@article{292, abstract = {Retina is a paradigmatic system for studying sensory encoding: the transformation of light into spiking activity of ganglion cells. The inverse problem, where stimulus is reconstructed from spikes, has received less attention, especially for complex stimuli that should be reconstructed “pixel-by-pixel”. We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small randomly-moving discs. We constructed nonlinear (kernelized and neural network) decoders that improved significantly over linear results. An important contribution to this was the ability of nonlinear decoders to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous-like activity. This improvement crucially depended on the precise, non-Poisson temporal structure of individual spike trains, which originated in the spike-history dependence of neural responses. We propose a general principle by which downstream circuitry could discriminate between spontaneous and stimulus-driven activity based solely on higher-order statistical structure in the incoming spike trains.}, author = {Botella Soler, Vicent and Deny, Stephane and Martius, Georg S and Marre, Olivier and Tkacik, Gasper}, journal = {PLoS Computational Biology}, number = {5}, publisher = {Public Library of Science}, title = {{Nonlinear decoding of a complex movie from the mammalian retina}}, doi = {10.1371/journal.pcbi.1006057}, volume = {14}, year = {2018}, } @article{543, abstract = {A central goal in theoretical neuroscience is to predict the response properties of sensory neurons from first principles. To this end, “efficient coding” posits that sensory neurons encode maximal information about their inputs given internal constraints. There exist, however, many variants of efficient coding (e.g., redundancy reduction, different formulations of predictive coding, robust coding, sparse coding, etc.), differing in their regimes of applicability, in the relevance of signals to be encoded, and in the choice of constraints. It is unclear how these types of efficient coding relate or what is expected when different coding objectives are combined. Here we present a unified framework that encompasses previously proposed efficient coding models and extends to unique regimes. We show that optimizing neural responses to encode predictive information can lead them to either correlate or decorrelate their inputs, depending on the stimulus statistics; in contrast, at low noise, efficiently encoding the past always predicts decorrelation. Later, we investigate coding of naturalistic movies and show that qualitatively different types of visual motion tuning and levels of response sparsity are predicted, depending on whether the objective is to recover the past or predict the future. Our approach promises a way to explain the observed diversity of sensory neural responses, as due to multiple functional goals and constraints fulfilled by different cell types and/or circuits.}, author = {Chalk, Matthew J and Marre, Olivier and Tkacik, Gasper}, journal = {PNAS}, number = {1}, pages = {186 -- 191}, publisher = {National Academy of Sciences}, title = {{Toward a unified theory of efficient, predictive, and sparse coding}}, doi = {10.1073/pnas.1711114115}, volume = {115}, year = {2018}, } @misc{5584, abstract = {This package contains data for the publication "Nonlinear decoding of a complex movie from the mammalian retina" by Deny S. et al, PLOS Comput Biol (2018). The data consists of (i) 91 spike sorted, isolated rat retinal ganglion cells that pass stability and quality criteria, recorded on the multi-electrode array, in response to the presentation of the complex movie with many randomly moving dark discs. The responses are represented as 648000 x 91 binary matrix, where the first index indicates the timebin of duration 12.5 ms, and the second index the neural identity. The matrix entry is 0/1 if the neuron didn't/did spike in the particular time bin. (ii) README file and a graphical illustration of the structure of the experiment, specifying how the 648000 timebins are split into epochs where 1, 2, 4, or 10 discs were displayed, and which stimulus segments are exact repeats or unique ball trajectories. (iii) a 648000 x 400 matrix of luminance traces for each of the 20 x 20 positions ("sites") in the movie frame, with time that is locked to the recorded raster. The luminance traces are produced as described in the manuscript by filtering the raw disc movie with a small gaussian spatial kernel. }, author = {Deny, Stephane and Marre, Olivier and Botella-Soler, Vicente and Martius, Georg S and Tkacik, Gasper}, keywords = {retina, decoding, regression, neural networks, complex stimulus}, publisher = {Institute of Science and Technology Austria}, title = {{Nonlinear decoding of a complex movie from the mammalian retina}}, doi = {10.15479/AT:ISTA:98}, year = {2018}, } @article{1104, abstract = {In the early visual system, cells of the same type perform the same computation in different places of the visual field. How these cells code together a complex visual scene is unclear. A common assumption is that cells of a single-type extract a single-stimulus feature to form a feature map, but this has rarely been observed directly. Using large-scale recordings in the rat retina, we show that a homogeneous population of fast OFF ganglion cells simultaneously encodes two radically different features of a visual scene. Cells close to a moving object code quasilinearly for its position, while distant cells remain largely invariant to the object's position and, instead, respond nonlinearly to changes in the object's speed. We develop a quantitative model that accounts for this effect and identify a disinhibitory circuit that mediates it. Ganglion cells of a single type thus do not code for one, but two features simultaneously. This richer, flexible neural map might also be present in other sensory systems.}, author = {Deny, Stephane and Ferrari, Ulisse and Mace, Emilie and Yger, Pierre and Caplette, Romain and Picaud, Serge and Tkacik, Gasper and Marre, Olivier}, issn = {20411723}, journal = {Nature Communications}, number = {1}, publisher = {Nature Publishing Group}, title = {{Multiplexed computations in retinal ganglion cells of a single type}}, doi = {10.1038/s41467-017-02159-y}, volume = {8}, year = {2017}, } @article{720, abstract = {Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system’s state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality.}, author = {Humplik, Jan and Tkacik, Gasper}, issn = {1553734X}, journal = {PLoS Computational Biology}, number = {9}, publisher = {Public Library of Science}, title = {{Probabilistic models for neural populations that naturally capture global coupling and criticality}}, doi = {10.1371/journal.pcbi.1005763}, volume = {13}, year = {2017}, } @article{1197, abstract = {Across the nervous system, certain population spiking patterns are observed far more frequently than others. A hypothesis about this structure is that these collective activity patterns function as population codewords–collective modes–carrying information distinct from that of any single cell. We investigate this phenomenon in recordings of ∼150 retinal ganglion cells, the retina’s output. We develop a novel statistical model that decomposes the population response into modes; it predicts the distribution of spiking activity in the ganglion cell population with high accuracy. We found that the modes represent localized features of the visual stimulus that are distinct from the features represented by single neurons. Modes form clusters of activity states that are readily discriminated from one another. When we repeated the same visual stimulus, we found that the same mode was robustly elicited. These results suggest that retinal ganglion cells’ collective signaling is endowed with a form of error-correcting code–a principle that may hold in brain areas beyond retina.}, author = {Prentice, Jason and Marre, Olivier and Ioffe, Mark and Loback, Adrianna and Tkacik, Gasper and Berry, Michael}, journal = {PLoS Computational Biology}, number = {11}, publisher = {Public Library of Science}, title = {{Error-robust modes of the retinal population code}}, doi = {10.1371/journal.pcbi.1005148}, volume = {12}, year = {2016}, } @article{1248, abstract = {Life depends as much on the flow of information as on the flow of energy. Here we review the many efforts to make this intuition precise. Starting with the building blocks of information theory, we explore examples where it has been possible to measure, directly, the flow of information in biological networks, or more generally where information-theoretic ideas have been used to guide the analysis of experiments. Systems of interest range from single molecules (the sequence diversity in families of proteins) to groups of organisms (the distribution of velocities in flocks of birds), and all scales in between. Many of these analyses are motivated by the idea that biological systems may have evolved to optimize the gathering and representation of information, and we review the experimental evidence for this optimization, again across a wide range of scales.}, author = {Tkacik, Gasper and Bialek, William}, journal = {Annual Review of Condensed Matter Physics}, pages = {89 -- 117}, publisher = {Annual Reviews}, title = {{Information processing in living systems}}, doi = {10.1146/annurev-conmatphys-031214-014803}, volume = {7}, year = {2016}, } @article{1697, abstract = {Motion tracking is a challenge the visual system has to solve by reading out the retinal population. It is still unclear how the information from different neurons can be combined together to estimate the position of an object. Here we recorded a large population of ganglion cells in a dense patch of salamander and guinea pig retinas while displaying a bar moving diffusively. We show that the bar’s position can be reconstructed from retinal activity with a precision in the hyperacuity regime using a linear decoder acting on 100+ cells. We then took advantage of this unprecedented precision to explore the spatial structure of the retina’s population code. The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar’s position. Instead, we found that most ganglion cells in the salamander fired sparsely and idiosyncratically, so that their neural image did not track the bar. Furthermore, ganglion cell activity spanned an area much larger than predicted by their receptive fields, with cells coding for motion far in their surround. As a result, population redundancy was high, and we could find multiple, disjoint subsets of neurons that encoded the trajectory with high precision. This organization allows for diverse collections of ganglion cells to represent high-accuracy motion information in a form easily read out by downstream neural circuits.}, author = {Marre, Olivier and Botella Soler, Vicente and Simmons, Kristina and Mora, Thierry and Tkacik, Gasper and Berry, Michael}, journal = {PLoS Computational Biology}, number = {7}, publisher = {Public Library of Science}, title = {{High accuracy decoding of dynamical motion from a large retinal population}}, doi = {10.1371/journal.pcbi.1004304}, volume = {11}, year = {2015}, } @article{1701, abstract = {The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but, with more spikes, the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. We suggest further tests of criticality, and give a brief discussion of its functional significance. }, author = {Tkacik, Gasper and Mora, Thierry and Marre, Olivier and Amodei, Dario and Palmer, Stephanie and Berry Ii, Michael and Bialek, William}, journal = {PNAS}, number = {37}, pages = {11508 -- 11513}, publisher = {National Academy of Sciences}, title = {{Thermodynamics and signatures of criticality in a network of neurons}}, doi = {10.1073/pnas.1514188112}, volume = {112}, year = {2015}, } @article{1886, abstract = {Information processing in the sensory periphery is shaped by natural stimulus statistics. In the periphery, a transmission bottleneck constrains performance; thus efficient coding implies that natural signal components with a predictably wider range should be compressed. In a different regime—when sampling limitations constrain performance—efficient coding implies that more resources should be allocated to informative features that are more variable. We propose that this regime is relevant for sensory cortex when it extracts complex features from limited numbers of sensory samples. To test this prediction, we use central visual processing as a model: we show that visual sensitivity for local multi-point spatial correlations, described by dozens of independently-measured parameters, can be quantitatively predicted from the structure of natural images. This suggests that efficient coding applies centrally, where it extends to higher-order sensory features and operates in a regime in which sensitivity increases with feature variability.}, author = {Hermundstad, Ann and Briguglio, John and Conte, Mary and Victor, Jonathan and Balasubramanian, Vijay and Tkacik, Gasper}, journal = {eLife}, number = {November}, publisher = {eLife Sciences Publications}, title = {{Variance predicts salience in central sensory processing}}, doi = {10.7554/eLife.03722}, year = {2014}, }