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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2016

Training qualitatively shifts the neural mechanisms that support attentional selection

Attention supports the selection of relevant sensory information from competing irrelevant sensory information. This selective processing is thought to be supported via the attentional gain amplification of sensory responses evoked by attended compared to unattended stimuli. However, recent studies in highly trained subjects suggest that attentional gain plays a relatively modest role and that other types of neural modulations - such as a reduction in neural noise - better explain attention-related changes in behavior. We hypothesized that the amount of training may alter neural mechanisms that support attentional selection in visual cortex. To test this hypothesis, we investigated the influence of training on attentional modulations of stimulus-evoked visual responses by recording electroencephalography (EEG) from humans performing a selective visuospatial attention task over the course of one month. Early in training, visuospatial attention induced a robust attentional gain amplification of sensory-evoked responses in contralateral visual cortex that emerged within ~100ms after stimulus onset, and a quantitative model based on signal detection theory (SDT) successfully linked this attentional gain amplification to attention-related improvements in behavior. However, after training, this attentional gain amplification of visual responses was almost completely eliminated and modeling suggested that noise reduction was required to link the amplitude of visual responses with attentional modulations of behavior. These findings suggest that the neural mechanisms supporting selective attention can change as a function of training and expertise, and help to bridge different results from studies carried out in different model systems that require substantially different amount of training.

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bioRxiv2016

Stimulus visibility controls the balance between attention-induced changes in contrast appearance and decision bias

While attention is known to improve information processing, whether attention can alter visual appearance has been a cornerstone of debate for 100+ years. Although recent studies suggest that attention can alter appearance, it has been argued that the reported appearance changes reflect response bias. Here, we provide a resolution to this debate by showing that attention has different effects on appearance and response bias depending on stimulus visibility. In a contrast judgment task where the contrast of attended and unattended stimuli varied across a full range of contrast values, human participants exhibited a substantial amount of response bias to the attended stimulus, when stimuli were hard to see. However, when stimuli were easier to see, response bias decreased and attention primarily increased perceived contrast. These results help constrain philosophical arguments about the cognitive penetrability of perception and reconcile the long-standing debate about the attention effect on appearance and response bias.

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bioRxiv2016

The interaction of orientation-specific surround suppression and visual-spatial attention

Orientation selective surround suppression (OSSS) is a reduction in the perceived contrast of a stimulus, which occurs when a collinear grating is placed adjacent to the stimulus. Attention affects performance on many visual tasks, and we asked whether the perceptual effects of OSSS are mitigated through the allocation of voluntary visual-spatial attention. Participants were tested in a contrast discrimination task: at the beginning of each trial, one location on the screen was cued and a subsequent contrast judgment was then more likely (70%) to be performed in that location. Replicating previous results, we found that the point of subjective equality (PSE) was elevated for a collinear, relative to an orthogonal, surround. While the PSE was similar for validly and invalidly cued trials, the just noticeable difference (JND) was larger for invalid cue trials, and for collinear, relative to orthogonal surround, suggesting that while OSSS affects both perceived contrast and sensitivity, voluntary attention affects only perceptual sensitivity. In another experiment no informative cue was provided, and attention was distributed over the entire display. In this case, JND and PSE were shifted depending on the contrast of the distractor, suggesting that OSSS is affected by the allocation of visual-spatial attention, but only under conditions of distributed attention.

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bioRxiv2016

Heterogeneous firing responses predict diverse couplings to presynaptic activity in mice layer V pyramidal neurons

In this study, we present a theoretical framework combining experimental characterizations and analytical calculus to capture the firing rate input-output properties of single neurons in the fluctuation-driven regime. Our framework consists of a two-step procedure to treat independently how the dendritic input translates into somatic fluctuation variables, and how the latter determine action potential firing. We use this framework to investigate the functional impact of the heterogeneity in firing responses found experimentally in young mice layer V pyramidal cells. We first design and calibrate in vitro a simplified morphological model of layer V pyramidal neurons with a dendritic tree following Rall's branching rule. Then, we propose an analytical derivation for the membrane potential fluctuations at the soma as a function of the properties of the synaptic input in dendrites. This mathematical description allows us to easily emulate various forms of synaptic input: either balanced, unbalanced, synchronized, purely proximal or purely distal synaptic activity. We find that those different forms of input activity lead to various impact on the membrane potential fluctuations properties, thus raising the possibility that individual neurons will differentially couple to specific forms of activity as a result of their different firing response. We indeed found such a heterogeneous coupling between synaptic input and firing response for all types of presynaptic activity. This heterogeneity can be explained by different levels of cellular excitability in the case of the balanced, unbalanced, synchronized and purely distal activity. A notable exception appears for proximal dendritic inputs: increasing the input level can either promote firing response in some cells, or suppress it in some other cells whatever their individual excitability. This behavior can be explained by different sensitivities to the speed of the fluctuations, which was previously associated to different levels of sodium channel inactivation and density. Because local network connectivity rather targets proximal dendrites, our results suggest that this aspect of biophysical heterogeneity might be relevant to neocortical processing by controlling how individual neurons couple to local network activity.

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bioRxiv2016

Spatiotemporal characterization of the neural correlates of outcome valence and surprise during reward learning in humans

Reward learning depends on accurate reward associations with potential choices. Two separate outcome dimensions, namely the valence (positive or negative) and surprise (the absolute degree of deviation from expectations) of an outcome are thought to subserve adaptive decision-making and learning, however their neural correlates and relative contribution to learning remain debated. Here, we coupled single-trial analyses of electroencephalography with simultaneously acquired fMRI, while participants performed a probabilistic reversal-learning task, to offer evidence of temporally overlapping but largely distinct spatial representations of outcome valence and surprise in the human brain. Electrophysiological variability in outcome valence correlated with activity in regions of the human reward network promoting approach or avoidance learning. Variability in outcome surprise correlated primarily with activity in regions of the human attentional network controlling the speed of learning. Crucially, despite the largely separate spatial extend of these representations we also found a linear superposition of the two outcome dimensions in a smaller network encompassing visuo-mnemonic and reward areas. This spatiotemporal overlap was uniquely exposed by our EEG-informed fMRI approach. Activity in this network was further predictive of stimulus value updating indicating a comparable contribution of both signals to reward learning.

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bioRxiv2016

Expanding the language network: Domain-specific hippocampal recruitment during high-level linguistic processing

Language processing requires us to encode linear relations between acoustic forms and map them onto hierarchical relations between meaning units. Such relational binding of linguistic elements might recruit the hippocampus given its engagement by similar operations in other cognitive domains. Historically, hippocampal engagement in online language use has received little attention because patients with hippocampal damage are not aphasic. However, recent studies have found that these patients exhibit language impairments when the demands on flexible relational binding are high, suggesting that the hippocampus does, in fact, contribute to linguistic processing. A fundamental question is thus whether language processing engages domain-general hippocampal mechanisms that are also recruited across other cognitive processes or whether, instead, it relies on certain language-selective areas within the hippocampus. To address this question, we conducted the first systematic analysis of hippocampal engagement during comprehension in healthy adults (n=150 across three experiments) using fMRI. Specifically, we functionally localized putative \"language-regions\" within the hippocampus using a language comprehension task, and found that these regions (i) were selectively engaged by language but not by six non-linguistic tasks; and (ii) were coupled in their activity with the cortical language network during both \"rest\" and especially story comprehension, but not with the domain-general \"multiple-demand (MD)\" network. This functional profile did not generalize to other hippocampal regions that were localized using a non-linguistic, working memory task. These findings suggest that some hippocampal mechanisms that maintain and integrate information during language comprehension are not domain-general but rather belong to the language-specific brain network.\n\nSignificance statementAccording to popular views, language processing is exclusively supported by neocortical mechanisms. However, recent patient studies suggest that language processing may also require the hippocampus, especially when relations among linguistic elements have to be flexibly integrated and maintained. Here, we address a core question about the place of the hippocampus in the cognitive architecture of language: are certain hippocampal operations language-specific rather than domain-general? By extensively characterizing hippocampal recruitment during language comprehension in healthy adults using fMRI, we show that certain hippocampal subregions exhibit signatures of language specificity in both their response profiles and their patterns of activity synchronization with known functional regions in the neocortex. We thus suggest that the hippocampus is a satellite constituent of the language network.

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bioRxiv2016

C. elegans detect the color of pigmented food sources to guide foraging decisions.

Here we establish that, contrary to expectations, Caenorhabditis elegans nematode worms possess a color discrimination system despite lacking any opsin or other known visible light photoreceptor genes. We found that white light guides C. elegans foraging decisions away from harmful bacteria that secrete a blue pigment toxin. Absorption of amber light by this blue pigment toxin alters the color of light sensed by the worm, and thereby triggers an increase in avoidance. By combining narrow-band blue and amber light sources, we demonstrated that detection of the specific blue:amber ratio by the worm guides its foraging decision. These behavioral and psychophysical studies thus establish the existence of a color detection system that is distinct from those of other animals.

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bioRxiv2016

Influence Of Desire To Belong And Feelings Of Loneliness On Emotional Prosody Perception In Schizophrenia.

Objective: Humans are social creatures, with desires to connect or belong, producing loneliness when isolated. Individuals with schizophrenia are often more isolated than healthy adults and demonstrate profound social communication impairments such as vocal affect perception (prosody). Loneliness, levels of desire for social connectedness (need to belong, NTB), and their relationship to perception of social communications have not been investigated in schizophrenia.\n\nMethod: In a sample of 69 individuals (36 SZ), we measured endorsements of loneliness and NTB, and evaluated their putative relationships to clinical symptoms and social communication abilities, as indexed by emotional prosody and pitch perception.\n\nResults: Loneliness endorsement was highly variable but particularly so in patients, whilst patients endorsed NTB at levels equivalent to healthy controls. In schizophrenia, pitch and prosody acuity were reduced, and prosody perception correlated with NTB. Loneliness, but not desire for social connectedness, correlated with negative symptoms.\n\nConclusion: Loneliness and negative symptoms likely exert bidirectional effects on each other. Loneliness and desire to form interpersonal attachments may be pivotal in shaping and stimulating social interactions and, subsequently, the ability to perceive social intent through prosody. Intact NTB levels in patients augurs well for cognitive remediation which, target vocal-communication processing to improve social skills.\n\nSignificant OutcomesO_LIPatients with schizophrenia endorsed higher levels of loneliness than controls, but ratings of desire for social connectedness were at normal levels.\nC_LIO_LIPitch acuity and prosody perception were correlated, confirming the importance of basic sensory processing in recognizing prosodic emotions.\nC_LIO_LISocio-cognitive perceptual ability (emotional prosody perception) correlated with increased desire for social connections, implying that they may still be motivated to find social interactions reinforcing. Thus interventions to improve perceptual deficits could still be an effective means of improving social function.\nC_LI\n\nLimitationsO_LICausal relationships between desire for social connections, loneliness, and emotional prosody perception cannot be inferred through correlations and cross-sectional studies alone.\nC_LIO_LISubjective endorsements of loneliness through self-report are not the same thing as objective indices of loneliness. New and more extensive tools for measuring desire for both loneliness and social connectedness may be needed.\nC_LIO_LIDirect experimental comparison of the interrelations between desire for social connectedness, loneliness, pitch acuity and emotional prosody perception in patients with schizophrenia and other populations such as autism will enable a more accurate comparison of the likely success of remediating socio-cognitive perceptual impairment in neuropsychiatric disorders.\nC_LI

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bioRxiv2016

What rhythmic perception and amusia can tell us about vocal social communication in schizophrenia

BackgroundPerceiving social intent throughvocal intonation is impaired in schizophrenia; thisdysprosodia partly arisingfrom impaired pitch perception.Individuals with amusia (tone-deafness) are insensitive to pitch change andalso demonstrate prosody deficits. Sensitivity to rhythm is reduced in amusia when tonal sequences contain pitch changes (polytonic), but is normal for monotonic sequences, suggesting perceptual impairment originates at a secondary processing stage where pitch- and time-relatedcues are yoked. Here, we sought to ascertain: 1) whether schizophreniapatients demonstrate rhythmic deficits, 2) whether suchdeficits are restricted to polytonic sequences, and 3) how pitch and rhythm perception relate to prosodic processing.\n\nMethodsSeventy-sixparticipants (33 schizophrenia) completed tasks assessing pitch and prosody perception, as well as monotonic and polytonic rhythmic perception.\n\nResultsIncreasing tone-deafness correlated with pitch-dependent rhythm detection impairments. Pitch and prosody correlated across all participants. Schizophreniapatients displayed basic time and pitch deficits. Correlations and path analyses indicated prosodic processing is an associatedfunction of pitch and pitch-dependent rhythm perception,with pure temporal processing playing an indirect role.In schizophrenia, deficits in monotonic and polytonic rhythmic perception did not contribute to prosodic processing dysfunction, and montonic rhythmic dysfunction and pitch perception did not covary.\n\nConclusionsExploring similarities between amusia and schizophrenia focused our characterization of prosodic processing as the function of sub-processes reflecting pitch and time perception,whichare prerequisite for prosodic processing. The uniqueness of dysprosodia in schizophrenia relative to other illnesses may be measured by idiosyncrasy in the pattern and magnitude of the sub-process task relationships.

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bioRxiv2016

Neural Homophily: Similar Neural Responses Predict Friendship

We resemble our friends on a wide range of dimensions (e.g., age, gender), but do similarities between friends reflect deeper similarities in how we perceive, interpret, and respond to the world? To find out, we characterized the social network of a cohort of 279 students, a subset of whom participated in a functional magnetic resonance imaging (fMRI) study involving free-viewing of video stimuli. We compared fMRI response time series between corresponding brain regions across pairs of individuals and found that neural response similarity decreased with increasing distance in the social network. These effects persisted after controlling for demographic similarity. Further, it was possible to accurately classify the distance between individuals in their social network based on the similarity of their fMRI response time series across brain regions. These results suggest that we are exceptionally similar to our friends in how we perceive and react to the world around us.

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bioRxiv2016

Local Discriminant Hyperalignment for multi-subject fMRI data alignment

Multivariate Pattern (MVP) classification can map different cognitive states to the brain tasks. One of the main challenges in MVP analysis is validating the generated results across subjects. However, analyzing multi-subject fMRI data requires accurate functional alignments between neuronal activities of different subjects, which can rapidly increase the performance and robustness of the final results. Hyperalignment (HA) is one of the most effective functional alignment methods, which can be mathematically formulated by the Canonical Correlation Analysis (CCA) methods. Since HA mostly uses the unsupervised CCA techniques, its solution may not be optimized for MVP analysis. By incorporating the idea of Local Discriminant Analysis (LDA) into CCA, this paper proposes Local Discriminant Hyperalignment (LDHA) as a novel supervised HA method, which can provide better functional alignment for MVP analysis. Indeed, the locality is defined based on the stimuli categories in the train-set, where the correlation between all stimuli in the same category will be maximized and the correlation between distinct categories of stimuli approaches to near zero. Experimental studies on multi-subject MVP analysis confirm that the LDHA method achieves superior performance to other state-of-the-art HA algorithms.

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bioRxiv2016

NeuroNLP: a natural language portal for aggregated fruit fly brain data

NeuroNLP, is a key application on the Fruit Fly Brain Observatory platform (FFBO, http://fruitflybrain.org), that provides a modern web-based portal for navigating fruit fly brain circuit data. Increases in the availability and scale of fruit fly connectome data, demand new, scalable and accessible methods to facilitate investigation into the functions of the latest complex circuits being uncovered. NeuroNLP enables in-depth exploration and investigation of the structure of brain circuits, using intuitive natural language queries that are capable of revealing the latent structure and information, obscured due to expansive yet independent data sources. NeuroNLP is built on top of a database system call NeuroArch that codifies knowledge about the fruit fly brain circuits, spanning multiple sources. Users can probe biological circuits in the NeuroArch database with plain English queries, such as \"show glutamatergic local neurons in the left antennal lobe\" and \"show neurons with dendrites in the left mushroom body and axons in the fan-shaped body\". This simple yet powerful interface replaces the usual, cumbersome checkboxes and dropdown menus prevalent in todays neurobiological databases. Equipped with powerful 3D visualization, NeuroNLP standardizes tools and methods for graphical rendering, representation, and manipulation of brain circuits, while integrating with existing databases such as the FlyCircuit. The userfriendly graphical user interface complements the natural language queries with additional controls for exploring the connectivity of neurons and neural circuits. Designed with an open-source, modular structure, it is highly scalable/flexible/extensible to additional databases or to switch between databases and supports the creation of additional parsers for other languages. By supporting access through a web browser from any modern laptop or smartphone, NeuroNLP significantly increases the accessibility of fruit fly brain data and improves the impact of the data in both scientific and educational exploration.

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bioRxiv2016

NeuroGFX: a graphical functional explorer for fruit fly brain circuits

Recently, multiple focused efforts have resulted in substantial increase in the availability of connectome data in the fruit fly brain. Elucidating neural circuit function from such structural data calls for a scalable computational modeling methodology. We propose such a methodology that includes i) a brain emulation engine, with an architecture that can tackle the complexity of whole brain modeling, ii) a database that supports tight integration of biological and modeling data along with support for domain specific queries and circuit transformations, and iii) a graphical interface that allows for total flexibility in configuring neural circuits and visualizing run-time results, both anchored on model abstractions closely reflecting biological structure. Towards the realization of such a methodology, we have developed NeuroGFX and integrated it into the architecture of the Fruit Fly Brain Observatory (http://fruitflybrain.org). The computational infrastructure in NeuroGFX is provided by Neurokernel, an open source platform for the emulation of the fruit fly brain, and NeuroArch, a database for querying and executing fruit fly brain circuits. The integration of the two enables the algorithmic construction/manipulation/revision of executable circuits on multiple levels of abstraction of the same model organism. The power of this computational infrastructure can be leveraged through an intuitive graphical interface that allows visualizing execution results in the context of biological structure. This provides an environment where computational researchers can present configurable, executable neural circuits, and experimental scientists can easily explore circuit structure and function ultimately leading to biological validation. With these capabilities, NeuroGFX enables the exploration of function from circuit structure at whole brain, neuropil, and local circuit level of abstraction. By allowing for independently developed models to be integrated at the architectural level, NeuroGFX provides an open plug and play, collaborative environment for whole brain computational modeling of the fruit fly.

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bioRxiv2016

Evidence that the ventral stream codes the errors used in hierarchical inference and learning

Ventral visual stream neural responses are dynamic, even for static image presentations. However, dynamical neural models of visual cortex are lacking as most progress has been made modeling static, time-averaged responses. Here, we studied population neural dynamics during face detection across three cortical processing stages. Remarkably, ~30 milliseconds after the initially evoked response, we found that neurons in intermediate level areas decreased their preference for faces, becoming anti-face preferring on average even while neurons in higher level areas achieved and maintained a face preference. This pattern of hierarchical neural dynamics was inconsistent with extensions of standard feedforward circuits that implemented recurrence within a cortical stage. Rather, recurrent models computing errors between stages captured the observed temporal signatures. Without additional parameter fitting, this model of neural dynamics, which simply augments the standard feedforward model of online vision to encode errors, also explained seemingly disparate dynamical phenomena in the ventral stream.

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bioRxiv2016

In vivo magnetic recording of neuronal activity

Neuronal activity generates ionic flows and thereby both magnetic fields and electric potential differences, i.e. voltages. Voltage measurements are widely used, but suffer from isolating and smearing properties of tissue between source and sensor, are blind to ionic flow direction, and reflect the difference between two electrodes, complicating interpretation. Magnetic field measurements could overcome these limitations, but have been essentially limited to magnetoencephalography (MEG), using centimeter-sized, helium-cooled extracranial sensors. Here, we report on in vivo magnetic recordings of neuronal activity from visual cortex of cats with magnetrodes, specially developed needle-shaped probes carrying micron-sized, non-cooled magnetic sensors based on spin electronics. Event-related magnetic fields inside the neuropil were on the order of several nanoteslas, informing MEG source models and efforts for magnetic field measurements through MRI. Though the signal-to-noise ratio is still inferior to electrophysiology, this proof of concept demonstrates the potential to exploit the fundamental advantages of magnetophysiology.\n\nHIGHLIGHTSO_LISpin-electronics based probes achieve local magnetic recordings inside the neuropil\nC_LIO_LIMagnetic field recordings were performed in vivo, in anesthetized cat visual cortex\nC_LIO_LIEvent-related fields (ERFs) to visual stimuli were up to several nanoteslas in size\nC_LIO_LIERFs could be detected after averaging less than 20 trials\nC_LI\n\nIN BRIEFCaruso et al. report in vivo, intra-cortical recordings of magnetic fields that reflect neuronal activity, using magnetrodes, i.e. micron size magnetic sensors based on spin electronics.

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bioRxiv2016

Early survival and delayed death of developmentally-born dentate gyrus neurons

The storage and persistence of memories depends on plasticity in the hippocampus. Adult neurogenesis produces new neurons that mature through critical periods for plasticity and cellular survival, which determine their contributions to learning and memory. However, most granule neurons are generated prior to adulthood; the maturational timecourse of these neurons is poorly understood compared to adult-born neurons, but is essential to identify how the dentate gyrus, as a whole, contributes to behavior. To characterize neurons born in the early postnatal period, we labeled dentate gyrus neurons born on postnatal day 6 (P6) with BrdU and quantified maturation and survival across early (1 hour to 8 weeks old) and late (2-6 months old) cell ages. We find that the dynamics of developmentally-born neuron survival is essentially the opposite of neurons born in adulthood: P6-born neurons did not go through a period of cell death during their immature stages (from 1-8 weeks). In contrast, 17% of P6-born neurons died after reaching maturity, between 2-6 months of age. Delayed death was evident from the loss of BrdU+ cells as well as pyknotic BrdU+caspase3+ neurons within the superficial granule cell layer. Patterns of DCX, NeuN and activity-dependent Fos expression indicate that developmentally-born neurons mature over several weeks and a sharp peak in zif268 expression at 2 weeks suggests that developmentally-born neurons mature faster than adult-born neurons (which peak at 3 weeks). Collectively, our findings are relevant for understanding how developmentally-born dentate gyrus neurons contribute to memory and disorders throughout the lifespan. High levels of early survival and zif268 expression may promote learning, while also rendering neurons sensitive to insults at defined stages. Late neuronal death in young adulthood may result in the loss of hundreds of thousands of dentate gyrus neurons, which could impact memory persistence and contribute to hippocampal/dentate gyrus atrophy in disorders such as depression.

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bioRxiv2016

Random versus maximum entropy models of neural population activity

The principle of maximum entropy provides a useful method for inferring statistical mechanics models from observations in correlated systems, and is widely used in a variety of fields where accurate data are available. While the assumptions underlying maximum entropy are intuitive and appealing, its adequacy for describing complex empirical data has been little studied in comparison to alternative approaches. Here data from the collective spiking activity of retinal neurons is reanalysed. The accuracy of the maximum entropy distribution constrained by mean firing rates and pairwise correlations is compared to a random ensemble of distributions constrained by the same observables. In general, maximum entropy approximates the true distribution better than the typical or mean distribution from that ensemble. This advantage improves with population size, with groups as small as 8 being almost always better described by maximum entropy. Failure of maximum entropy to outperform random models is found to be associated with strong correlations in the population.

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bioRxiv2016

Neurons in the mouse deep superior colliculus encode orientation/direction through suppression and extract selective visual features

The superior colliculus (SC) is an integrative sensorimotor structure that contributes to multiple visiondependent behaviors. It is a laminated structure; the superficial SC layers (sSC) contain cells that respond to visual stimuli, while the deep SC layers (dSC) contain cells that also respond to auditory and somatosensory stimuli. Despite the increasing interest in mice for visual system study, the differences in the visual response properties between the sSC and the dSC are largely unknown. Here we used a large-scale silicon probe recording system to examine the visual response properties of neurons within the SC of head-fixed, awake and behaving mice. We find that both the sSC and dSC cells respond to visual stimuli, but dSC cells have three key differences. (1) The majority of the dSC orientation/direction selective (OS/DS) cells have their firing rate suppressed by drifting sinusoidal gratings (negative OS/DS cells) rather than being stimulated like the sSC cells (positive OS/DS cells). (2) Almost all the dSC cells have complex-cell-like spatial summation nonlinearity, and a significantly smaller fraction of the positive OS/DS cells in the dSC respond to flashing spots than those in the sSC. (3) The dSC cells lack Y-like spatial summation nonlinearity unlike the sSC cells. These results provide the first description of cells that are suppressed by a visual stimulus with a specific orientation or direction, show that neurons in the dSC have properties analogous to cortical complex cells, and show the presence of Y-like nonlinearity in the sSC but their absence in the dSC.\n\nSignificance statementThe superior colliculus receives visual input from the retina in its superficial layers (sSC) and induces eye/head orientating movements and innate defensive responses in its deeper layers (dSC). Despite their importance, very little is known about the visual response properties of dSC neurons. Using highdensity electrode recordings and novel model-based analysis, we find that the dSC contains cells with a novel property; they are suppressed by the orientation or direction of specific stimuli. We also show that dSC cells have properties similar to cortical \"complex\" cells. Conversely, cells with Y-like nonlinear spatial summation properties are located only in the sSC. These findings contribute to our understanding of how the SC processes visual inputs, a critical step in comprehending visually-guided behaviors.\n\nAcknowledgementsThis work was supported by the Brain Research Seed Funding provided by UCSC and from the National Institutes of Health Grant NEI R21EYO26758 to D. A. F. and A. M. L. We thank Michael Stryker for training on the electrophysiology experiments and his very helpful comments on the manuscript, Sotiris Masmanidis for providing us with the silicon probes, Forest Martinez-McKinney and Serguei Kachiguin for their technical contributions to the silicon probe system, Jeremiah Tsyporin for taking an image of neural tissues and the training of mice, Jena Yamada, Anahit Hovhannisyan, Corinne Beier, and Sydney Weiser, for their helpful comments on the manuscript.

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