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1. Diverse coactive neurons encode stimulus-driven and stimulus-independent variables

   https://doi.org/10.1152/jn.00431.2020  

   Xia, Ji; Marks, Tyler D.; Goard, Michael J.; Wessel, Ralf (November 2020, Journal of Neurophysiology)

   null (Ed.)

   Both experimenter-controlled stimuli and stimulus-independent variables impact cortical neural activity. A major hurdle to understanding neural representation is distinguishing between qualitatively different causes of the fluctuating population activity. We applied an unsupervised low-rank tensor decomposition analysis to the recorded population activity in the visual cortex of awake mice in response to repeated presentations of naturalistic visual stimuli. We found that neurons covaried largely independently of individual neuron stimulus response reliability and thus encoded both stimulus-driven and stimulus-independent variables. Importantly, a neuron’s response reliability and the neuronal coactivation patterns substantially reorganized for different external visual inputs. Analysis of recurrent balanced neural network models revealed that both the stimulus specificity and the mixed encoding of qualitatively different variables can arise from clustered external inputs. These results establish that coactive neurons with diverse response reliability mediate a mixed representation of stimulus-driven and stimulus-independent variables in the visual cortex. NEW & NOTEWORTHY V1 neurons covary largely independently of individual neuron’s response reliability. A single neuron’s response reliability imposes only a weak constraint on its encoding capabilities. Visual stimulus instructs a neuron’s reliability and coactivation pattern. Network models revealed using clustered external inputs. 

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2. Contrast polarity-specific mapping improves efficiency of neuronal computation for collision detection

   https://doi.org/10.7554/eLife.79772  

   Dewell, Richard Burkett; Zhu, Ying; Eisenbrandt, Margaret; Morse, Richard; Gabbiani, Fabrizio (October 2022, eLife)

   Neurons receive information through their synaptic inputs, but the functional significance of how those inputs are mapped on to a cell’s dendrites remains unclear. We studied this question in a grasshopper visual neuron that tracks approaching objects and triggers escape behavior before an impending collision. In response to black approaching objects, the neuron receives OFF excitatory inputs that form a retinotopic map of the visual field onto compartmentalized, distal dendrites. Subsequent processing of these OFF inputs by active membrane conductances allows the neuron to discriminate the spatial coherence of such stimuli. In contrast, we show that ON excitatory synaptic inputs activated by white approaching objects map in a random manner onto a more proximal dendritic field of the same neuron. The lack of retinotopic synaptic arrangement results in the neuron’s inability to discriminate the coherence of white approaching stimuli. Yet, the neuron retains the ability to discriminate stimulus coherence for checkered stimuli of mixed ON/OFF polarity. The coarser mapping and processing of ON stimuli thus has a minimal impact, while reducing the total energetic cost of the circuit. Further, we show that these differences in ON/OFF neuronal processing are behaviorally relevant, being tightly correlated with the animal’s escape behavior to light and dark stimuli of variable coherence. Our results show that the synaptic mapping of excitatory inputs affects the fine stimulus discrimination ability of single neurons and document the resulting functional impact on behavior. 

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3. Chaotic dynamics in spatially distributed neuronal networks generate population-wide shared variability

   https://doi.org/10.1371/journal.pcbi.1010843  

   Mosheiff, Noga; Ermentrout, Bard; Huang, Chengcheng (January 2023, PLOS Computational Biology)

   Berry, Hugues (Ed.)

   Neural activity in the cortex is highly variable in response to repeated stimuli. Population recordings across the cortex demonstrate that the variability of neuronal responses is shared among large groups of neurons and concentrates in a low dimensional space. However, the source of the population-wide shared variability is unknown. In this work, we analyzed the dynamical regimes of spatially distributed networks of excitatory and inhibitory neurons. We found chaotic spatiotemporal dynamics in networks with similar excitatory and inhibitory projection widths, an anatomical feature of the cortex. The chaotic solutions contain broadband frequency power in rate variability and have distance-dependent and low-dimensional correlations, in agreement with experimental findings. In addition, rate chaos can be induced by globally correlated noisy inputs. These results suggest that spatiotemporal chaos in cortical networks can explain the shared variability observed in neuronal population responses. 

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4. Dynamic Modeling of Spike Count Data With Conway-Maxwell Poisson Variability

   https://doi.org/10.1162/neco_a_01593  

   Wei, Ganchao; Stevenson, Ian H. (June 2023, Neural Computation)

   Abstract In many areas of the brain, neural spiking activity covaries with features of the external world, such as sensory stimuli or an animal's movement. Experimental findings suggest that the variability of neural activity changes over time and may provide information about the external world beyond the information provided by the average neural activity. To flexibly track time-varying neural response properties, we developed a dynamic model with Conway-Maxwell Poisson (CMP) observations. The CMP distribution can flexibly describe firing patterns that are both under- and overdispersed relative to the Poisson distribution. Here we track parameters of the CMP distribution as they vary over time. Using simulations, we show that a normal approximation can accurately track dynamics in state vectors for both the centering and shape parameters (λ and ν). We then fit our model to neural data from neurons in primary visual cortex, “place cells” in the hippocampus, and a speed-tuned neuron in the anterior pretectal nucleus. We find that this method outperforms previous dynamic models based on the Poisson distribution. The dynamic CMP model provides a flexible framework for tracking time-varying non-Poisson count data and may also have applications beyond neuroscience. 

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5. Encoding manifolds constructed from grating responses organize responses to natural scenes in cortical visual areas

   https://doi.org/10.1101/2024.10.24.620089  

   Dyballa, Luciano; Field, Greg D; Stryker, Michael P; Zucker, Steven W (October 2024, bioRxiv.org)

   We have created encoding manifolds to reveal the overall responses of a brain area to a variety of stimuli. Encoding manifolds organize response properties globally: each point on an encoding manifold is a neuron, and nearby neurons respond similarly to the stimulus ensemble in time. We previously found, using a large stimulus ensemble including optic flows, that encoding manifolds for the retina were highly clustered, with each cluster corresponding to a different ganglion cell type. In contrast, the topology of the V1 manifold was continuous. Now, using responses of individual neurons from the Allen Institute Visual Coding-Neuropixels dataset in the mouse, we infer encoding manifolds for V1 and for five higher cortical visual areas (VISam, VISal, VISpm, VISlm, and VISrl). We show here that the encoding manifold topology computed only from responses to various grating stimuli is also continuous, not only for V1 but also for the higher visual areas, with smooth coordinates spanning it that include, among others, orientation selectivity and firing-rate magnitude. Surprisingly, the encoding manifold for gratings also provides information about natural scene responses. To investigate whether neurons respond more strongly to gratings or natural scenes, we plot the log ratio of natural scene responses to grating responses (mean firing rates) on the encoding manifold. This reveals a global coordinate axis organizing neurons' preferences between these two stimuli. This coordinate is orthogonal (i.e., uncorrelated) to that organizing firing rate magnitudes in VISp. Analyzing layer responses, a preference for gratings is concentrated in layer 6, whereas preference for natural scenes tends to be higher in layers 2/3 and 4. We also find that preference for natural scenes dominates the responses of neurons that prefer low (0.02 cpd) and high (0.32 cpd) spatial frequencies, rather than intermediate ones (0.04 to 0.16 cpd). Conclusion: while gratings seem limited and natural scenes unconstrained, machine learning algorithms can reveal subtle relationships between them beyond linear techniques. 

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