is high enough and I will keep my rating.", "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": "We sincerely appreciate your time and effort in reviewing our paper. We are grateful for your constructive feedback and insightful questions, which have helped us refine our work and clarify its contributions. We acknowledge your concerns regarding the theoretical foundations, biological plausibility, and certain experimental analyses, and we appreciate the opportunity to further elaborate on these aspects. In this response, we address each of your comments in detail, providing clarifications, additional discussions, and further justifications where necessary. We hope that our responses effectively resolve your concerns and further demonstrate the significance and robustness of our proposed framework.\n\n**A1:** The CCNN simulates the encoding mechanism of primary visual cortex neurons. The proposed chaotic dynamics framework, consisting of three modules—CCNN, CWT, and LPF—mimics the dorsal stream's function in extracting dynamic object location information. \\\n**Experimental Evidence:** Siegel observed complex electrical signal fluctuations in the primary visual cortex neurons of cats under periodic signal stimulation, revealing the presence of chaotic behavior in the mammalian primary visual cortex [1]. Building on this, Liu further modified the dynamic threshold adjustment mechanism of PCNN and proposed CCNN [2]. This model exhibits periodic signals under constant stimulation and chaotic signals under periodic stimulation, aligning with findings from biological experiments.\n\n[1] Siegel, R. M. Non-linear dynamical system theory and primary visual cortical processing. Physical D: Nonlinear Phenomena, 42(1-3):385–395, 1990. \n\n[2] Liu, J., Lian, J., Sprott, J. C., Liu, Q., and Ma, Y. The butterfly effect in primary visual cortex. IEEE Transactions on Computers, 71(11):2803–2815, 2022.\n\n**A2:** The period $k$ has no physical relationship with the event stream (such as velocity or direction) and cannot be used to improve classification performance. The derived period $k$ is used to demonstrate that the CCNN generates periodic signal outputs when subjected to constant stimuli.\n\n**A3:** The Gaussian wavelet is widely used in time-frequency analysis. Compared to the Morlet wavelet, it offers superior smoothness and better time-frequency localization, making it particularly suitable for analyzing periodic and chaotic signals generated by CCNN neurons.\n\n**A4:** This design does not lead to information loss. Setting negative values to 255 is intended to extract the coordinates of event signal points with constant polarity, thereby detecting moving objects. A more refined threshold design will be presented in future papers.\n\n**A5:** As event density increases, the proposed framework captures finer textures, improving IoU. While denser events enhance performance, the framework is not dependent on them. For sparse events, effective denoising and enhancement techniques may be needed to optimize IoU.\n\n**A6:** Our model uses ResNet-34 with 21.9M parameters, more than EV-VGCNN (0.8M), but parameter count mainly affects memory usage and training time, not inference speed. The faster inference is due to: (1) Efficient 3×3 convolutions and residual connections optimizing computation flow; (2) GPU-optimized deep learning frameworks (cuDNN) accelerating ResNet inference; (3) Convolutional optimizations like Winograd transformations reducing computational complexity. In contrast, EV-VGCNN's fully connected layers may limit parallelism, leading to slower inference.\n\n**A7:**  The LIF method suppresses many event points due to an unreasonable threshold, leading to an incomplete event pattern. The Time Surface captures motion trajectories but introduces redundancy, affecting IoU detection. Event Count maps event accumulation to pixel values, causing inconsistencies that impact pattern integrity. This paper proposes a framework that converts events with constant and periodic polarity changes into periodic and chaotic signals via CCNN, then processes them using CWT and LPF. Event coordinates with constant polarity are set to 255 to extract valid event signals effectively.\n\n**A8:** Dataset size and category count significantly impact classification performance. N-MNIST, N-CARS, and ASL-DVS, with fewer categories and high intra-class similarity, enable effective feature learning and high accuracy. In contrast, N-Caltech101's limited samples, diverse categories, and large intra-class variation increase training difficulty and lower accuracy. This study introduces a chaotic dynamic framework inspired by the dorsal visual pathway, generating robust event representations. Combined with lightweight ResNet34, it achieves high accuracy across all four datasets.\n\n**A9:** The appendix and open-source code provide the full CCNN implementation, including network architecture and hyperparameters. This documentation enables readers to reproduce results with high confidence in their reproducibility.", "claims_and_evidence": "The paper's main contributions are well-supported by experimental evidence. It introduces a chaotic dynamics-based event representation using CCNN, validated through mathematical modeling and phase-space trajectory analysis. Event mapping with CWT effectively distinguishes stable and dynamic events, as demonstrated by heatmaps of transformed matrices. Experimental validation on multiple datasets shows superior classification accuracy compared to baseline methods, highlighting strong generalization and stability, further confirmed by IoU evaluation. While the results are convincing, further discussion on the applicability of CCNN’s chaotic properties in different data scenarios would be beneficial.", "methods_and_evaluation_criteria": "The proposed method adopts appropriate evaluation criteria, including classification accuracy, IoU, and computational complexity (number of parameters, MACs, and inference time). The choice of datasets covers both static (N-MNIST) and dynamic (N-CARS) event classification tasks, ensuring a comprehensive evaluation. One potential improvement would be to analyze the impact of different time window lengths on the temporal representation.", "theoretical_claims": "The paper’s theoretical foundation is based on the dynamical equations of CCNN, providing a mathematical proof of periodic and chaotic signal responses. The nonlinear analysis, including equilibrium point derivation and Taylor expansion, is reasonable.", "experimental_designs_or_analyses": "The experimental setup is well-structured, primarily evaluating classification accuracy and model complexity. A ResNet-34 network pre-trained on ImageNet is used for classification, with a clear training strategy. However, the study does not investigate the impact of different CCNN coupling strengths on the quality of event representations. Including such an analysis could provide deeper insights into the generalizability of the proposed method.", "relation_to_broader_scientific_literature": "This work is closely related to event-based data representation methods (HATS, EST, TORE, TOKEN) and biologically inspired computing (Spiking Neural Networks). The paper provides a clear literature review of mathematical, deep-learning-based, and bio-inspired event processing approaches, highlighting their limitations, such as strong dependence on specific data structures. The proposed method introduces a novel perspective by incorporating chaotic dynamics modeling, which is rarely explored in event vision research.", "essential_references_not_discussed": "The paper cites most relevant literature but could benefit from additional references on chaotic dynamics in computational neuroscience, such as Lorenz systems and Hindmarsh-Rose models for visual cortex modeling, as well as event camera applications in low-power embedded systems to explore the method’s practical deployment potential.", "other_strengths_and_weaknesses": "Strengths、\n\n1. The biologically inspired integration of dorsal stream mechanisms with chaotic dynamics presents a novel event representation framework with theoretical significance.\n2. Outperforms existing methods significantly on multiple mainstream datasets while achieving high inference efficiency, demonstrating practical deployment potential.\n3. Robustness is validated through cross-dataset experiments, particularly showing stability in dynamic event streams.\nProvides mathematical modeling and visualization analyses of CCNN and CWT.\n\nWeaknesses\n\n1. The biological plausibility of CCNN’s connection to the dorsal stream is insufficiently supported, lacking explanations of how it mimics the spatiotemporal encoding mechanisms of the \"where pathway.\"\n2. No comparisons with bio-inspired models (e.g., spiking neural networks) to justify the unique advantages of dorsal stream inspiration.", "other_comments_or_suggestions": "The mathematical analysis section could benefit from further discussions on the stability and bifurcation behavior of nonlinear systems to strengthen the theoretical foundation.", "questions_for_authors": "1. How does the neuronal dynamics of CCNN specifically simulate dorsal stream functions (e.g., motion perception, spatial localization)? Are there neuroscientific experiments supporting this design?\n2. In Equation (6), does the derived periodic parameter  k directly correlate with physical properties of event streams (e.g., velocity, direction)? How is it leveraged to enhance classification performance?\n3. Why was the Gaussian wavelet selected as the CWT basis function? What advantages does it offer over alternatives (e.g., Morlet wavelet)?\n4. The low-pass filter in Equation (14) sets negative values to 255 and positive values to 0. Could this lead to information loss? Is there a more refined thresholding strategy?\n5. Table 3 shows improved IoU with increasing event density. Does this imply the framework relies on dense events? How is its performance optimized for sparse event streams?\n6. In Table 2, the model’s parameter count (21.9M) exceeds EV-VGCNN (0.8M), yet inference is faster. Is this due to architectural design (e.g., parallelization)? Please elaborate.\n7. In Fig. 8, Why do the other methods not work well? Why does the author's method work better?\n8. I have observed that the results of different methods vary greatly between datasets, as shown in Fig. 1. Can the authors explain what differences in the datasets lead to such results? What advantages of the proposed method can alleviate such differences?\n9.  Does the open-sourced code include CCNN training details? If not, how can reproducibility be ensured?", "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": "fMBYDSP232", "reviewer": "Reviewer_BkdQ", "summary": "The methods combining event cameras and deep learning mainly involve integrating traditional deep learning techniques with the high temporal resolution and low latency characteristics of event cameras, aiming to process the event stream data. However, the limitation of existing methods for event cameras is their heavy reliance on data structures, which restricts the stability and generalization ability of the models. These models may not adapt well to different tasks or scenarios, leading to unstable performance in real-world applications. This paper proposes a chaotic dynamics signal processing framework inspired by the dorsal visual pathway of the brain. It utilizes the Continuous Coupled Neural Network (CCNN) to encode the event stream, encoding polarity-changing event sequences as chaotic signals. Continuous wavelet transforms are then used to analyze the dynamic states of CCNN neurons and establish high-order mappings of the event stream.\n\n## update after rebuttal\nAuthors' rebuttal have solved my concerns. I think previous score is high enough and I will keep my rating.", "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": "we would like to express our sincere gratitude for your in-depth review of our paper and your valuable feedback. We greatly appreciate your recognition of the proposed method and experimental results, and we also thank you for raising some important questions that will help us further improve the quality of the paper. In response to your comments, we have made several revisions and additions to the manuscript, and we provide detailed answers to the concerns you raised below. We believe that, with your feedback, the paper will be further enhanced.\n\n**W1:** The author did not explain some of the parameters in the brain-inspired model, especially certain setting parameters in the CCNN, which play an important role in understanding the brain's visual mechanisms. \\\n**A1:** The unconnected CCNN comprises modulated input $U(k)$, continuous output  $Y(k)$, and dynamic threshold $E(k)$. The terms $e^{-\\alpha_f}$ and $e^{-\\alpha_e}$ denote exponential decay factors that record the previous input states. $V_E$ is the weight factor for adjusting the neuronal potential. When $U(k)>E(k)$, the output is $Y(k) = \\frac{1}{1+e^{-(U(k)-E(k))}}$, indicating excitation. After stimulation, $E(k)$ increases, requiring a stronger stimulus for the next output, mimicking the neuronal refractory period. Siegel observed chaotic behavior in the primary visual cortex of cats under periodic stimulation [1]. Similarly, CCNN exhibits periodic signals under constant stimulation and chaotic signals under periodic stimulation, adhering to this biological constraint. \n\n[1] Siegel, R. M. Non-linear dynamical system theory and primary visual cortical processing. Physical D: Nonlinear Phenomena, 42(1-3):385–395, 1990.\n\n**C1:** In Figure 4, the x-axis labels for subfigures (a), (b), and (c) should be corrected from ‘Iterations’ to ‘Iterations’. Additionally, in Table 3, the value in the second row, fifth column should be changed from ‘70000’ to ‘100000’. \\\n**A1:** Thank you for your careful review. We have revised the x-axis labels in subfigures (a), (b), and (c) of Figure 4 as suggested and corrected the value in the second row, fifth column of Table 3 to ensure accuracy. These changes have been incorporated into the revised manuscript.\n\n**A1:** Thank you for your reminder. In Equation (4), $U(k)$ represents the modulation input, $Y(k)$ is the continuous output, and $E(k)$ is the dynamic threshold. The terms $e^{-\\alpha_f}$ and $e^{-\\alpha_e}$ denote exponential decay factors that record the previous input states. $V_E$ is the weight factor for adjusting the neuronal potential.\n\n**A2:** When the modulation input $U(k)$ exceeds the dynamic threshold $E(k)$, the output $Y(k)$ is given by $\\frac{1}{1+e^-(U(k)-E(k))}$, which corresponds to the neuron generating an excitatory potential in response to sufficient stimulation. Upon the next stimulus, the dynamic threshold increases, and the model requires a stronger input to produce an output, mimicking the refractory period of biological neuronal cell membranes.\n\n**A3:** In Equation (7), $k$ is a parameter that controls the input. When the event signal input follows the periodic sequence\n {0,1,0,1,$\\cdots$,0,1}, its equivalent formula is $\\sin(\\frac{k\\cdot \\pi}{2})$, where $k$={0,1,2,$\\cdots$, n}.\n\n**A4:** Both $E(0)$ and $V_E$ can affect the period of the output signal. However, the setting of these parameters does not impact the results, as long as the CCNN output is a periodic signal when the input polarity of the event signal remains constant.\n\n**A5:** Under the stimulation of periodic signals, the CCNN exhibits complex chaotic dynamic characteristics. This paper discusses its equilibrium state, where a balance point exists in the output, such that $E(k+1)=E(k)$.\n\n**A6:** This section discusses the equilibrium point of the CCNN neuron. When $V_E >0$, $\\alpha_e >0$, and $4\\frac{V_E}{1-e^{-\\alpha_e}}>0$, with $\\Delta>0$, the equation has a solution, and an equilibrium point exists. This discussion does not affect the computational results.\n\n**A7:** Gaussian wavelets are widely used in time-frequency analysis due to their smoothness and good time-frequency locality. They are well-suited for analyzing periodic and chaotic signals generated by CCNN neurons.\n\n**A8:** The heatmap of the output sequence is a visualization of the sequence after CWT waveform analysis, showing the value distribution of the real part of the coefficient matrix. The sum of the real part of the coefficient matrix for chaotic sequences is an integer, while for periodic sequences, the sum is negative. A low-pass filter is then applied to extract the coordinate points of the periodic sequence.\n\n**A9:** The Low-pass Filter is a module proposed in this paper's chaotic dynamic framework and is not directly related to the brain-like mechanism of the CCNN. It is used to extract the coordinate points corresponding to the constant polarity event sequences, enabling the detection of moving objects.", "claims_and_evidence": "The claims made in the submission are well-supported by clear and convincing evidence. The experimental results are comprehensive, with appropriate comparisons to existing methods, and the statistical analyses are thorough. The theoretical justifications are sound, and the cited literature is relevant and up-to-date. Overall, the evidence provided strongly supports the paper’s conclusions.", "methods_and_evaluation_criteria": "The proposed methods are well-designed and appropriate for the problem at hand. The paper provides a clear explanation of the methodology, with sufficient theoretical justifications and algorithmic details. The evaluation criteria are well-chosen, using relevant benchmark datasets and standard performance metrics. Additionally, the experiments include comprehensive comparisons with state-of-the-art methods, which further validate the effectiveness of the proposed approach.", "theoretical_claims": "The paper proposes a generalizable event representation, validated across multiple datasets. Comparative experiments with state-of-the-art methods show competitive performance, achieving the highest accuracy on certain datasets. The experimental results support the proposed theoretical claims.", "experimental_designs_or_analyses": "The experimental design is well-structured, covering multiple datasets and providing a comprehensive comparison with state-of-the-art methods from recent years. Additionally, the paper includes a complexity analysis of the model and IoU experiments, further validating the feasibility of the proposed approach. The results are clearly presented, and the conclusions are well-supported by the experimental findings.", "relation_to_broader_scientific_literature": "The paper provides a comprehensive discussion of related work, thoroughly analyzing the principles, advantages, and limitations of frame-based event representations, contrast maximization-based event representations, and end-to-end network-based event representations. Inspired by the dorsal visual pathway in the primary visual cortex, the study introduces CCNN to encode event signals, employs CWT to analyze the dynamic properties of neurons, and finally utilizes LPF to extract information about moving objects. The comparison with existing methods is extensive, effectively highlighting the proposed approach’s generality and robustness.", "essential_references_not_discussed": "The paper provides a thorough discussion of prior work, covering key contributions in the field. It appropriately cites and compares relevant studies on event representation methods, ensuring a comprehensive contextual understanding. No essential references appear to be missing.", "other_strengths_and_weaknesses": "Strength\nThe advantages of this proposed chaotic dynamics signal processing framework include improved stability and generalization, dynamic state analysis, high-order mapping capabilities, improved performance in real-world applications. The structure is well-organized and logical, with the design principle clearly and appropriately explained.\n\nWeakness\nThe author did not explain some of the parameters in the brain-inspired model, especially certain setting parameters in the CCNN, which play an important role in understanding the brain's visual mechanisms.", "other_comments_or_suggestions": "In Figure 4, the x-axis labels for subfigures (a), (b), and (c) should be corrected from ‘Iterations’ to ‘Iterations’. Additionally, in Table 3, the value in the second row, fifth column should be changed from ‘70000’ to ‘100000’.", "questions_for_authors": "1. Many parameters in the CCNN model are not explained, such as Y(k), VE, exp(-af), and exp(-ae) in Equation (4).\n2. What is the relationship between the unexplained parameters in Equation (4)? Please provide relevant explanations.\n3. In Equation (7), how should the value of parameter K be set?\n4. It seems that E(0) and VE determines the period of the output signal. What effect do the settings of these parameters have on the results?\n5. In Equation (8), why is E(K+1) = E(K)? What is the significance of this setting?\n6. In Equation (9), why is VE > 0 and αe > 0? What is the impact of this on the calculation results?\n7. In Equation (11), what is the rationale for choosing the Gaussian function?\n8. In Fig. 6(a), what is the relationship between the heatmap corresponding to the chaotic sequence and the final computation results of the CCNN?\n9.The author has already presented the CCNN, why is there further research on the CWT? What is the purpose of this?\n10. What is the relationship between the Low-pass Filter and the brain-inspired mechanism of CCNN? Why is it necessary to conduct relevant experiments?", "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": "MWxw3tlj6d", "reviewer": "Reviewer_PCzW", "summary": "This paper proposes a chaotic dynamical framework inspired by the dorsal visual pathway for processing event signals and generating stable and generalizable event representations. By integrating it with deep neural networks, the authors achieved high accuracy on multiple event-based object classification datasets while demonstrating efficient inference. The work exhibits strong completeness in theoretical derivation, experimental validation, and cross-dataset generalization analysis.", "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": "We sincerely appreciate your thorough evaluation and valuable feedback on our paper. We are pleased that our chaotic dynamical framework and experimental results have been recognized, and we are grateful for the insightful questions that have helped us further improve the paper. In response to your comments, we have made the necessary revisions, and we believe these improvements will enhance the quality and impact of our work. Below, we address each of your comments in detail, providing clarifications and additional analyses where necessary. \n\n**A1:** Thank you for the reminder. The annotations in Figure 1(a) highlight the superiority of the event representation generated by our framework. While the pyramid in the original image shows numbers 1-6, our framework clearly restores their contours, unlike event representations from end-to-end networks, which fail to preserve these details.\n\n**A2:** The chaotic dynamic framework proposed in this paper includes CCNN, CWT, and LPF. CCNN is a network inspired by the primary visual cortex (V1) of the brain. After the event signals are processed by CCNN, they are further analyzed and extracted using CWT and LPF for dynamic object detection, which corresponds to the brain's processing from V1 to MT.\n\n**A3:** The unconnected CCNN comprises modulated input $U(k)$, continuous output  $Y(k)$, and dynamic threshold $E(k)$. When $U(k)>E(k)$, the output is $Y(k) = \\frac{1}{1+e^{-(U(k)-E(k))}}$, indicating excitation. After stimulation, $E(k)$ increases, requiring a stronger stimulus for the next output, mimicking the neuronal refractory period. Siegel observed chaotic behavior in the primary visual cortex of cats under periodic stimulation [1]. Similarly, CCNN exhibits periodic signals under constant stimulation and chaotic signals under periodic stimulation, adhering to this biological constraint.\n\n\n[1] Siegel, R. M. Non-linear dynamical system theory and primary visual cortical processing. Physical D: Nonlinear Phenomena, 42(1-3):385–395, 1990.\n\n**A4:** Thank you for your reminder. The testing has been conducted on a local workstation, and the experimental results demonstrate the model's potential for real-time applications. Future work will focus on further validation in real-world scenarios.\n\n**A5:** Hyperparameters include batch size 16, cross-entropy loss, Adam (lr=1e-4), 40% Dropout before FC layers, and early stopping. A smaller batch size aids generalization, cross-entropy improves accuracy, Adam ensures stable convergence, and Dropout with early stopping prevents overfitting. The appendix and open-source code include the parameter settings, facilitating reviewers and readers in reproducing and improving this work.\n\n**A6:** The model was tested on a workstation with an Intel Core i9 CPU, NVIDIA RTX 4060 GPU (8GB VRAM), and 16GB RAM. Training on a small dataset (N-Caltech101/N-CARS, ~50,000 samples) took 12-18 minutes, while a large dataset (N-MNIST/ASL-DVS, >200,000 samples) took 30-60 minutes. To optimize for resource-constrained environments, techniques like pruning, quantization, knowledge distillation, lightweight architectures, and mixed-precision training can be used to reduce computational demands and improve efficiency.\n\n**A7:** The chaotic dynamic framework proposed in this paper processes event signals to obtain a general event representation, which can be extended to other tasks. We also plan to apply this event representation method to more event-based tasks in future work.\n\n**A8:** Parameter count affects memory and training time but not inference speed. PointNet++ runs slower due to costly neighborhood queries, while dense CNNs benefit from optimized parallelism. Our method (21.9M params) achieves 2.1ms inference on an RTX4060 via a regularized convolutional design (MACs = 3.7G, 7.5% lower than PointNet++) and PyTorch + TensorRT optimizations.\n\n**A9:** We propose a hybrid CCNN-SNN framework that replaces the ResNet-34 classifier with SNNs to reduce computational energy consumption. By integrating the STDP mechanism, this architecture achieves adaptive learning capabilities while enhancing biological plausibility and interpretability. Future investigations will prioritize systematic evaluation of its low-power computing performance and neuro-inspired operational principles.\n\n**A10:** We acknowledge EvT's strengths in global spatiotemporal modeling but exclude it from comparisons due to its computational inefficiency with high-resolution event data. Our current focus on efficiency-generalization trade-offs motivates the proposed CCNN-Transformer hybrid architecture for enhanced event representation in complex scenarios.\n\n**A11:** We have not yet conducted systematic small-sample learning experiments, but CCNN’s chaotic dynamics allow it to maintain good generalization with limited data. Future research will include small-sample experiments and explore combining CCNN with meta-learning to enhance adaptability in low-data scenarios.", "claims_and_evidence": "The claims presented in this paper are well-supported through rigorous theoretical derivations and experimental validations. The mathematical modeling of CCNN is robust, and its chaotic properties are demonstrated through phase space analysis. The experimental results on multiple datasets, including N-Caltech101, N-CARS, N-MNIST, and ASL-DVS, indicate superior classification performance compared to existing methods. Additionally, the proposed method achieves a low inference time of 2.1ms per sample, demonstrating computational efficiency. Overall, the claims are substantiated with strong theoretical and empirical evidence, making the conclusions credible.", "methods_and_evaluation_criteria": "The proposed methods and evaluation criteria are well-designed and appropriate for event signal processing. The CCNN model presents a novel approach for event stream encoding, while CWT enhances the event representation. The benchmark datasets (N-Caltech101, N-CARS, etc.) are widely used, ensuring fair comparisons. The evaluation metrics (classification accuracy, IoU, inference time) comprehensively assess the framework’s performance. Furthermore, comparisons with various existing methods, including voxel-based and ANN-based approaches, validate the proposed framework’s advantages. The methodology is sound and well-justified, with appropriately chosen evaluation criteria.", "theoretical_claims": "The theoretical derivations are clear, and the chaotic behavior of CCNN is rigorously analyzed through mathematical modeling and phase space analysis. However, some assumptions in the derivations, particularly in Equations (4)–(7), could be more explicitly stated. Additionally, a discussion on the generalization of the chaotic encoding approach to other tasks (e.g., motion segmentation, object tracking) would further strengthen the theoretical contributions. Overall, the theoretical claims are well-founded, albeit with minor areas for clarification.", "experimental_designs_or_analyses": "The experimental design is comprehensive and well-structured, covering multiple datasets (N-Caltech101, N-MNIST, N-CARS, ASL-DVS) to ensure the robustness and generalization of the proposed framework. The training and evaluation protocols (ResNet-34 pretrained model, Adam optimizer) are appropriate for the task. The results consistently demonstrate the superiority of the proposed method in accuracy, robustness, and computational efficiency compared to prior works.", "relation_to_broader_scientific_literature": "The paper is well-situated within the broader scientific literature on event-based vision and bio-inspired neural computation. It extends existing event representation methods, integrates insights from biological visual processing, and aligns with research on spiking neural networks (SNNs) and chaotic dynamics. The combination of chaotic dynamics and event camera processing is novel and contributes valuable insights to the field, paving the way for further advancements in neuromorphic computing.", "essential_references_not_discussed": "The paper cites most of the essential prior works, but additional references on chaotic neural networks in neuromorphic computing and self-supervised learning approaches for event streams could further strengthen the literature review. Including these references would provide a more comprehensive discussion of the related work.", "other_strengths_and_weaknesses": "Strengths\n\nThe biologically inspired chaotic dynamical model integrated with neuromorphic vision sensor data provides novel theoretical insights and methodological frameworks for event signal processing. This interdisciplinary approach offers valuable inspiration for both computational neuroscience and computer vision.\n\n Comprehensive experimental design, including comparative studies across multiple event-based datasets, effectively validates the method's universality. Theoretical derivations and visualizations complement each other, enhancing the credibility of conclusions.\n\nWeaknesses\n\nThe lateral brain diagram shows misalignment in the circular annotation, and the left arrow in the right-side network model is not centered. Layout adjustments are recommended to improve readability. In Figure 4, overlapping axis labels are observed in the first two subplots, requiring precision adjustments.\n\nTheoretical Completeness: The stability proof in Section 3.2 regarding \"periodic outputs from constant event signal inputs\" is overly concise. Critical derivation steps should be supplemented.", "other_comments_or_suggestions": "Clarifying the assumptions in the CCNN equations and providing inference speed comparisons across different hardware platforms would enhance the paper’s practical relevance.", "questions_for_authors": "1. Figure 1(a): The purpose of the two rectangular boxes in the Event image is unclear. Do their spatial positions correspond to specific event-triggering patterns? Why were these regions selected for annotation?\n2. Biological Relevance of Figure 3: While visualizing human brain motion recognition processes, the text lacks explanations linking these results to dorsal visual pathway functions. Please supplement biological interpretations of these visualizations.\n3. The paper claims CCNN is inspired by the primary visual cortex but fails to clarify how model parameters relate to biological mechanisms. Biological constraints during model design should be explicitly discussed.\n4. The authors highlight real-time inference efficiency but provide no hardware deployment tests. Has the model been tested in real-world scenarios? What is the actual inference latency?\n5. Could the authors provide detailed hyperparameter selection criteria (e.g., learning rate, batch size) and their impact on results? Are the improvements dataset-specific or task-agnostic?\n6. What are the computational resource requirements and training time? Are there optimizations for resource-constrained environments?\n7. The current method is primarily used for classification. Can it be extended to other tasks (e.g., object detection, optical flow estimation)?\n8. Why does PointNet++ have a longer runtime despite fewer parameters? Shouldn't it have a shorter runtime despite fewer parameters? Why does the method in this paper have more parameters than the Frame-based method but has a shorter runtime?\n9. Have you considered combining CCNN with SNN (Spiking Neural Networks) to further enhance biological plausibility?\n10. Why was no comparison made with Event Transformer (EvT)? Have you considered using Transformer for event representation?\n11. Is CCNN still effective with low data quantities? Have small-sample learning experiments been conducted?", "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": "O3B3OZC0vv", "reviewer": "Reviewer_FeC7", "summary": "Current state-of-the-art event stream processing methods are data-driven deep learning methods. Although these models have achieved high accuracy, they are heavily dependent on the structure of the training dataset. At a time when event sensors are not yet popular and there is a lack of large-scale event stream training data, these methods cannot be directly deployed in the real world. Thus, event stream data processing requires novel processing methods. This paper presents an event signal processing inspired by dorsal stream visual pathways of the brain. The proposed framework utilized chaotic dynamics to express the event data, and combines it with traditional classification networks to realize the event classification and achieve the superior performance.", "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": "We sincerely appreciate your thorough evaluation and valuable feedback on our manuscript. We are also grateful for the constructive suggestions, which have helped us further refine the theoretical derivations, experimental design, and analysis in our paper. In response to your comments, we have revised and supplemented our manuscript, including providing a more detailed description of the biological background, conducting a more rigorous dynamic characteristics analysis (such as Lyapunov exponent analysis), and offering further clarification on key equations and experimental parameters. We believe these improvements enhance the clarity, completeness, and impact of our work. Below, we provide detailed responses to each comment and explain the corresponding revisions. \n\n**W1:** The author does not adequately describe the dynamic visual pathways of the real brain, which limits the reader's understanding of the paper. In the dynamic analysis section, the author only provides phase space plots and equilibrium point analysis, and lacks more rigorous analysis methods such as Lyapunov exponents.  \\\n**A1:** Thank you for your valuable feedback. We have supplemented the discussion on the dynamic visual pathways of the brain to enhance the understanding of the biological visual system. Additionally, we have incorporated discussions and calculations of the Lyapunov exponent to provide a rigorous mathematical analysis of the dynamic characteristics of CCNN neuron.\n\n**A1:** The chaotic dynamic framework proposed in this paper includes CCNN, CWT, and LPF. The CCNN is a brain-inspired network based on the primary visual cortex V1. After processing the event signals with CCNN, CWT and LPF are applied for analysis and extraction, enabling the detection of dynamic objects. This process corresponds to the brain's processing from V1 to MT.\n\n**A2:**  In Equation (4), $U(k)$ represents the modulation input, $Y(k)$ is the continuous output, $E(k)$ is the dynamic threshold, and  $e^{-\\alpha_f}$, $e^{-\\alpha_e }$ denote the exponential decay factors that record the previous input states. $V(e_k)$ represents the input event signal data.\n\n**A3:** The polarity of the event signal is a Boolean value that can only be 0 or 1. Therefore, the output periodic signal generated by the event data through the CCNN is constant.\n\n**A4:**  Thank you for your valuable suggestions. We will remove the transient time periods in Figures 4(d) and (e), keeping only the steady-state behavior to display the dynamic characteristics.\n\n**A5:** Thank you for your suggestion. This paper performs a Taylor expansion of $e^{-(U(k)-E(k))}$, retaining only the first two terms, where $x$ refers to $–(U(k)-E(k))$. We will revise the explanation in the paper accordingly.\n\n**A6:**  Thank you for your reminder. This is a detail expression error in the paper. It should be revised to 'In this case, $E(k)$ can be expressed as...'. The magnitude of $E(k)$ cannot be determined.\n\n**A7:** To analyze the stability of the equilibrium point, we need to compute the Jacobian matrix of the equation $E(k)^2-(U(k)-2)E(k)-\\frac{V_E}{1-e^{-\\alpha_e }} =0$, that is, by differentiating $F(E):J=\\frac{dF}{dE}=\\frac{d}{dE}(E^2-(U(k)-2)E-\\frac{V_E}{1-e^{-\\alpha_e }})$. The derivative is $J=2E-(U(n)-2)$. The Jacobian matrix at the equilibrium point $E^\\ast$ is:$J(E^\\ast )=2E^\\ast-(U(n)-2)$. When $E^\\ast=\\frac{U(k)-2-\\sqrt{(U(k)-2)^2+4V_E (1-e^{-\\alpha_e} )}}{2}, J(E^\\ast)<0$, indicating that the equilibrium point is stable (attractive point); when $E^\\ast=\\frac{U(k)-2+\\sqrt{(U(k)-2)^2+4V_E (1-e^{-\\alpha_e} )}}{2}, J(E^\\ast)>0$, indicating that the equilibrium point is unstable (possibly a saddle point).\n\n**A8:** Parameters of the CCNN model: $\\alpha_f = 0.1, \\alpha_e = 1.00, V_e = 50, U(0) = 0, E(0) = 0, Y(0) = 0$.\nParameters of the CWT: 'gaus1' is used as the base wavelet, with a scale range of 10.\nTraining parameters: The data is split into training, validation, and test sets in a 3:1:1 ratio, with the random seed set to 2024. The model was trained for 5 epochs, with a batch size of 16. During optimization, the cross-entropy loss function was used, and the Adam optimizer was applied with an initial learning rate of 1e-4. To alleviate overfitting, a Dropout layer was added before the fully connected layers, and early stopping was incorporated during training.\n\n**A9:** Let $F(x,y)$ be the generative function of the event representation. The input $S_{ij}$ is the sum of the real part of the coefficient matrix, corresponding to different types of event sequences. It is convolved with the low-pass filter $H(f)$, and the coordinate points corresponding to event sequences where $S_{ij} >0$ are assigned a value of 255, while others are assigned a value of 0, enabling the detection of moving objects.", "claims_and_evidence": "The claims made in the paper are well-supported by clear and convincing evidence. The theoretical justifications, experimental results, and comparisons with prior work effectively validate the proposed approach. The methodology is sound, and the conclusions drawn are consistent with the presented data.", "methods_and_evaluation_criteria": "The proposed methods and evaluation criteria are well-suited to the problem at hand. The methodological choices are well-justified, and the evaluation is conducted using appropriate benchmark datasets and metrics. The experiments are designed carefully, and the comparisons with baseline methods are meaningful. Overall, the paper adopts a sound approach to assessing the proposed method’s effectiveness.", "theoretical_claims": "The paper proposes a chaotic dynamical framework for processing event signals, utilizing a CCNN to encode event signals. In this approach, polarity-invariant event signals are encoded as periodic signals, while polarity-changing event signals are encoded as chaotic signals. The paper provides a comprehensive validation of the proposed theory, from theoretical derivations to experimental analyses. The theoretical claims appear to be well-supported, with logical derivations and rigorous justifications. The experimental results further reinforce the correctness of the theoretical framework, demonstrating its effectiveness in handling event signals. Overall, the paper presents a solid theoretical foundation and empirical validation for the proposed method.", "experimental_designs_or_analyses": "The paper employs a dorsal-stream-inspired chaotic dynamical framework to process event signals, generating a dorsal-stream-inspired event representation. This representation is then used as input to conventional deep learning models for object recognition experiments. The proposed approach achieves state-of-the-art performance on certain datasets, further validating the effectiveness of the framework. The experimental design is well-structured, covering multiple datasets and providing a comprehensive comparison with recent state-of-the-art methods. Additionally, the paper includes a complexity analysis of the model and IoU experiments, further reinforcing the feasibility of the proposed approach. The results are clearly presented, and the conclusions are well-supported by the experimental findings. Overall, the study provides a solid theoretical foundation and empirical validation for the proposed method.", "relation_to_broader_scientific_literature": "The paper builds upon prior work in event-based vision and chaotic dynamics, drawing inspiration from the dorsal visual stream to develop a novel event representation. Previous studies have explored event-based feature extraction and object recognition, but this work uniquely integrates a chaotic dynamical framework to encode event signals, distinguishing it from conventional approaches.", "essential_references_not_discussed": "The paper provides a thorough review of relevant literature and appropriately cites key prior works related to event-based vision, chaotic dynamical systems, and biologically inspired representations. Based on my review, I did not identify any essential references that are missing. The citations effectively contextualize the proposed approach within the broader scientific literature.", "other_strengths_and_weaknesses": "Strength\nThis paper refers to the dynamic visual cognition mechanism of the brain and proposes an event representation method based on chaotic dynamics. Generally, the results are interesting, the process is correct to the best of my knowledge. The paper is well organized and clearly written. \nWeakness\nThe author does not adequately describe the dynamic visual pathways of the real brain, which limits the reader's understanding of the paper. In the dynamic analysis section, the author only provides phase space plots and equilibrium point analysis, and lacks more rigorous analysis methods such as Lyapunov exponents.", "other_comments_or_suggestions": "It is recommended to use Lyapunov exponent to analyze the dynamic characteristics of CCNN neuron, which is a more reliable way.", "questions_for_authors": "1.  What is the correspondence between the proposed framework and the real brain? Event cameras mimic the three-layer structure of the peripheral retina in humans. What part of the visual cognition process does CCNN correspond to?\n2. The variables in Eq. (4) lack explanation, such as exp(-ae), exp(-af),  and V(ek) is the inputted event data?\n3. In Eq. (6) the authors says \" Thus, CCNN neurons output a periodic sequence Y(k) under constant stimulation, with the frequency of the period determined by the intensity of the input stimulus.\" However, in event stream processing, isn't the input signal a boolean variable of polarity? Therefore, in the framework proposed in this article, the frequency of the output periodic signal is constant, right?\n4. I observed that Fig. 4(d) and Fig. 4(e) seem to have transient behavior, which is unnecessary when showing the dynamic characteristics.\n5. In pg. 5, col. 2, line 248, the authors says \" Using the Taylor series expansion of exp(x)\", This sentence may cause misunderstanding. Which variable in the paper does x refer to?\n6. In pg. 5, col. 2, line 256, the authors says, \"In this case, E(k)>0 can be expressed as......\", why E(k) should greater than zero?\n7. Can the author analyze the stability of the equilibrium point, is it a saddle point or a focus point?\n8.  Could the author provide detailed parameter settings for CCNN, CWT and subsequent training models? This will be helpful for reproducing the work.\n9. Could you please clarify the role of F(x, y) in the wavelet transform experiment in Equation (15)?", "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:

Meta-review: This paper proposes a chaotic dynamics-based framework inspired by the dorsal visual pathway for processing event camera signals. Using Continuous Coupled Neural Networks (CCNN) and Continuous Wavelet Transform (CWT), the method encodes event sequences as periodic or chaotic signals and achieves strong performance on multiple event classification tasks. According to the reviewers, the paper integrates theoretical modeling, biological inspiration, and experimental validation effectively.
The proposed method demonstrates strong generalization, computational efficiency, and state-of-the-art accuracy across multiple datasets. The framework introduces a novel perspective by leveraging chaos theory and brain-inspired dynamics, which reviewers believe adds both theoretical and practical value. The open-sourced code and comprehensive experiments can also further enhance the paper’s reproducibility and credibility.

Some reviewers also pointed out that the biological connection to the dorsal stream might be weakly supported. According to reviewers, there are also missing analyses on CCNN parameter sensitivity, unclear variable definitions in equations, and limited dynamic system analysis (e.g., no Lyapunov exponent). Authors may want to further enhance the paper by addressing these aspects.
Decision: Accept (poster)