Predicting Cortical Dark/Bright Asymmetries from Natural Image Statistics and Early Visual Transforms

The nervous system has evolved in an environment with structure and predictability. One of the ubiquitous principles of sensory systems is the creation of circuits that capitalize on this predictability. Previous work has identified predictable non-uniformities in the distributions of basic visual features in natural images that are relevant to the encoding tasks of the visual system. Here, we report that the well-established statistical distributions of visual features -- such as visual contrast, spatial scale, and depth -- differ between bright and dark image components. Following this analysis, we go on to trace how these differences in natural images translate into different patterns of cortical input that arise from the separate bright (ON) and dark (OFF) pathways originating in the retina. We use models of these early visual pathways to transform natural images into statistical patterns of cortical input. The models include the receptive fields and non-linear response properties of the magnocellular (M) and parvocellular (P) pathways, with their ON and OFF pathway divisions. The results indicate that there are regularities in visual cortical input beyond those that have previously been appreciated from the direct analysis of natural images. In particular, several dark/bright asymmetries provide a potential account for recently discovered asymmetries in how the brain processes visual features, such as violations of classic energy-type models. On the basis of our analysis, we expect that the dark/bright dichotomy in natural images plays a key role in the generation of both cortical and perceptual asymmetries.     

Temporal Statistics of Natural Image Sequences Generated by Movements with Insect Flight Characteristics

Many flying insects, such as flies, wasps and bees, pursue a saccadic flight and gaze strategy. This behavioral strategy is thought to separate the translational and rotational components of self-motion and, thereby, to reduce the computational efforts to extract information about the environment from the retinal image flow. Because of the distinguishing dynamic features of this active flight and gaze strategy of insects, the present study analyzes systematically the spatiotemporal statistics of image sequences generated during saccades and intersaccadic intervals in cluttered natural environments. We show that, in general, rotational movements with saccade-like dynamics elicit fluctuations and overall changes in brightness, contrast and spatial frequency of up to two orders of magnitude larger than translational movements at velocities that are characteristic of insects. Distinct changes in image parameters during translations are only caused by nearby objects. Image analysis based on larger patches in the visual field reveals smaller fluctuations in brightness and spatial frequency composition compared to small patches. The temporal structure and extent of these changes in image parameters define the temporal constraints imposed on signal processing performed by the insect visual system under behavioral conditions in natural environments.     

Stimulus-dependent Maximum Entropy Models of Neural Population Codes

Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. For large populations, direct sampling of these distributions is impossible, and so we must rely on constructing appropriate models. We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. We introduce the stimulus-dependent maximum entropy (SDME) model—a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population.     

The Natural Statistics of Audiovisual Speech

Humans, like other animals, are exposed to a continuous stream of signals, which are dynamic, multimodal, extended, and time varying in nature. This complex input space must be transduced and sampled by our sensory systems and transmitted to the brain where it can guide the selection of appropriate actions. To simplify this process, it''s been suggested that the brain exploits statistical regularities in the stimulus space. Tests of this idea have largely been confined to unimodal signals and natural scenes. One important class of multisensory signals for which a quantitative input space characterization is unavailable is human speech. We do not understand what signals our brain has to actively piece together from an audiovisual speech stream to arrive at a percept versus what is already embedded in the signal structure of the stream itself. In essence, we do not have a clear understanding of the natural statistics of audiovisual speech. In the present study, we identified the following major statistical features of audiovisual speech. First, we observed robust correlations and close temporal correspondence between the area of the mouth opening and the acoustic envelope. Second, we found the strongest correlation between the area of the mouth opening and vocal tract resonances. Third, we observed that both area of the mouth opening and the voice envelope are temporally modulated in the 2–7 Hz frequency range. Finally, we show that the timing of mouth movements relative to the onset of the voice is consistently between 100 and 300 ms. We interpret these data in the context of recent neural theories of speech which suggest that speech communication is a reciprocally coupled, multisensory event, whereby the outputs of the signaler are matched to the neural processes of the receiver.     

Learning to Estimate Dynamical State with Probabilistic Population Codes

Tracking moving objects, including one’s own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, “probabilistic population codes.” We show that a recurrent neural network—a modified form of an exponential family harmonium (EFH)—that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.     

A Refined Neuronal Population Measure of Visual Attention

Neurophysiological studies of cognitive mechanisms such as visual attention typically ignore trial-by-trial variability and instead report mean differences averaged across many trials. Advances in electrophysiology allow for the simultaneous recording of small populations of neurons, which may obviate the need for averaging activity over trials. We recently introduced a method called the attention axis that uses multi-electrode recordings to provide estimates of attentional state of behaving monkeys on individual trials. Here, we refine this method to eliminate problems that can cause bias in estimates of attentional state in certain scenarios. We demonstrate the sources of these problems using simulations and propose an amendment to the previous formulation that provides superior performance in trial-by-trial assessments of attentional state.     

Neuronal Adaptation Translates Stimulus Gaps into a Population Code

Neurons in sensory pathways exhibit a vast multitude of adaptation behaviors, which are assumed to aid the encoding of temporal stimulus features and provide the basis for a population code in higher brain areas. Here we study the transition to a population code for auditory gap stimuli both in neurophysiological recordings and in a computational network model. Independent component analysis (ICA) of experimental data from the inferior colliculus of Mongolian gerbils reveals that the network encodes different gap sizes primarily with its population firing rate within 30 ms after the presentation of the gap, where longer gap size evokes higher network activity. We then developed a computational model to investigate possible mechanisms of how to generate the population code for gaps. Phenomenological (ICA) and functional (discrimination performance) analyses of our simulated networks show that the experimentally observed patterns may result from heterogeneous adaptation, where adaptation provides gap detection at the single neuron level and neuronal heterogeneity ensures discriminable population codes for the whole range of gap sizes in the input. Furthermore, our work suggests that network recurrence additionally enhances the network''s ability to provide discriminable population patterns.     

Genetic Variation and Discriminating Power of Four DNA Microsatellites in the Russian Population

The allele and genotype frequency distributions of four STRs (the LPL, vWA, FES/FPS, and F13B loci) commonly used in forensic medicine were studied with a sample of 200 ethnic Russians from Siberia. Genetic and molecular diversity of the four STRs was characterized in comparison with the American Caucasoid population. The set of the four STRs showed a high power of discrimination (PD = 0.99975). Comparison of the genetic variation at the four loci revealed a considerable difference between the Russian and American Caucasoid populations, precluding the use of data on allele frequencies in American Caucasoids for forensic testing in Russia. The results can be used as a reference in Siberia.     

The Influence of Synaptic Weight Distribution on Neuronal Population Dynamics

The manner in which different distributions of synaptic weights onto cortical neurons shape their spiking activity remains open. To characterize a homogeneous neuronal population, we use the master equation for generalized leaky integrate-and-fire neurons with shot-noise synapses. We develop fast semi-analytic numerical methods to solve this equation for either current or conductance synapses, with and without synaptic depression. We show that its solutions match simulations of equivalent neuronal networks better than those of the Fokker-Planck equation and we compute bounds on the network response to non-instantaneous synapses. We apply these methods to study different synaptic weight distributions in feed-forward networks. We characterize the synaptic amplitude distributions using a set of measures, called tail weight numbers, designed to quantify the preponderance of very strong synapses. Even if synaptic amplitude distributions are equated for both the total current and average synaptic weight, distributions with sparse but strong synapses produce higher responses for small inputs, leading to a larger operating range. Furthermore, despite their small number, such synapses enable the network to respond faster and with more stability in the face of external fluctuations.     

Discriminating self from nonself with short peptides from large proteomes

We studied whether the peptides of nine amino acids (9-mers) that are typically used in MHC class I presentation are sufficiently unique for self:nonself discrimination. The human proteome contains 28,783 proteins, comprising 10⁷ distinct 9-mers. Enumerating distinct 9-mers for a variety of microorganisms we found that the average overlap, i.e., the probability that a foreign peptide also occurs in the human self, is about 0.2%. This self:nonself overlap increased when shorter peptides were used, e.g., was 30% for 6-mers and 3% for 7-mers. Predicting all 9-mers that are expected to be cleaved by the immunoproteasome and to be translocated by TAP, we find that about 25% of the self and the nonself 9-mers are processed successfully. For the HLA-A*0201 and HLA-A*0204 alleles, we predicted which of the processed 9-mers from each proteome are expected to be presented on the MHC. Both alleles prefer to present processed 9-mers to nonprocessed 9-mers, and both have small preference to present foreign peptides. Because a number of amino acids from each 9-mer bind the MHC, and are therefore not exposed to the TCR, antigen presentation seems to involve a significant loss of information. Our results show that this is not the case because the HLA molecules are fairly specific. Removing the two anchor residues from each presented peptide, we find that the self:nonself overlap of these exposed 7-mers resembles that of 9-mers. Summarizing, the 9-mers used in MHC class I presentation tend to carry sufficient information to detect nonself peptides amongst self peptides.     

Transfer of Transposon Tn916 from Bacillus subtilis into a Natural Soil Population

A Bacillus subtilis transconjugant with a Tn916 chromosomal insert was obtained through mating with Escherichia coli carrying the transposon as a plasmid insert. Actinomycetes were identified as frequent transposon recipients following the introduction of the B. subtilis transconjugant into a soil microcosm.     

Cytogenetic Analysis of an SD Chromosome from a Natural Population of DROSOPHILA MELANOGASTER

The discovery and the cytogenetic characterization of a new SD (Segregation Distorter) chromosome 2 from a natural population in Ranna (Sicily, Italy), SD(Ra), are reported. The main features of this chromosome are as follows: (a) it contains an Sd(Ra) gene with a moderate degree of segregation distortion (k = 0.72), (b) a recessive female sterile gene, fs(2)(TLM), responsible for modifications of the morphology and structure of the tests and ovaries is located at 89.7, (c) SD(Ra)/SD(Ra) males and females are viable but sterile, the females due to homozygosis of fs(2)(TLM) and the males because of homozygosis of a region containing the Sd locus, and (d) SDi/SDj combinations are fertile, thus suggesting that the different Sd factors found in natural populations constitute a multiple allelic series.-These data may indicate that each population containing SD chromosomes has evolved its own genetic architecture for the complex SD system, with specific modifiers and perhaps different Sd genes. The possibility of reconstructing the evolutionary pattern of the SD(Ra) chromosome in the natural Ranna population after the model of Charlesworth and Hartl (1978) and Crow (1979) is considered.     

Discriminating causes from consequences of persistent parrotfish corallivory

A detailed understanding of the dual role of parrotfish as both key herbivores and potentially important corallivores is essential to the study of coral health and reef trophodynamics. Some Caribbean parrotfish regularly consume live coral, and discriminate both among coral species and among colonies within a particular species. While they prefer Montastraea spp. corals, which are dominant Caribbean reef builders, causes of selective and persistent grazing of certain colonies remain unknown. We manipulated coral exposure to parrotfish grazing through a long-term cage exclusion experiment in Belize, comparing initially grazed vs. intact (non-grazed) Montastraea spp. colonies. We measured nutrition-related characteristics (C:N ratio, %C, and %N) as well as defensive characteristics (nematocyst density and skeletal hardness) to determine if any of these variables accurately predicted parrotfish grazing. There were substantial reductions in coral nutritional quality (C:N) associated with parrotfish grazing, although these changes appear to be a consequence rather than a cause of parrotfish selectivity. Likewise, nematocyst densities were suppressed in grazed corals, also likely a result of chronic grazing stress. We found no intraspecific differences in skeletal hardness related to grazing. These results provide further demonstration of the physiological consequences of grazing, but the cause of preferential grazing by parrotfishes on certain Montastraea spp. colonies still requires further investigation.     

Transposable Element Numbers in Cosmopolitan Inversions from a Natural Population of Drosophila Melanogaster

Population studies of the distribution of transposable elements (TEs) on the chromosomes of Drosophila melanogaster have suggested that their copy number increase due to transposition is balanced by some form of natural selection. Theory suggests that, as a consequence of deleterious ectopic meiotic exchange between TEs, selection can favor genomes with lower TE copy numbers. This predicts that TEs should be less deleterious, and hence more abundant, in chromosomal regions in which recombination is reduced. To test this, we surveyed the abundance and locations of 10 families of TEs in recombination-suppressing chromosomal inversions from a natural population. The sample of 49 chromosomes included multiple independent isolates of seven different inversions and a corresponding set of standard chromosomes. For all 10 TE families pooled, copy numbers were significantly higher overall within low frequency inversions than within corresponding regions of standard chromosomes. TEs occupied chromosomal sites at significantly higher frequencies within the In(3R)M0 and In(3R)K inversions than within the corresponding regions of standard 3R chromosomes. These results are consistent with the predictions of the ectopic exchange model.     

The Limited Utility of Multiunit Data in Differentiating Neuronal Population Activity

To date, single neuron recordings remain the gold standard for monitoring the activity of neuronal populations. Since obtaining single neuron recordings is not always possible, high frequency or ‘multiunit activity’ (MUA) is often used as a surrogate. Although MUA recordings allow one to monitor the activity of a large number of neurons, they do not allow identification of specific neuronal subtypes, the knowledge of which is often critical for understanding electrophysiological processes. Here, we explored whether prior knowledge of the single unit waveform of specific neuron types is sufficient to permit the use of MUA to monitor and distinguish differential activity of individual neuron types. We used an experimental and modeling approach to determine if components of the MUA can monitor medium spiny neurons (MSNs) and fast-spiking interneurons (FSIs) in the mouse dorsal striatum. We demonstrate that when well-isolated spikes are recorded, the MUA at frequencies greater than 100Hz is correlated with single unit spiking, highly dependent on the waveform of each neuron type, and accurately reflects the timing and spectral signature of each neuron. However, in the absence of well-isolated spikes (the norm in most MUA recordings), the MUA did not typically contain sufficient information to permit accurate prediction of the respective population activity of MSNs and FSIs. Thus, even under ideal conditions for the MUA to reliably predict the moment-to-moment activity of specific local neuronal ensembles, knowledge of the spike waveform of the underlying neuronal populations is necessary, but not sufficient.     

The Effect of Correlated Neuronal Firing and Neuronal Heterogeneity on Population Coding Accuracy in Guinea Pig Inferior Colliculus

It has been suggested that the considerable noise in single-cell responses to a stimulus can be overcome by pooling information from a large population. Theoretical studies indicated that correlations in trial-to-trial fluctuations in the responses of different neurons may limit the improvement due to pooling. Subsequent theoretical studies have suggested that inherent neuronal diversity, i.e., the heterogeneity of tuning curves and other response properties of neurons preferentially tuned to the same stimulus, can provide a means to overcome this limit. Here we study the effect of spike-count correlations and the inherent neuronal heterogeneity on the ability to extract information from large neural populations. We use electrophysiological data from the guinea pig Inferior-Colliculus to capture inherent neuronal heterogeneity and single cell statistics, and introduce response correlations artificially. To this end, we generate pseudo-population responses, based on single-cell recording of neurons responding to auditory stimuli with varying binaural correlations. Typically, when pseudo-populations are generated from single cell data, the responses within the population are statistically independent. As a result, the information content of the population will increase indefinitely with its size. In contrast, here we apply a simple algorithm that enables us to generate pseudo-population responses with variable spike-count correlations. This enables us to study the effect of neuronal correlations on the accuracy of conventional rate codes. We show that in a homogenous population, in the presence of even low-level correlations, information content is bounded. In contrast, utilizing a simple linear readout, that takes into account the natural heterogeneity, even of neurons preferentially tuned to the same stimulus, within the neural population, one can overcome the correlated noise and obtain a readout whose accuracy grows linearly with the size of the population.     

Multiplicative Auditory Spatial Receptive Fields Created by a Hierarchy of Population Codes

A multiplicative combination of tuning to interaural time difference (ITD) and interaural level difference (ILD) contributes to the generation of spatially selective auditory neurons in the owl''s midbrain. Previous analyses of multiplicative responses in the owl have not taken into consideration the frequency-dependence of ITD and ILD cues that occur under natural listening conditions. Here, we present a model for the responses of ITD- and ILD-sensitive neurons in the barn owl''s inferior colliculus which satisfies constraints raised by experimental data on frequency convergence, multiplicative interaction of ITD and ILD, and response properties of afferent neurons. We propose that multiplication between ITD- and ILD-dependent signals occurs only within frequency channels and that frequency integration occurs using a linear-threshold mechanism. The model reproduces the experimentally observed nonlinear responses to ITD and ILD in the inferior colliculus, with greater accuracy than previous models. We show that linear-threshold frequency integration allows the system to represent multiple sound sources with natural sound localization cues, whereas multiplicative frequency integration does not. Nonlinear responses in the owl''s inferior colliculus can thus be generated using a combination of cellular and network mechanisms, showing that multiple elements of previous theories can be combined in a single system.     

Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics

Spatial context in images induces perceptual phenomena associated with salience and modulates the responses of neurons in primary visual cortex (V1). However, the computational and ecological principles underlying contextual effects are incompletely understood. We introduce a model of natural images that includes grouping and segmentation of neighboring features based on their joint statistics, and we interpret the firing rates of V1 neurons as performing optimal recognition in this model. We show that this leads to a substantial generalization of divisive normalization, a computation that is ubiquitous in many neural areas and systems. A main novelty in our model is that the influence of the context on a target stimulus is determined by their degree of statistical dependence. We optimized the parameters of the model on natural image patches, and then simulated neural and perceptual responses on stimuli used in classical experiments. The model reproduces some rich and complex response patterns observed in V1, such as the contrast dependence, orientation tuning and spatial asymmetry of surround suppression, while also allowing for surround facilitation under conditions of weak stimulation. It also mimics the perceptual salience produced by simple displays, and leads to readily testable predictions. Our results provide a principled account of orientation-based contextual modulation in early vision and its sensitivity to the homogeneity and spatial arrangement of inputs, and lends statistical support to the theory that V1 computes visual salience.