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International Joint Conference on neural Networks (IJCNN)

作者： Galluppi, Francesco Furber, Steve Univ Manchester Sch Comp Sci Manchester Lancs England

ISBN: (纸本)9781424496365

The introduction of axonal delays in networks of spiking neurons has enhanced the representational capabilities of neural networks, whilst also providing more biological realism. Approaches in neural coding such as rank order coding and polychronization have exploited the precise timing of action potential observed in real neurons. In a rank order code information is coded in the order of firing of a pool of neurons;on the other hand with polychronization it is the time of arrival of different spikes at the postsynaptic neuron which triggers different post-synaptic responses, with the axonal delays compensating for different timings in the afferents. In this paper we propose a model in which rank order coding is used to decode, represent and recall that symbol. To prove that the polychronous layer is able to do this a detector neuron is trained with a supervised learning strategy and associated with a single code. According to this premise the detector neuron only fires on the appearance of the associated code, even in the presence of noise. Tests prove that rank order coding and polychronization can be coupled to code and decode information such as intensity or significance using timing information in spiking neural networks in an effective way.

关键词： axonal delays decoding learning (artificial intelligence) neural coding neural nets neural networks polychronization polychronous layer postsynaptic neuron rank order code information representational capabilities spiking neural networks supervised learning strategy

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Noise-Robust Deep Spiking neural Networks with Temporal Information 58

Noise-Robust Deep Spiking Neural Networks with Temporal Info...

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58th ACM/IEEE Design Automation Conference (DAC)

作者： Park, Seongsik Lee, Dongjin Yoon, Sungroh Seoul Natl Univ Dept Elect & Comp Engn Seoul 08826 South Korea Seoul Natl Univ Inst New Media & Commun Seoul 08826 South Korea Seoul Natl Univ ASRI Seoul 08826 South Korea Seoul Natl Univ Interdisciplinary Program Artificial Intelligence Seoul 08826 South Korea

ISBN: (纸本)9781665432740

Spiking neural networks (SNNs) have emerged as energy-efficient neural networks with temporal information. SNNs have shown a superior efficiency on neuromorphic devices, but the devices are susceptible to noise, which hinders them from being applied in real-world applications. Several studies have increased noise robustness, but most of them considered neither deep SNNs nor temporal information. In this paper, we investigate the effect of noise on deep SNNs with various neural coding methods and present a noise-robust deep SNN with temporal information. With the proposed methods, we have achieved a deep SNN that is efficient and robust to spike deletion and jitter.

关键词： neural coding neuromorphic computing noise robustness spiking neural network(SNN) temporal coding

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Towards a Deep Learning Model of Retina: Retinal neural Encoding of Color Flash Patterns 7th

Towards a Deep Learning Model of Retina: Retinal Neural Enco...

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6th International Work-Conference on the Interplay between Natural and Artificial Computation (IWINAC)

作者： Lozano, Antonio Garrigos, Javier Javier Martinez, J. Manuel Ferrandez, J. Fernandez, Eduardo Univ Politecn Cartagena Dept Elect Tecnol Comp & Proyectos Cartagena Spain Univ Miguel Hernandez Inst Bioingn Alicante Spain

ISBN: (纸本)9783319597409;9783319597393

The retina is the first stage of visual neural information coding on the visual system, and several challenges remain on its functioning. Overcoming these challenges would suppose both a step further in the general understanding of the biological neural systems and a potential way to enhance millions of people's lives that suffer from visual degeneration or impairment. In this work, a data-driven deep learning approach is applied to learn the behavior of mice's retinal ganglion cells in response to light, as a step towards the development of a system able to mimic a real retina in terms of neural coding of visual stimuli.

关键词： Retina modeling neural coding Deep learning Convolutional neural networks

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Optimal quantization and suprathreshold stochastic resonance

Optimal quantization and suprathreshold stochastic resonance

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Conference on Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems III

作者： McDonnell, MD Stocks, NG Pearce, CEM Abbott, D Univ Adelaide Ctr Biomed Engn Adelaide SA 5005 Australia

ISBN: (纸本)0819458368

It is shown that Suprathreshold Stochastic Resonance (SSR) is effectively a way of using noise to perform quantization or lossy signal compression with a population of identical threshold-based devices. Quantization of an analog signal is a fundamental requirement for its efficient storage or compression in a digital system. This process will always result in a loss of quality, known as distortion, in a reproduction of the original signal. The distortion can be decreased by increasing the number of states available for encoding the signal (measured by the rate, or mutual information). Hence, designing a quantizer requires a tradeoff between distortion and rate. Quantization theory has recently been applied to the analysis of neural coding and here we examine the possibility that SSR is a possible mechanism used by populations of sensory neurons to quantize signals. In particular, we analyze the rate-distortion performance of SSR for a range of input SNR's and show that both the optimal distortion and optimal rate occurs for an input SNR of about 0 dB, which is a biologically plausible situation. Furthermore, we relax the constraint that all thresholds are identical, and find the optimal threshold values for a range of input SNRs. We find that for sufficiently small input SNRs, the optimal quantizer is one in which all thresholds are identical, that is, the SSR situation is optimal in this case.

关键词： suprathreshold stochastic resonance stochastic resonance quantization rate-distortion Analog to Digital Conversion noise neural coding dithering lossy source coding compression

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Approaches to Information-Theoretic Analysis of neural Activity

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Biological Theory 2006年第3期1卷 302-316页

作者： Victor, Jonathan D. Department of Neurology and Neuroscience Weill Medical College of Cornell University New York NY United States

Understanding how neurons represent, process, and manipulate information is one of the main goals of neuroscience. These issues are fundamentally abstract, and information theory plays a key role in formalizing and addressing them. However, application of information theory to experimental data is fraught with many challenges. Meeting these challenges has led to a variety of innovative analytical techniques, with complementary domains of applicability, assumptions, and goals. © 2006, Konrad Lorenz Institute for Evolution and Cognition Research.

关键词： direct method information theory metric space method neural coding sensory spike trains systems

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neural Representation of Hedonic Tastes in the Ventral Pallidum

Neural Representation of Hedonic Tastes in the Ventral Palli...

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作者： Itoga, Christy A University of Michigan

学位级别：doctor

The overall goal of this thesis was to investigate how the ventral pallidum tracks experimentally-induced changes in the hedonic value of tastes. Taste reactions allows us to measure how 'liked' or 'disliked' a taste is by a rat. These reactions are not purely reflexive as they are altered when circumstances change: hunger can increase and satiety can decrease the tastiness of foods (Berridge 1996) and a salt appetite can make a normally 'disliked' salt solution 'liked' (Berridge et al. 1984, Schulkin 1991). Therefore taste reactions reflect the palatability of tastes. Understanding how the reward pathway tracks changes in the hedonic value of tastes may elucidate how neural coding is affected in disorders such as drug addiction and obesity. The first project investigated how neurons in the ventral pallidum (VP) track changes in hedonic value of a sweet taste by a conditioned taste aversion. When a taste changed from 'liked' to 'disliked', the predominant VP response also changed: taste responsive VP cells tastes typically increase firing rate in response to 'liked' tastes whereas the hedonically devalued taste typically triggered a decrease in firing rate. The second project compared the effects of dopamine and opioid modulation of 'liked', relatively neutral, and 'disliked' taste stimuli. We found that opioid modulation enhanced the hedonic value of quinine (which is normally 'disliked'), but that only morphine resulted in a VP population response that mirrored the hedonic value of the tastes. The third project tested the effects of dopamine and opioid modulation on the hedonic value of a pair of cues (water infusions) and reward (a sucrose infusion). Overall, the cues did increase in hedonic value via association with reward, and the VP reflected the similarity in hedonic value between the cues and reward by responding similarly to cues and reward, under most drug conditions. Amphetamine decreased the proportion of units that responded to the first cue with an in

关键词： Ventral Pallidum neural Responses neural coding Hedonic Value Hedonia Taste Neurosciences Health Sciences

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Retinal coding: Encoding and Decoding in Natural Scenes

Retinal Coding: Encoding and Decoding in Natural Scenes

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作者： Hoshal, Benjamin The University of Chicago

学位级别：Ph.D., Doctor of Philosophy

Everything that the brain sees must first be encoded by the retina, which maintains a reliable representation of the visual world in many different, complex natural scenes while also adapting to stimulus changes. Decomposing the population code into independent and cell-cell interactions reveals how broad scene structure is encoded in the adapted retinal output. By recording from the same retina while presenting many different natural movies, we see that the population structure, characterized by strong interactions, is consistent across both natural and synthetic stimuli. We show that these interaction contribute to encoding scene identity, and demonstrate that leveraging this underlying interaction network improves scene decoding. This population structure likely arises in part from shared bipolar cell input as well as from gap junctions between retinal ganglion cells and amacrine cells. Separately, we use a task-agnostic deep architecture, and encoder-decoder, to model the retinal encoding process and characterize its representation of ‘time in the natural scene’ in a compressed latent space. In this end-to-end training, an encoder learns a compressed latent representation from the retinal ganglion cell population, while a decoder samples from this latent space to to generate the appropriate future scene frame. By comparing latent representation of retinal activity from three natural movies, we find that the retina has a generalizable encoding for time in natural scenes, and that this encoding can be used to decode future frames with up to 17ms resolution. Lastly, we explore methods to efficiently scale small population models up to a large population using an aggregate approach.

关键词： Natural scenes neural coding neural computation Population code Retina

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A MULTI-SPIKE APPROACH FOR ROBUST SOUND RECOGNITION 44

A MULTI-SPIKE APPROACH FOR ROBUST SOUND RECOGNITION

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Yu, Qiang Yao, Yanli Wang, Longbiao Tang, Huajin Dang, Jianwu Tianjin Univ Tianjin Key Lab Cognit Comp & Applicat Coll Intelligence & Comp Tianjin Peoples R China Sichuan Univ Coll Comp Sci Chengdu Sichuan Peoples R China

ISBN: (纸本)9781479981311

The extraordinary performance of the brain on various cognitive tasks motivates the design of a biologically plausible system for the challenging task of environmental sound recognition. In this paper, we propose a novel approach based on multi-spike learning and key-point encoding. Our encoding extracts local temporal and spectral information from the sound and converts it into spatiotemporal spike pattern, which is further learned by the following spiking neural networks. Our experiments demonstrate the robustness and effectiveness of our approach across a variety of noise conditions, outperforming other conventional baseline methods in both mismatched and multi-condition scenarios.

关键词： Sound recognition neural coding multi-spike learning neuromorphic computing

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Effects of tACS-Like Electrical Stimulation on Correlated Firing of Retinal Ganglion Cells: Part III

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EYE AND BRAIN 2022年 14卷 1-15页

作者： Amthor, Franklin R. Strang, Christianne E. Univ Alabama Birmingham Dept Psychol 1300 Univ Blvd Birmingham AL 35294 USA

Purpose: Transcranial alternating current stimulation (tACS) is a stimulation protocol used for learning enhancement and mitigation of cognitive dysfunction. Correlated firing has been postulated to be a meta-code that links neuronal spike responses associated with a single entity and may be an important component of high-level cognitive functions. Thus, changes in the covariance firing structure of CNS neurons such as retinal ganglion cells are one potential mechanism by which tACS can exert its effects. Materials and Methods: We used microelectrode arrays to record light-evoked spike responses of 24 retinal ganglion cells in 7 rabbit eyecup preparations and analyzed the covariance between 30 pairs of neighboring retinal ganglion cells before, during, and after 10-minute application of alternating currents of 1 microampere at 10 or 20 Hz. Results: tACS stimulation significantly changed the covariance structure of correlated firing in 60% of simultaneously recorded retinal ganglion cells. Application of tACS in the retinal preparation increased cross-covariance in 26% of cell pairs, an effect usually associated with increased light-evoked ganglion cell firing. tACS associated decreases in cross-covariance occurred in 37% of cell pairs. Increased covariance was more common in response to the first, 10-minute application of tACS in isolated retina preparation. Changes in covariance were rare after repeated stimulation, and more likely to result in decreased covariance. Conclusion: Retinal ganglion cell correlated firing is modulated by 1 microampere tACS currents showing that electrical stimulation can significantly and persistently change the structure of the correlated firing of simultaneously recorded rabbit retinal ganglion cells.

关键词： retina tACS mechanisms CNS in vitro model neural coding neuromodulation correlated firing cross covariance

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Periodic neural codes and sound localization in barn owls

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INVOLVE, A JOURNAL OF MATHEMATICS 2022年第1期15卷 1-37页

作者： Brown, Lindsey S. Curto, Carina Princeton Univ Princeton Neurosci Inst Princeton NJ 08544 USA Penn State Univ Dept Math State Coll PA USA

Inspired by the sound localization system of the barn owl, we define a new class of neural codes, called periodic codes, and study their basic properties. Periodic codes are binary codes with a special patterned form that reflects the periodicity of the stimulus. Because these codes can be used by the owl to localize sounds within a convex set of angles, we investigate whether they are examples of convex codes, which have previously been studied for hippocampal place cells. We find that periodic codes are typically not convex, but can be completed to convex codes in the presence of noise. We introduce the convex closure and Hamming distance completion as ways of adding codewords to make a code convex, and describe the convex closure of a periodic code. We also find that the probability of the convex closure arising stochastically is greater for sparser codes. Finally, we provide an algebraic method using the neural ideal to detect if a code is periodic. We find that properties of periodic codes help to explain several aspects of the behavior observed in the sound localization system of the barn owl, including common errors in localizing pure tones.

关键词： neural coding convex codes periodic codes sound localization neural ideal

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