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Of mice and monkeys: Somatosensory processing in two prominent animal models

PROGRESS IN NEUROBIOLOGY 2021年第0期201卷 102008-102008页

作者： O'Connor, Daniel H. Krubitzer, Leah Bensmaia, Sliman Johns Hopkins Univ Sch Med Kavli Neurosci Discovery Inst Solomon H Snyder Dept Neurosci Baltimore MD USA Johns Hopkins Univ Zanvyl Krieger Mind Brain Inst Baltimore MD USA Univ Calif Davis Dept Psychol Davis CA 95616 USA Univ Calif Davis Ctr Neurosci Davis CA 95616 USA Univ Chicago Dept Organismal Biol & Anat 1025 E 57Th St Chicago IL 60637 USA Univ Chicago Comm Computat Neurosci Chicago IL 60637 USA Univ Chicago Grossman Inst Neurosci Quantitat Biol & Human Beh Chicago IL 60637 USA

Our understanding of the neural basis of somatosensation is based largely on studies of the whisker system of mice and rats and the hands of macaque monkeys. Results across these animal models are often interpreted as providing direct insight into human somatosensation. Work on these systems has proceeded in parallel, capitalizing on the strengths of each model, but has rarely been considered as a whole. This lack of integration promotes a piecemeal understanding of somatosensation. Here, we examine the functions and morphologies of whiskers of mice and rats, the hands of macaque monkeys, and the somatosensory neuraxes of these three species. We then discuss how somatosensory information is encoded in their respective nervous systems, highlighting similarities and differences. We reflect on the limitations of these models of human somatosensation and consider key gaps in our understanding of the neural basis of somatosensation.

关键词： Rats Primates neural coding Touch Proprioception Comparative neuroscience

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Explaining face representation in the primate brain using different computational models

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CURRENT BIOLOGY 2021年第13期31卷 2785-+页

作者： Chang, Le Egger, Bernhard Vetter, Thomas Tsao, Doris Y. Caltech Div Biol & Biol Engn Computat & Neural Syst Pasadena CA 91125 USA Chinese Acad Sci Inst Neurosci CAS Ctr Excellence Brain Sci & Intelligence Techn Key Lab Primate Neurobiol Shanghai 200031 Peoples R China Howard Hughes Med Inst Pasadena CA 91125 USA Univ Basel Dept Math & Comp Sci Basel Switzerland MIT Dept Brain & Cognit Sci Cambridge MA 02139 USA

Understanding how the brain represents the identity of complex objects is a central challenge of visual neuroscience. The principles governing object processing have been extensively studied in the macaque face patch system, a sub-network of inferotemporal (IT) cortex specialized for face processing. A previous study reported that single face patch neurons encode axes of a generative model called the "active appearance"model, which transforms 50D feature vectors separately representing facial shape and facial texture into facial images. However, a systematic investigation comparing this model to other computational models, especially convolutional neural network models that have shown success in explaining neural responses in the ventral visual stream, has been lacking. Here, we recorded responses of cells in the most anterior face patch anterior medial (AM) to a large set of real face images and compared a large number of models for explaining neural responses. We found that the active appearance model better explained responses than any other model except CORnet-Z, a feedforward deep neural network trained on general object classification to classify non-face images, whose performance it tied on some face image sets and exceeded on others. Surprisingly, deep neural networks trained specifically on facial identification did not explain neural responses well. A major reason is that units in the network, unlike neurons, are less modulated by face related factors unrelated to facial identification, such as illumination.

关键词： inferotemporal cortex primate vision face processing neural coding electrophysiology computational model

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The medial prefrontal cortex leaves the hippocampus when it prepares for the future

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SCIENCE PROGRESS 2024年第2期107卷 368504241261833页

作者： Sun, Yixiong Takehara-Nishiuchi, Kaori Univ Toronto Dept Cell & Syst Biol Toronto ON Canada Univ Toronto Dept Psychol Sidney Smith Hall Room 4007100 St George St Toronto ON M5S 3G3 Canada Univ Toronto Collaborat Program Neurosci Toronto ON Canada

Our memories help us plan for the future. In some cases, we use memories to repeat the choices that led to preferable outcomes in the past. The success of these memory-guided decisions depends on close interactions between the hippocampus and medial prefrontal cortex. In other cases, we need to use our memories to deduce hidden connections between the present and past situations to decide the best choice of action based on the expected outcome. Our recent study investigated neural underpinnings of such inferential decisions by monitoring neural activity in the medial prefrontal cortex and hippocampus in rats. We identified several neural activity patterns indicating awake memory trace reactivation and restructuring of functional connectivity among multiple neurons. We also found that these patterns occurred concurrently with the ongoing hippocampal activity when rats recalled past events but not when they planned new adaptive actions. Here, we discussed how these computational properties might contribute to success in inferential decision-making and propose a working model on how the medial prefrontal cortex changes its interaction with the hippocampus depending on whether it reflects on the past or looks into the future.

关键词： Inference decision-making prior knowledge memory replay neural coding mind wandering single neuron activity sharp wave ripples rats

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Point Process Adaptive Filters for neural Data Analysis: Theory and Applications

Point Process Adaptive Filters for Neural Data Analysis: The...

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Proceedings of the 46th IEEE Conference on Decision and Control, Pages 4135-4592

作者： Uri T. Eden The Department of Mathematics and Statistics Boston University Boston MA USA

ISBN: (纸本)9781424414970;1424414970

Although it is well known that neurons receive, process and transmit signals via sequences of sudden stereotyped electrical events, called action potentials or spikes, many analyses of neural data ignore the highly localized nature of these events. We discuss a point process modeling framework for neural systems to perform inference, assess goodness-of-fit, and estimate a state variable from spiking observations. Under this framework, we develop state space estimation and inference algorithms by constructing state models that describe the stochastic evolution of the signals to estimate, and conditional intensity models that define the probability distribution of observing a particular sequence of spike times for a neuron or ensemble. Posterior densities can then be computed using a recursive Bayesian framework combined with the Chapman-Kolmogorov system of equations for discrete-time analyses or the forward Kolmogorov equation for continuous-time analyses. This allows us to derive a toolbox of estimation algorithms and adaptive filters to address questions of static and dynamic encoding and decoding. We discuss the application of these modeling and estimation methods to the problem of predicting an intended reaching arm movement from simulated neurons in primate primary motor cortex. We show that a Bayesian approximate Gaussian filter is able to maintain accurate estimates of intended arm trajectories.

关键词： Adaptive Filters Bayesian Estimation neural coding Point Processes

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Metaheuristic Optimisation Algorithms for Tuning a Bioinspired Retinal Model

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SENSORS 2019年第22期19卷 4834-4834页

作者： Crespo-Cano, Ruben Cuenca-Asensi, Sergio Fernandez, Eduardo Martinez-Alvarez, Antonio Univ Alicante Dept Comp Technol Alicante 03690 Spain Univ Miguel Hernandez Inst Bioengn Elche 03202 Alicante Spain CIBER BBN Elche 03202 Alicante Spain

A significant challenge in neuroscience is understanding how visual information is encoded in the retina. Such knowledge is extremely important for the purpose of designing bioinspired sensors and artificial retinal systems that will, in so far as may be possible, be capable of mimicking vertebrate retinal behaviour. In this study, we report the tuning of a reliable computational bioinspired retinal model with various algorithms to improve the mimicry of the model. Its main contribution is two-fold. First, given the multi-objective nature of the problem, an automatic multi-objective optimisation strategy is proposed through the use of four biological-based metrics, which are used to adjust the retinal model for accurate prediction of retinal ganglion cell responses. Second, a subset of population-based search heuristics-genetic algorithms (SPEA2, NSGA-II and NSGA-III), particle swarm optimisation (PSO) and differential evolution (DE)-are explored to identify the best algorithm for fine-tuning the retinal model, by comparing performance across a hypervolume metric. Nonparametric statistical tests are used to perform a rigorous comparison between all the metaheuristics. The best results were achieved with the PSO algorithm on the basis of the largest hypervolume that was achieved, well-distributed elements and high numbers on the Pareto front.

关键词： neural prosthesis retinal modelling neural coding population-based metaheuristic evolutionary computation swarm intelligence multi-objective optimisation

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Hormonal coordination of motor output and internal prediction of sensory consequences in an electric fish

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CURRENT BIOLOGY 2023年第16期33卷 3350-+页

作者： Fukutomi, Matasaburo Carlson, Bruce A. Washington Univ St Louis Dept Biol St Louis MO 63130 USA

Steroid hormones remodel neural networks to induce seasonal or developmental changes in behavior. Hormonal changes in behavior likely require coordinated changes in sensorimotor integration. Here, we investigate hormonal effects on a predictive motor signal, termed corollary discharge, that modulates sensory processing in weakly electric mormyrid fish. In the electrosensory pathway mediating communication behavior, inhibition activated by a corollary discharge blocks sensory responses to self-generated electric pulses, allowing the downstream circuit to selectively analyze communication signals from nearby fish. These pulses are elongated by increasing testosterone levels in males during the breeding season. We induced electric-pulse elongation using testosterone treatment and found that the timing of electroreceptor responses to self-generated pulses was delayed as electric-pulse duration increased. Simultaneous recordings from an electrosensory nucleus and electromotor neurons revealed that the timing of corollary discharge inhibition was delayed and elongated by testosterone. Furthermore, this shift in the timing of corollary discharge inhibition was precisely matched to the shift in timing of receptor responses to self-generated pulses. We then asked whether the shift in inhibition timing was caused by direct action of testosterone on the corollary discharge circuit or by plasticity acting on the circuit in response to altered sensory feedback. We surgically silenced the electric organ of fish and found similar hormonal modulation of corollary discharge timing between intact and silent fish, suggesting that sensory feedback was not required for this shift. Our findings demonstrate that testosterone directly regulates motor output and internal prediction of the resulting sensory consequences in a coordinated manner.

关键词： sensorimotor integration corollary discharge electrosensory system neuroendocrinology neuroethology plasticity testosterone electric fish communication neural coding

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Time and experience differentially affect distinct aspects of hippocampal representational drift

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NEURON 2023年第15期111卷 2357-+页

作者： Geva, Nitzan Deitch, Daniel Rubin, Alon Ziv, Yaniv Weizmann Inst Sci Dept Brain Sci Rehovot Israel

Hippocampal activity is critical for spatial memory. Within a fixed, familiar environment, hippocampal codes gradually change over timescales of days to weeks-a phenomenon known as representational drift. The passage of time and the amount of experience are two factors that profoundly affect memory. However, thus far, it has remained unclear to what extent these factors drive hippocampal representational drift. Here, we longitudinally recorded large populations of hippocampal neurons in mice while they repeatedly explored two different familiar environments that they visited at different time intervals over weeks. We found that time and experience differentially affected distinct aspects of representational drift: the passage of time drove changes in neuronal activity rates, whereas experience drove changes in the cells' spatial tuning. Changes in spatial tuning were context specific and largely independent of changes in activity rates. Thus, our results suggest that representational drift is a multi-faceted process governed by distinct neuronal mechanisms.

关键词： hippocampus calcium imaging place cells representational drift miniature microscopes neural coding remapping

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Training and Spontaneous Reinforcement of Neuronal Assemblies by Spike Timing Plasticity

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CEREBRAL CORTEX 2019年第3期29卷 937-951页

作者： Ocker, Gabriel Koch Doiron, Brent Univ Pittsburgh Dept Neurosci Pittsburgh PA 15260 USA Univ Pittsburgh Ctr Neural Basis Cognit Pittsburgh PA 15260 USA Carnegie Mellon Univ Pittsburgh PA 15260 USA Allen Inst Brain Sci Seattle WA 98109 USA Univ Pittsburgh Dept Math Pittsburgh PA 15260 USA

The synaptic connectivity of cortex is plastic, with experience shaping the ongoing interactions between neurons. Theoretical studies of spike timing-dependent plasticity (STDP) have focused on either just pairs of neurons or large-scale simulations. A simple analytic account for how fast spike time correlations affect both microscopic and macroscopic network structure is lacking. We develop a low-dimensional mean field theory for STDP in recurrent networks and show the emergence of assemblies of strongly coupled neurons with shared stimulus preferences. After training, this connectivity is actively reinforced by spike train correlations during the spontaneous dynamics. Furthermore, the stimulus coding by cell assemblies is actively maintained by these internally generated spiking correlations, suggesting a new role for noise correlations in neural coding. Assembly formation has often been associated with firing rate-based plasticity schemes;our theory provides an alternative and complementary framework, where fine temporal correlations and STDP form and actively maintain learned structure in cortical networks.

关键词： cell assembly neural coding noise correlation spike timing synaptic plasticity

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Early Emergence of Solid Shape coding in Natural and Deep Network Vision

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CURRENT BIOLOGY 2021年第1期31卷 51-+页

作者： Srinath, Ramanujan Emonds, Alexandriya Wang, Qingyang Lempel, Augusto A. Dunn-Weiss, Erika Connor, Charles E. Nielsen, Kristina J. Johns Hopkins Univ Zanvyl Krieger Mind Brain Inst Baltimore MD 21218 USA Johns Hopkins Univ Solomon H Snyder Dept Neurosci Sch Med Baltimore MD 21205 USA Johns Hopkins Univ Dept Biomed Engn Sch Med Baltimore MD 21205 USA

Area V4 is the first object-specific processing stage in the ventral visual pathway, just as area MT is the first motion-specific processing stage in the dorsal pathway. For almost 50 years, coding of object shape in V4 has been studied and conceived in terms of flat pattern processing, given its early position in the transformation of 2D visual images. Here, however, in awake monkey recording experiments, we found that roughly half of V4 neurons are more tuned and responsive to solid, 3D shape-in-depth, as conveyed by shading, specularity, reflection, refraction, or disparity cues in images. Using 2-photon functional microscopy, we found that flat- and solid-preferring neurons were segregated into separate modules across the surface of area V4. These findings should impact early shape-processing theories and models, which have focused on 2D pattern processing. In fact, our analyses of early object processing in AlexNet, a standard visual deep network, revealed a similar distribution of sensitivities to flat and solid shape in layer 3. Early processing of solid shape, in parallel with flat shape, could represent a computational advantage discovered by both primate brain evolution and deep-network training.

关键词： vision primate cortex 3D V4 shape object ventral pathway neural coding deep network

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Grid-cell modules remain coordinated when neural activity is dissociated from external sensory cues

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NEURON 2022年第11期110卷 1843-+页

作者： Waaga, Torgeir Agmon, Haggai Normand, Valentin A. Nagelhus, Anne Gardner, Richard J. Moser, May-Britt Moser, Edvard, I Burak, Yoram Norwegian Univ Sci & Technol Kavli Inst Syst Neurosci Trondheim Norway Norwegian Univ Sci & Technol Ctr Neural Computat Trondheim Norway Hebrew Univ Jerusalem Edmond & Lily Safra Ctr Brain Sci Jerusalem Israel Hebrew Univ Jerusalem Racah Inst Phys Jerusalem Israel

The representation of an animal's position in the medial entorhinal cortex (MEC) is distributed across several modules of grid cells, each characterized by a distinct spatial scale. The population activity within each module is tightly coordinated and preserved across environments and behavioral states. Little is known, however, about the coordination of activity patterns across modules. We analyzed the joint activity patterns of hundreds of grid cells simultaneously recorded in animals that were foraging either in the light, when sensory cues could stabilize the representation, or in darkness, when such stabilization was disrupted. We found that the states of different modules are tightly coordinated, even in darkness, when the internal representation of position within the MEC deviates substantially from the true position of the animal. These findings suggest that internal brain mechanisms dynamically coordinate the representation of position in different modules, ensuring that they jointly encode a coherent and smooth trajectory.

关键词： neural coding neural decoding grid cells entorhinal cortex spatial memory spatial coding population coding neural circuits

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