nips nips2008 nips2008-220 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Zhi Yang, Qi Zhao, Wentai Liu
Abstract: This paper presents a spike feature extraction algorithm that targets real-time spike sorting and facilitates miniaturized microchip implementation. The proposed algorithm has been evaluated on synthesized waveforms and experimentally recorded sequences. When compared with many spike sorting approaches our algorithm demonstrates improved speed, accuracy and allows unsupervised execution. A preliminary hardware implementation has been realized using an integrated microchip interfaced with a personal computer. 1
Reference: text
sentIndex sentText sentNum sentScore
1 edu Abstract This paper presents a spike feature extraction algorithm that targets real-time spike sorting and facilitates miniaturized microchip implementation. [sent-3, score-1.848]
2 When compared with many spike sorting approaches our algorithm demonstrates improved speed, accuracy and allows unsupervised execution. [sent-5, score-0.794]
3 A preliminary hardware implementation has been realized using an integrated microchip interfaced with a personal computer. [sent-6, score-0.352]
4 In order for a spike feature extraction algorithm to be functional as a small device with real-time low-latency processing and low power operation it must be efficient in both computation and IC implementation. [sent-9, score-0.939]
5 Implementing spike sorting before data telemetry offers many significant advantages. [sent-10, score-0.869]
6 Spike feature extraction provides the necessary information required to sort spikes from raw sampled data. [sent-11, score-0.545]
7 With this information each spike event can be represented by its unique features and firing time, resulting in significant data compression. [sent-12, score-0.671]
8 A data transceiver designed with the current semiconductor technology can simultaneously support a large number of recording channels for a microchip implementation to extract the spike feature. [sent-13, score-0.894]
9 System integration using wireless power telemetry or a rechargeable battery as well as wireless data telemetry removes the need for tethering wires. [sent-14, score-0.367]
10 Frequently used spike feature extraction algorithms include principal component analysis (PCA) [1], bayesian algorithm [2], template matching [3], wavelets [4] and independent component analysis (ICA) [5], which demand significant computation. [sent-16, score-0.971]
11 In part, complex algorithm procedures are applied to mediate the effects of noise and distortion in the recording process. [sent-18, score-0.3]
12 The associated noise includes ion channel noise, activities from distant neurons, field potentials, thermal noise and circuit noise. [sent-19, score-0.324]
13 Significant sampling distortion is also present since it is unrealistic to synchronize the sampling clock with individual recorded spikes. [sent-20, score-0.317]
14 This paper reports a new spike feature extraction algorithm which is suitable for real-time spike sorting and enables integrated microchip implementation. [sent-21, score-1.89]
15 1 Related Work PCA Based Spike Feature Extraction PCA is a feature extraction algorithm widely employed for spike sorting. [sent-23, score-0.886]
16 However, recorded spikes are usually corrupted by large low frequency noise and distortion, which blur sample correlation and compromise the quality of the estimated covariance matrix and its eigenvectors. [sent-26, score-0.627]
17 As a result, PCA may fail to resolve spike clusters in noisy recordings. [sent-27, score-0.726]
18 2 Variable Selection Techniques As a complementary approach to dimensionality reduction algorithms, Jolliffe discussed a general feature extraction algorithm based on a subset of samples in the classic work [6]. [sent-29, score-0.358]
19 The power and area are the primary problems with the microchip implementation of other spike feature extraction algorithms. [sent-35, score-1.14]
20 3 Our Approach We have developed a spike feature extraction algorithm based on informative samples. [sent-39, score-1.046]
21 The theoretical framework includes neuronal geometry signatures, noise shaping, and informative sample selection. [sent-40, score-0.468]
22 By evaluating neuronal geometry signatures with the compartment model, we find that high frequency signal spectrum may contain useful information to differentiate neurons. [sent-41, score-0.565]
23 Studying the noise properties has revealed that a frequency shaping filter can be used to boost the SNR. [sent-42, score-0.369]
24 The sample selection technique using estimated entropy identifies informative samples for sorting spikes. [sent-43, score-0.441]
25 In addition, a preliminary IC implementation of the algorithm has been reported [7, 8] and further integrated onto a multi-channel neural recording IC with wireless telemetry [9]. [sent-44, score-0.291]
26 jm (τ )W (t − τ )dτ, (2) The recorded waveforms from neurons with similar ion channel populations can be very similar. [sent-52, score-0.439]
27 A general spike sorting algorithm frequently fails to resolve such ambiguity and may report a single, large, spike cluster. [sent-53, score-1.428]
28 Assume W1 (t) and W2 (t) as the geometry kernel functions of two neurons with the same ion channel population, the difference between the two spikes is △V (t) = jm (τ )[W1 (t − τ ) − W2 (t − τ )]dτ, (3) Small waveform differences appear if (W1 (t) − W2 (t))dt ≈ 0. [sent-55, score-0.796]
29 The condition of [W1 (t) − W2 (t)]dt ≈ 0 is equivalent to F(W1 −W2 ) ≈ 0|f =0Hz , which implies that the waveform difference caused by the geometry kernel functions has small contribution at lower frequency spectrum. [sent-59, score-0.424]
30 A more quantitative explanation can be given by studying the derivative of F(△V ) with respect to the frequency using Eq. [sent-60, score-0.305]
31 5, on the other hand, exhibits a strong frequency dependency within the dominant spectrum of F(jm ). [sent-68, score-0.281]
32 In summary, the waveform difference between similar neurons caused by geometry functions satisfies the following conditions F(△V ) ≈ 0|f =0Hz (7) ∂F (△V ) ≈ 4π 2 f F(jm ) (W1 (t) − W2 (t))t sin(2πf t) dt ∝ f. [sent-70, score-0.287]
33 7, ∂F ∂f ) is linear to frequency f at low frequency region, as sin(2πf t) ≈ 1. [sent-72, score-0.342]
34 The strong 2πf t emphasis on frequency shows that F(△V ) exhibits a higher frequency spectrum. [sent-73, score-0.342]
35 2 Noise and Sample Distortion An estimated power spectrum of noise associated with recorded neural signal, where the dominance of low frequency noise is clear, is plotted in Figure 1. [sent-76, score-0.623]
36 In case a fast transition edge is sampled 4 times, the sampling distortion can be more than 10% of the spike peak-to-peak magnitude. [sent-85, score-0.827]
37 4 Sample Information In order to use informative samples to sort spikes, it is necessary to quantify the information carried by individual spike samples. [sent-89, score-0.979]
38 Intuitively, a sample is considered to be informative if the superimposed spikes can be classified into multiple clusters by evaluating that sample alone. [sent-90, score-0.68]
39 Therefore, the amount of spikes to compute information can be reduced to a relatively small number, which should allow hardware implementation in terms of storage space and computation complexity. [sent-99, score-0.397]
40 With the synthesized spike data we used, each sequence contains 3 neuronal sources with similar firing rate. [sent-100, score-0.756]
41 Quantitative comparisons to investigate the existence of informative samples in noisy spikes have been done. [sent-104, score-0.495]
42 Results using synthesized spikes with recordings from neocortex and basal ganglia [4] are shown in Figure 2. [sent-105, score-0.394]
43 Second, it is necessary to create informative samples if due to severe noise, distortion and similarity of spike clusters, few of the samples is informative. [sent-108, score-1.103]
44 As a constraint to create informative samples, the computation and storage space have to be feasible for microchip implementation. [sent-109, score-0.358]
45 5 Create Informative Samples Using Frequency Shaping Filter As analyzed in Section 3, a frequency shaping filter can be used to manifest different geometry kernel functions, reduce noise and redistribute distortion among spike samples. [sent-110, score-1.313]
46 7 spike derivative spike 1 spike derivative spike 0. [sent-113, score-2.752]
47 1 0 5 10 (b) spike derivative spike 1 0 −0. [sent-123, score-1.376]
48 1 15 20 25 sample number 30 35 spike derivative spike spike derivative spike 0. [sent-124, score-2.8]
49 4 5 10 15 20 25 sample number (e) 30 35 35 spike derivative spike 0. [sent-128, score-1.424]
50 1 0 5 (g) 10 15 20 25 sample number 30 35 (h) Figure 2: (a) - (h) information carried by samples from spikes and their derivatives. [sent-168, score-0.42]
51 The black solid line and red dotted line represent the sample information from spikes and their derivatives, respectively. [sent-170, score-0.337]
52 designed to boost high frequency spike features, which should be localized and less correlated if examined in time domain. [sent-171, score-0.838]
53 In this section, we use derivative operation as an example to illustrate the usefulness of the frequency shaping filter, and further demonstrate that the filter creates additional informative samples. [sent-172, score-0.559]
54 In a discrete time spike sequence, the frequency response of taking derivative is H(f ) = 2ejπf /2 sin(πf /fs ), (9) where fs is the sampling frequency of the ADC. [sent-173, score-1.114]
55 1, the difference between neuron geometry kernel functions W (t) of similar spikes is contained in the higher frequency components, which should be emphasized by derivative operation. [sent-175, score-0.716]
56 Intuitively, low frequency noise is reduced and the high frequency thermal noise is amplified, as shown in Figure 1 (b). [sent-177, score-0.554]
57 The quantitative impact of the frequency shaping filter on noise is affected by the recording system and biological environment, and the typical values of α we observe vary around 2 within the signal band as shown in Figure 1. [sent-178, score-0.481]
58 In case λ is less than 1, SNR further increases, which favors spike sorting from the noise perspective. [sent-181, score-0.872]
59 In the original waveforms, samples close to peaks suffer less distortion compared with those in transition. [sent-183, score-0.334]
60 In these data, the black solid lines represent information carried by the samples from spikes and the dotted red lines represent the derivatives. [sent-188, score-0.399]
61 The spike data are 8 challenging sequences from [4]. [sent-189, score-0.634]
62 The corresponding feature extraction results using the most informative samples from spikes as well as their derivatives are shown in Figure 3 (a) - (h), which clearly presents a 3 cluster configuration. [sent-194, score-0.776]
63 6 (an) Figure 3: feature extraction results using the proposed algorithm and competing algorithms. [sent-731, score-0.281]
64 (a) (h) display the extracted features using the most informative samples of spikes and their derivatives (proposed). [sent-732, score-0.655]
65 (i) - (p) display the extracted features using a subset of samples includes the peaks of the spike derivative and spike height (implemented on chip, proposed). [sent-733, score-1.711]
66 (ag) - (an) display spike peaks based feature extraction. [sent-736, score-0.835]
67 Nonlinear energy operator (NEO) [11] is used as the spike detection algorithm. [sent-738, score-0.634]
68 ) Table 1: Accuracy comparison of using different spike feature extraction algorithms Sequence Number 1 2 3 4 5 6 7 8 Informative Samples 97. [sent-742, score-0.886]
69 0% Note: Informative samples are harvested from both spikes and their derivatives. [sent-782, score-0.335]
70 6 Experiments Synthesized spike sequences used in Figure 2 are applied to compare the sorting accuracies of different approaches. [sent-785, score-0.794]
71 Feature extraction using the pre-specified subset consists of the peaks of the spike derivative as well as the height of the original spike is shown in Figure 3 (i) - (p). [sent-786, score-1.7]
72 g, PCA, wavelets, spike peaks and width are also shown in Figure 3. [sent-788, score-0.732]
73 The extracted spike features are clustered on a PC [12]. [sent-789, score-0.721]
74 About 5% overlapping spikes are ignored to clearly quantify the performance of different spike feature extraction algorithms. [sent-790, score-1.221]
75 The proposed feature extraction algorithm including the most informative samples (corresponding to Figure 3 (a) - (h)) achieves the highest accuracy (97. [sent-791, score-0.485]
76 2 0 1 (b) (f) (g) (c) (h) (i) (j) (k) Figure 4: (a) recorded spikes from cat cerebral cortex are superimposed, (b) the extracted spike features using a subset of samples are plotted and grouped with a clustering algorithm implemented on PC. [sent-809, score-1.354]
77 (d) - (k) individual spike clusters superimposed in (c) are displayed. [sent-811, score-0.796]
78 The counterpart algorithms include PCA, wavelets and spike peaks and width give 78. [sent-821, score-0.817]
79 An example with overlapped spike clusters is selected for demonstration. [sent-827, score-0.726]
80 In Figure 4 (a), the detected 1210 spikes are superimposed. [sent-830, score-0.289]
81 Extracted spike features using the pre-specified subset of samples implemented on chip are shown in Figure 4 (b). [sent-831, score-0.817]
82 Less than 10 % of noisy spikes and overlapping spikes are discarded, the rest are classified and plotted in Figure 4(c). [sent-833, score-0.618]
83 To further quantify the validity of the classified spike clusters, superimposed clusters in Figure 4(c) are individually plotted in Figure 4(d)-(k). [sent-834, score-0.905]
84 The second example containing more than 4000 spikes recorded from a monkey is shown in Figure 5. [sent-835, score-0.363]
85 Extracted features using the pre-specified subset of informative samples are shown in Figure 5 (b). [sent-837, score-0.303]
86 A zoom in of Figure 5 (b) is plotted in Figure 5 (c) to display the isolation quality of clusters in feature space. [sent-838, score-0.286]
87 The classified spike clusters using the prespecified subset of informative samples are plotted in Figure 6 (a) - (e). [sent-840, score-1.057]
88 To demonstrate that the informative samples based sorting does not over partitioning the data set, the derivatives of spike clusters plotted in Figure 6 (a) - (e) are also plotted in Figure 6 (f)-(j) with the same color indication. [sent-842, score-1.278]
89 7 Conclusion A sample selection based spike feature extraction algorithm is reported in this paper. [sent-844, score-0.934]
90 The theoretical framework includes neuronal geometry signatures, frequency shaping filter, and informative sample selection. [sent-845, score-0.681]
91 Unlike PCA which uses correlated features, the sample selection algorithm focuses on localized and uncorrelated features which are strengthened by the frequency shaping filter. [sent-846, score-0.409]
92 With simulated spike waveforms from a public data base, the algorithm demonstrates an improved sorting accuracy compared with many competing algorithms. [sent-847, score-0.913]
93 The algorithm is designed for integrated microchip implementation and performing real-time spike sorting. [sent-848, score-0.877]
94 2 0 (d) Figure 5: (a) detected spikes from a monkey, (b) extracted spike features using a subset of samples, (c) zoom in of (b) for better visualization; (d) extracted features using PCA. [sent-890, score-1.156]
95 Power feasibility of implantable digital spike sorting circuits for neural prosthetic systems. [sent-894, score-0.864]
96 Automated spike sorting using density grid contour clustering and subtractive waveform decomposition. [sent-903, score-0.9]
97 Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. [sent-907, score-0.879]
98 A neuron signature based spike feature extraction algorithm for on-chip implementation. [sent-919, score-0.94]
99 A 128 channel 6mW wireless neural recording IC with on-the-fly spike sorting and UWB transmitter. [sent-930, score-0.971]
100 Pattern and inhibition-dependent invasion of pyramidal cell dendrites by fast spikes in the hippocampus in vivo. [sent-934, score-0.292]
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