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Stimulus-invariant aspects of the retinal code drive discriminability of natural scenes

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2024年第52期121卷 e2313676121页

作者： Hoshal, Benjamin D. Holmes, Caroline M. Bojanek, Kyle Salisbury, Jared M. Berry II, Michael J. Marre, Olivier Palmer, Stephanie E. Harvard Univ Dept Organism & Evolutionary Biol Cambridge MA 02138 USA Univ Chicago Comm Computat Neurosci Dept Organismal Biol & Anat Chicago IL 60637 USA Princeton Univ Dept Phys Princeton NJ 08540 USA Univ Chicago Dept Organismal Biol & Anat Chicago IL 60637 USA Univ Chicago Dept Phys Chicago IL 60637 USA Princeton Univ Princeton Neurosci Inst Dept Mol Biol Princeton NJ 08540 USA Sorbonne Univ Inst Vis INSERM F-75012 Paris France Princeton Univ Ctr Phys Biol Funct Dept Phys Princeton NJ 08540 USA

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. This study quantifies whether and how the brain selectively encodes stimulus features about scene identity in complex naturalistic environments. While a wealth of previous work has dug into the static and dynamic features of the population code in retinal ganglion cells (RGCs), less is known about how populations form both flexible and reliable encoding in natural moving scenes. We record from the larval salamander retina responding to five different natural movies, over many repeats, and use these data to characterize the population code in terms of single-cell fluctuations in rate and pairwise couplings between cells. Decomposing the population code into independent and cell-cell interactions reveals how broad scene structure is encoded in the retinal output. while the single-cell activity adapts to different stimuli, the population structure captured in the sparse, strong couplings is consistent across natural movies as well as synthetic stimuli. We show that these interactions contribute to encoding scene identity. We also demonstrate that this structure likely arises in part from shared bipolar cell input as well as from gap junctions between RGCs and amacrine cells.

关键词： neural coding natural scenes retina neural computation population code

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Single cortical neurons as deep artificial neural networks

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NEURON 2021年第17期109卷 2727-+页

作者： Beniaguev, David Segev, Idan London, Michael Hebrew Univ Jerusalem Edmond & Lily Safra Ctr Brain Sci ELSC IL-91904 Jerusalem Israel Hebrew Univ Jerusalem Dept Neurobiol IL-91904 Jerusalem Israel

Utilizing recent advances in machine learning, we introduce a systematic approach to characterize neurons' input/output (I/O) mapping complexity. Deep neural networks (DNNs) were trained to faithfully replicate the I/O function of various biophysical models of cortical neurons at millisecond (spiking) resolution. A temporally convolutional DNN with five to eight layers was required to capture the I/O mapping of a realistic model of a layer 5 cortical pyramidal cell (L5PC). This DNN generalized well when presented with inputs widely outside the training distribution. When NMDA receptors were removed, a much simpler network (fully connected neural network with one hidden layer) was sufficient to fit the model. Analysis of the DNNs' weight matrices revealed that synaptic integration in dendritic branches could be conceptualized as pattern matching from a set of spatiotemporal templates. This study provides a unified characterization of the computational complexity of single neurons and suggests that cortical networks therefore have a unique architecture, potentially supporting their computational power.

关键词： deep learning machine learning synaptic integration cortical pyramidal neuron compartmental model dendritic nonlinearities dendritic computation neural coding NMDA spike calcium spike

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The effect of stimulus intensity on neural envelope tracking

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HEARING RESEARCH 2021年 403卷 108175-108175页

作者： Verschueren, Eline Vanthornhout, Jonas Francart, Tom KU Leuven Univ Leuven Res Grp Expt Otorhinolaryngol ExpORL Dept Neurosci Herestr 49Bus 721 B-3000 Leuven Belgium

Objectives In recent years, there has been significant interest in recovering the temporal envelope of a speech signal from the neural response to investigate neural speech processing. The research focus is now broadening from neural speech processing in normal-hearing listeners towards hearing-impaired listeners. When testing hearing-impaired listeners, speech has to be amplified to resemble the effect of a hearing aid and compensate for peripheral hearing loss. Today it is not known with certainty how or if neural speech tracking is influenced by sound amplification. As these higher intensities could influence the outcome, we investigated the influence of stimulus intensity on neural speech tracking. Design We recorded the electroencephalogram (EEG) of 20 normal-hearing participants while they listened to a narrated story. The story was presented at intensities from 10 to 80 dB A. To investigate the brain responses, we analyzed neural tracking of the speech envelope by reconstructing the envelope from the EEG using a linear decoder and by correlating the reconstructed with the actual envelope. We investigated the delta (0.5-4 Hz) and the theta (4-8 Hz) band for each intensity. We also investigated the latencies and amplitudes of the responses in more detail using temporal response functions, which are the estimated linear response functions between the stimulus envelope and the EEG. Results neural envelope tracking is dependent on stimulus intensity in both the TRF and envelope reconstruction analysis. However, provided that the decoder is applied to the same stimulus intensity as it was trained on, envelope reconstruction is robust to stimulus intensity. Besides, neural envelope tracking in the delta (but not theta) band seems to relate to speech intelligibility. Similar to the linear decoder analysis, TRF amplitudes and latencies are dependent on stimulus intensity: The amplitude of peak 1 (30-50 ms) increases, and the latency of peak 2 (140-160 ms) decreases wi

关键词： neural coding Natural speech Stimulus intensity EEG Speech understanding

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Prelimbic neuron assemblies with delayed activation encode the economic decision-making process in a bandit game

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BEHAVIOURAL BRAIN RESEARCH 2023年第1期447卷 114419页

作者： Ma, Xiao Wang, Zhenni Lou, Shihao Guo, Hui Yang, Yupeng Univ Sci & Technol China Sch Life Sci Div Life Sci & Med CAS Key Lab Brain Funct & Dis Hefei 230027 Peoples R China

The medial prefrontal cortex (mPFC) is critical to an animal's value-based decision-making process. However, due to heterogeneity of local mPFC neurons, which neuron group and how it contributes to the alteration of the animal's decision is yet to be explored. And the effect of empty reward in this process is often neglected. Here, we adopted a two-port bandit game paradigm for mice and applied synchronized calcium imaging to the prelimbic area of the mPFC. The results showed that neurons recruited in the bandit game exhibit three distinct firing patterns. Specially, neurons with delayed activation (deA neurons1) carried exclusive information on reward type and changes of choice value. We demonstrated that these deA neurons were essential for the construction of choice-outcome correlation and the trial-to-trial modification of decision. Additionally, we found that in a long-term gambling game, members of the deA neuron assembly were dynamically shifting while maintaining the function, and the importance of empty reward feedbacks were gradually elevated to the same level as reward. Together, these results revealed a vital role for prelimbic deA neurons in the gambling tasks and a new perspective on the encoding of economic decision-making.

关键词： Medial prefrontal cortex Calcium imaging Decision-making neural coding Choice value Empty reward

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Speed-invariant encoding of looming object distance requires power law spike rate adaptation

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2013年第33期110卷 13624-13629页

作者： Clarke, Stephen E. Naud, Richard Longtin, Andre Maler, Leonard Univ Ottawa Dept Cellular & Mol Med Ottawa ON K1H 8M5 Canada Univ Ottawa Dept Phys Ottawa ON K1H 8M5 Canada

neural representations of a moving object's distance and approach speed are essential for determining appropriate orienting responses, such as those observed in the localization behaviors of the weakly electric fish, Apteronotus leptorhynchus. We demonstrate that a power law form of spike rate adaptation transforms an electroreceptor afferent's response to "looming" object motion, effectively parsing information about distance and approach speed into distinct measures of the firing rate. Neurons with dynamics characterized by fixed time scales are shown to confound estimates of object distance and speed. Conversely, power law adaptation modifies an electroreceptor afferent's response according to the time scales present in the stimulus, generating a rate code for looming object distance that is invariant to speed and acceleration. Consequently, estimates of both object distance and approach speed can be uniquely determined from an electroreceptor afferent's firing rate, a multiplexed neural code operating over the extended time scales associated with behaviorally relevant stimuli.

关键词： spike frequency adaptation perceptual invariance sensory transformations neural coding

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Interaction between neuronal encoding and population dynamics during categorization task switching in parietal cortex

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NEURON 2021年第4期109卷 700-712.e4页

作者： Mohan, Krithika Zhu, Ou Freedman, David J. Univ Chicago Dept Neurobiol Chicago IL 60637 USA Univ Chicago Grossman Inst Neurosci Quantitat Biol & Human Beh Chicago IL 60637 USA

Primates excel at categorization, a cognitive process for assigning stimuli into behaviorally relevant groups. Categories are encoded in multiple brain areas and tasks, yet it remains unclear how neural encoding and dynamics support cognitive tasks with different demands. We recorded from parietal cortex during flexible switching between categorization tasks with distinct cognitive and motor demands and also studied recurrent neural networks (RNNs) trained on the same tasks. In the one-interval categorization task (OIC), monkeys rapidly reported their decisions with a saccade. In the delayed match-to-category (DMC) task, monkeys decided whether sequentially presented stimuli were categorical matches. Neuronal category encoding generalized across tasks, but categorical encoding was more binary-like in the DMC task and more graded in the OIC task. Furthermore, analysis of trained RNNs supports the hypothesis that binary-like encoding in DMC arises through compression of graded feature encoding by attractor dynamics underlying stimulus maintenance and/or comparison in working memory.

关键词： Attractor dynamics Parietal cortex Categorization Task switching neural coding Working memory Recurrent neural network

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Statistical analysis and optimality of neural systems

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NEURON 2021年第7期109卷 1227-1241.e5页

作者： Mlynarski, Wiktor Hledik, Michal Sokolowski, Thomas R. Tkacik, GaSper IST Austria Campus 1 A-3400 Klosterneuburg Austria Frankfurt Inst Adv Studies Ruth Moufang Str 1 D-60438 Frankfurt Germany

Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks and proceeds to mathematically work out testable consequences of such optimality;parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on the efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function. Traditionally, these two approaches were developed independently and applied separately. Here, we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy ?optimization priors.?This family defines a smooth interpolation between a data-rich inference regime and a data-limited prediction regime. Using three neuroscience datasets, we demonstrate that our framework allows one to address fundamental challenges relating to inference in high-dimensional, biological problems.

关键词： neural coding evolution optimization statistical inference

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Alzheimer's pathology causes impaired inhibitory connections and reactivation of spatial codes during spatial navigation

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CELL REPORTS 2021年第3期35卷 109008-109008页

作者： Prince, Stephanie M. Paulson, Abigail L. Jeong, Nuri Zhang, Lu Amigues, Solange Singer, Annabelle C. Emory Univ Coulter Dept Biomed Engn Atlanta GA 30332 USA Georgia Inst Technol Atlanta GA 30332 USA Emory Univ Grad Div Biol & Biomed Sci Neurosci Grad Program Atlanta GA 30322 USA

Synapse loss and altered synaptic strength are thought to underlie cognitive impairment in Alzheimer's disease (AD) by disrupting neural activity essential for memory. While synaptic dysfunction in AD has been well characterized in anesthetized animals and in vitro, it remains unknown how synaptic transmission is altered during behavior. By measuring synaptic efficacy as mice navigate in a virtual reality task, we find deficits in interneuron connection strength onto pyramidal cells in hippocampal CA1 in the 5XFAD mouse model of AD. These inhibitory synaptic deficits are most pronounced during sharp-wave ripples, network oscillations important for memory that require inhibition. Indeed, 5XFAD mice exhibit fewer and shorter sharp-wave ripples with impaired place cell reactivation. By showing inhibitory synaptic dysfunction in 5XFAD mice during spatial navigation behavior and suggesting a synaptic mechanism underlying deficits in network activity essential for memory, this work bridges the gap between synaptic and neural activity deficits in AD.

关键词： Alzheimer’s disease interneurons monosynaptic connections sharp-wave ripples 5XFAD neural coding reactivation place cells inhibition

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Encoding of acquired sound-sequence salience by auditory cortical offset responses

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CELL REPORTS 2021年第5期37卷 109927-109927页

作者： Lee, Joonyeup Rothschild, Gideon Univ Michigan Dept Psychol Ann Arbor MI 48109 USA Univ Michigan Kresge Hearing Res Inst Ann Arbor MI 48109 USA Univ Michigan Dept Otolaryngol Head & Neck Surg Ann Arbor MI 48109 USA

Behaviorally relevant sounds are often composed of distinct acoustic units organized into specific temporal sequences. The meaning of such sound sequences can therefore be fully recognized only when they have terminated. However, the neural mechanisms underlying the perception of sound sequences remain unclear. Here, we use two-photon calcium imaging in the auditory cortex of behaving mice to test the hypothesis that neural responses to termination of sound sequences ("Off-responses") encode their acoustic history and behavioral salience. We find that auditory cortical Off-responses encode preceding sound sequences and that learning to associate a sound sequence with a reward induces enhancement of Off-responses relative to responses during the sound sequence ("On-responses"). Furthermore, learning enhances network-level discriminability of sound sequences by Off-responses. Last, learning-induced plasticity of Off-responses but not On-responses lasts to the next day. These findings identify auditory cortical Off-responses as a key neural signature of acquired sound-sequence salience.

关键词： auditory cortex off responses sound sequences learning neural coding two-photon imaging

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Synchronized Response to Grayscale Image Inputs in Chaotic Cellular neural Network

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JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE 2015年第1期2卷 26-29页

作者： Fujiwara, Masayuki Yamaguchi, Akihiro Kubo, Masao Fukuoka Inst Technol Dept Informat & Syst Engn Higashi Ku Fukuoka 8110116 Japan Natl Def Acad Japan Dept Comp Sci Yokosuka Kanagawa 2398686 Japan

In this study, the synchronized response in the chaotic cellular neural network for a grayscale visual stimulus was investigated in the viewpoint of neural coding. Simple gradation patterns were used as visual stimuli and the synchronized response was analyzed by the correlation of the spike firing times. As a result, synchronized responses were observed for the neurons that had similar input values and formed chaotic cell assemblies. Each assembly was distinguished from the others in terms of cross-correlation.

关键词： chaotic synchronization neural coding spike response model visual segmentation

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