Retinal and cortical nonlinearities combine to produce masking in V1 responses to plaids

The visual response of a cell in the primary visual cortex (V1) to a drifting grating stimulus at the cell’s preferred orientation decreases when a second, perpendicular, grating is superimposed. This effect is called masking. To understand the nonlinear masking effect, we model the response of Macaque V1 simple cells in layer 4Cα to input from magnocellular Lateral Geniculate Nucleus (LGN) cells. The cortical model network is a coarse-grained reduction of an integrate-and-fire network with excitation from LGN input and inhibition from other cortical neurons. The input is modeled as a sum of LGN cell responses. Each LGN cell is modeled as the convolution of a spatio-temporal filter with the visual stimulus, normalized by a retinal contrast gain control, and followed by rectification representing the LGN spike threshold. In our model, the experimentally observed masking arises at the level of LGN input to the cortex. The cortical network effectively induces a dynamic threshold that forces the test grating to have high contrast before it can overcome the masking provided by the perpendicular grating. The subcortical nonlinearities and the cortical network together account for the masking effect. Melinda Koelling is formerly from Center for Neural Science and Courant Institute, New York University.     

Directional tunings independent of orientation in the primary visual cortex of the cat

A family of moving ‘random-line’ patterns was developed and used to study the directional tuning of 91 single units in cat primary visual cortex (V1). The results suggest that, in addition to the well-known orientation-dependent mechanism, there is also some kind of orientationindependent mechanism underlying the direction selectivity. The directional tuning of the neurons varies in accordance with the increase of orientation or non-orientation element in the stimulus.     

Corticofugal feedback can reduce the visual latency of responses to antagonistic stimuli

 A biophysically realistical model of the primary visual pathway is designed, including feedback connections from the visual cortex to the lateral geniculate nucleus (LGN) – the so-called corticofugal pathway. The model comprises up to 10 000 retina and LGN cells divided into the ON and the OFF pathway according to their contrast response characteristics. An additional 6000 cortical simple cells are modeled. Apart from the direct excitatory afferent pathway we include strong mutual inhibition between the ON and the OFF subsystems. In addition, we propose a novel type of paradoxical corticofugal connection pattern which links ON dominated cortical simple cells to OFF-center LGN cells and vice versa. In accordance with physiological findings these connections are weakly excitatory and do not interfere with the steady-state responses to constant illumination, because during the steady-state inhibition arising from the active pathway effectively silences the nonstimulated pathway. At the moment of a contrast reversal the effect of the paradoxical connection pattern comes into play and the depolarization of the previously silent channel is accelerated, leading to a latency reduction of up to 4 ms using moderate synaptic weights. With increased weights reductions of more than 10 ms can be achieved. We introduce different synaptic characteristics for the feedback (AMPA, NMDA, AMPA+NMDA) and show that the strongest latency reduction is obtained for a combination of the membrane channels (i.e., AMPA+NMDA). The effect of the proposed paradoxical connection pattern is self-regulating; because the levels of inhibition and paradoxical excitation are always driven by the same inputs (strong inhibition is counterbalanced by a stronger paradoxical excitation and vice versa). In addition, the latency reduction for a contrast inversion which ends at a small absolute contrast level (small contrast step) is stronger than the reduction for an inversion with large final contrast (large contrast step). This leads to a more pronounced reduction in the reaction times for weak stimuli. Thus, reaction time differences for different contrast steps are smoothed out. Received: 22 January 1996/Accepted in revised form: 20 May 1996     

Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs reduce orientation selectivity

One of the reasons the visual cortex has attracted the interest of computational neuroscience is that it has well-defined inputs. The lateral geniculate nucleus (LGN) of the thalamus is the source of visual signals to the primary visual cortex (V1). Most large-scale cortical network models approximate the spike trains of LGN neurons as simple Poisson point processes. However, many studies have shown that neurons in the early visual pathway are capable of spiking with high temporal precision and their discharges are not Poisson-like. To gain an understanding of how response variability in the LGN influences the behavior of V1, we study response properties of model V1 neurons that receive purely feedforward inputs from LGN cells modeled either as noisy leaky integrate-and-fire (NLIF) neurons or as inhomogeneous Poisson processes. We first demonstrate that the NLIF model is capable of reproducing many experimentally observed statistical properties of LGN neurons. Then we show that a V1 model in which the LGN input to a V1 neuron is modeled as a group of NLIF neurons produces higher orientation selectivity than the one with Poisson LGN input. The second result implies that statistical characteristics of LGN spike trains are important for V1’s function. We conclude that physiologically motivated models of V1 need to include more realistic LGN spike trains that are less noisy than inhomogeneous Poisson processes.     

Determination of the precision of spike timing in the visual cortex of anaesthetised cats

 Neuronal cortical spike trains contain precisely replicating patterns whose presence cannot be accounted for by chance production. A comparison of the number of triplets of spikes present two times with the number of doublets replicated three times in the same window duration gives a frequency-insensitive measure of this type of fine temporal organisation. By varying the tolerance with which such precisely replicating patterns are detected, one can evaluate the accuracy of spike timing in spike trains. In the sample of data here analysed, it was found that replicating patterns were best seen in the precision range 0.4–1.4 ms (a result evidently at variance with a simple ‘integrate and fire’ model of neurons). Surprisingly, the fine temporal structure of spike trains thus evidenced was stronger at relatively low firing rate discharges and was present in both the ‘spontaneous’ and ‘evoked’ responses. Received: 3 April 1995/Accepted in revised form: 11 July 1995     

Combined Hebbian development of geniculocortical and lateral connectivity in a model of primary visual cortex

We present a network model of visual map development in layer 4 of primary visual cortex. Our model comprises excitatory and inhibitory spiking neurons. The input to the network consists of correlated spike trains to mimick the activity of neurons in the lateral geniculate nucleus (LGN). An activity-driven Hebbian learning mechanism governs the development of both the network's lateral connectivity and feedforward projections from LGN to cortex. Plasticity of inhibitory synapses has been included into the model so as to control overall cortical activity. Even without feedforward input, Hebbian modification of the excitatory lateral connections can lead to the development of an intracortical orientation map. We have found that such an intracortical map can guide the development of feedforward connections from LGN to cortical simple cells so that the structure of the final feedforward orientation map is predetermined by the intracortical map. In a scenario in which left- and right-eye geniculocortical inputs develop sequentially one after the other, the resulting maps are therefore very similar, provided the intracortical connectivity remains unaltered. This may explain the outcome of so-called reverse lid-suture experiments, where animals are reared so that both eyes never receive input at the same time, but the orientation maps measured separately for the two eyes are nevertheless nearly identical. Received: 20 December 1999 / Accepted in revised form: 9 June 2000     

Saccades differentially modulate human LGN and V1 responses in the presence and absence of visual stimulation

Saccades occur several times each second in normal human vision. The visual image moves across the retina at high velocity during a saccade, yet no blurring of the visual scene is perceived . Active suppression of visual input may account for this perceptual continuity, but the neural mechanisms underlying such saccadic suppression remain unclear. We used functional MRI to specifically examine responses in the lateral geniculate nucleus (LGN) and primary visual cortex (V1) during saccades. Activity in both V1 and LGN was strongly modulated by saccades. Furthermore, this modulation depended on whether visual stimulation was present or absent. In complete darkness, saccades led to reliable signal increases in V1 and LGN, whereas in the presence of visual stimulation, saccades led to suppression of visually evoked responses. These findings represent unequivocal evidence for saccadic suppression in human LGN and retinotopically defined V1 and are consistent with the earliest site of saccadic suppression lying at or before V1.     

Computational Modeling of Orientation Tuning Dynamics in Monkey Primary Visual Cortex

In the primate visual pathway, orientation tuning of neurons is first observed in the primary visual cortex. The LGN cells that comprise the thalamic input to V1 are not orientation tuned, but some V1 neurons are quite selective. Two main classes of theoretical models have been offered to explain orientation selectivity: feedforward models, in which inputs from spatially aligned LGN cells are summed together by one cortical neuron; and feedback models, in which an initial weak orientation bias due to convergent LGN input is sharpened and amplified by intracortical feedback. Recent data on the dynamics of orientation tuning, obtained by a cross-correlation technique, may help to distinguish between these classes of models. To test this possibility, we simulated the measurement of orientation tuning dynamics on various receptive field models, including a simple Hubel-Wiesel type feedforward model: a linear spatiotemporal filter followed by an integrate-and-fire spike generator. The computational study reveals that simple feedforward models may account for some aspects of the experimental data but fail to explain many salient features of orientation tuning dynamics in V1 cells. A simple feedback model of interacting cells is also considered. This model is successful in explaining the appearance of Mexican-hat orientation profiles, but other features of the data continue to be unexplained.     

A biologically motivated and analytically soluble model of collective oscillations in the cortex

. Feature linking and pattern separation are shown to be performed as simultaneous processes by a highly connected auto-associative network of spiking neurons (spike response model). In principle, many (e.g., with nine) patterns can be separated, but with a biological set of parameters the number is limited to four. The patterns have been learned by an asymmetric hebbian rule that can handle a low activity which may vary from pattern to pattern (in a range between 4% and 7%). Spikes are generated by a threshold process and – with some delay – transmitted to postsynaptic neurons. There they evoke an excitatory or inhibitory postsynaptic potential (EPSP or IPSP). Spike emission is followed by an absolute refractory period (1 ms) and activates an inhibitory delay loop that prevents continuous firing. Three different network topologies are discussed, i.e., a structureless fully connected system, a network composed of two ‘hemispheres’, and finally a hierarchical network with four subsystems that represent different ‘functions’ and interact via feedforward and feedback connections. Functional feedback turns out to be essential for context-sensitive binding. The coherence between the two hemispheres is dependent on the interhemispheric delays. If these are on average too large, the two hemispheres oscillate coherently by themselves but phase-shifted by half a period with respect to each other. Received: 16 June 1993/Accepted in revised form: 24 March 1994     

Low response variability in simultaneously recorded retinal, thalamic, and cortical neurons

The response of a cortical cell to a repeated stimulus can be highly variable from one trial to the next. Much lower variability has been reported of retinal cells. We recorded visual responses simultaneously from three successive stages of the cat visual system: retinal ganglion cells (RGCs), thalamic (LGN) relay cells, and simple cells in layer 4 of primary visual cortex. Spike count variability was lower than that of a Poisson process at all three stages but increased at each stage. Absolute and relative refractory periods largely accounted for the reliability at all three stages. Our results show that cortical responses can be more reliable than previously thought. The differences in reliability in retina, LGN, and cortex can be explained by (1) decreasing firing rates and (2) decreasing absolute and relative refractory periods.     

Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus

Biphasic neural response properties, where the optimal stimulus for driving a neural response changes from one stimulus pattern to the opposite stimulus pattern over short periods of time, have been described in several visual areas, including lateral geniculate nucleus (LGN), primary visual cortex (V1), and middle temporal area (MT). We describe a hierarchical model of predictive coding and simulations that capture these temporal variations in neuronal response properties. We focus on the LGN-V1 circuit and find that after training on natural images the model exhibits the brain's LGN-V1 connectivity structure, in which the structure of V1 receptive fields is linked to the spatial alignment and properties of center-surround cells in the LGN. In addition, the spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN. Moreover, the model displays a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses while neurons of opposite polarity increase their responses with feedback. This phase-reversed pattern of influence was recently observed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain.     

Brocchi,Darwin, and Transmutation: Phylogenetics and Paleontology at the Dawn of Evolutionary Biology

Giambattista Brocchi’s (1814) monograph (see Dominici, Evo Edu Outreach, this issue, 2010) on the Tertiary fossils of the Subappenines in Italy—and their relation to the living molluscan fauna—contains a theoretical, transmutational perspective (“Brocchian transmutation”). Unlike Lamarck (1809), Brocchi saw species as discrete and fundamentally stable entities. Explicitly analogizing the births and deaths of species with those of individual organisms (“Brocchi’s analogy”), Brocchi proposed that species have inherent longevities, eventually dying of old age unless driven to extinction by external forces. As for individuals, births and deaths of species are understood to have natural causes; sequences of births and deaths of species produce genealogical lineages of descent, and faunas become increasingly modernized through time. Brocchi calculated that over 50% of his fossil species are still alive in the modern fauna. Brocchi’s work was reviewed by Horner (1816) in Edinburgh. Brocchi’s influence as a transmutational thinker is clear in Jameson’s (1827) “geological illustrations” in his fifth edition of his translation of Cuvier’s Theory of the Earth (read by his student Charles Darwin) and in the anonymous essays of 1826 and 1827 published in the Edinburgh New Philosophical Journal—which also carried a notice of Brocchi’s death in 1827. The notion that new species replace older, extinct ones—in what today would be called an explicitly phylogenetic context—permeates these essays. Herschel’s (1830) discussion of temporal replacement of species and the modernization of faunas closely mirrors these prior discussions. His book, dedicated to the search for natural causes of natural phenomena, was read by Charles Darwin while a student at Cambridge. Darwin’s work on HMS Beagle was in large measure an exploration of replacement patterns of “allied forms” of endemic species in time and in space. His earliest discussions of transmutation, in his essay February 1835, as well as the Red Notebook and the early pages of Notebook B (the latter two written in 1837 back in England), contain Brocchi’s analogy, including the idea of inherent species longevities. Darwin’s first theory of the origin of species was explicitly saltational, invoking geographic isolation as the main cause of the abrupt appearance of new species. We conclude that Darwin was testing the predicted patterns of both Brocchian and Lamarckian transmutation as early as 1832 at the outset of his work on the Beagle.     

A simple model of retina-LGN transmission

To gain a deeper understanding of the transmission of visual signals from retina through the lateral geniculate nucleus (LGN), we have used a simple leaky integrate and-fire model to simulate a relay cell in the LGN. The simplicity of the model was motivated by two questions: (1) Can an LGN model that is driven by a retinal spike train recorded as synaptic (‘S’) potentials, but does not include a diverse array of ion channels, nor feedback inputs from the cortex, brainstem, and thalamic reticular nucleus, accurately simulate the LGN discharge on a spike-for-spike basis? (2) Are any special synaptic mechanisms, beyond simple summation of currents, necessary to model experimental recordings? We recorded cat relay cell responses to spatially homogeneous small or large spots, with luminance that was rapidly modulated in a pseudo-random fashion. Model parameters for each cell were optimized with a Simplex algorithm using a short segment of the recording. The model was then tested on a much longer, distinct data set consisting of responses to numerous repetitions of the noisy stimulus. For LGN cells that spiked in response to a sufficiently large fraction of retinal inputs, we found that this simplified model accurately predicted the firing times of LGN discharges. This suggests that modulations of the efficacy of the retino-geniculate synapse by pre-synaptic facilitation or depression are not necessary in order to account for the LGN responses generated by our stimuli, and that post-synaptic summation is sufficient.     

Characteristics of Human Luminance Discrimination and Modeling a Neural Network Based on the Response Properties of the Visual Cortex

Reaction time (RT) and error rate that depend on stimulus duration were measured in a luminance-discrimination reaction time task. Two patches of light with different luminance were presented to participants for ‘short’ (150 ms) or ‘long’ (1 s) period on each trial. When the stimulus duration was ‘short’, the participants responded more rapidly with poorer discrimination performance than they did in the longer duration. The results suggested that different sensory responses in the visual cortices were responsible for the dependence of response speed and accuracy on the stimulus duration during the luminance-discrimination reaction time task. It was shown that the simple winner-take-all-type neural network model receiving transient and sustained stimulus information from the primary visual cortex successfully reproduced RT distributions for correct responses and error rates. Moreover, temporal spike sequences obtained from the model network closely resembled to the neural activity in the monkey prefrontal or parietal area during other visual decision tasks such as motion discrimination and oddball detection tasks.     

A reaction-diffusion model to capture disparity selectivity in primary visual cortex

Decades of experimental studies are available on disparity selective cells in visual cortex of macaque and cat. Recently, local disparity map for iso-orientation sites for near-vertical edge preference is reported in area 18 of cat visual cortex. No experiment is yet reported on complete disparity map in V1. Disparity map for layer IV in V1 can provide insight into how disparity selective complex cell receptive field is organized from simple cell subunits. Though substantial amounts of experimental data on disparity selective cells is available, no model on receptive field development of such cells or disparity map development exists in literature. We model disparity selectivity in layer IV of cat V1 using a reaction-diffusion two-eye paradigm. In this model, the wiring between LGN and cortical layer IV is determined by resource an LGN cell has for supporting connections to cortical cells and competition for target space in layer IV. While competing for target space, the same type of LGN cells, irrespective of whether it belongs to left-eye-specific or right-eye-specific LGN layer, cooperate with each other while trying to push off the other type. Our model captures realistic 2D disparity selective simple cell receptive fields, their response properties and disparity map along with orientation and ocular dominance maps. There is lack of correlation between ocular dominance and disparity selectivity at the cell population level. At the map level, disparity selectivity topography is not random but weakly clustered for similar preferred disparities. This is similar to the experimental result reported for macaque. The details of weakly clustered disparity selectivity map in V1 indicate two types of complex cell receptive field organization.     

Linkage maps of the apple ( Malus × domestica Borkh.) cvs ‘Ralls Janet’ and ‘Delicious’ include newly developed EST markers

Two apple genetic linkage maps were constructed using amplified fragment length polymorphisms (AFLPs), simple sequence repeats (SSRs), random amplified polymorphic DNAs (RAPDs), and expressed sequence tag (EST)-derived markers in combination with a pseudo-testcross mapping strategy in which the cultivars ‘Ralls Janet’ and ‘Delicious’ were used as the respective seed parents. Mitsubakaido (Malus sieboldii) was used as the pollen parent for each of the segregating F₁ populations. Expressed sequence tag data were obtained from the random sequencing of cDNA libraries constructed from in vitro cultured shoots and maturing fruits of cv ‘Fuji’, which is the offspring of a cross between ‘Ralls Janet’ and ‘Delicious’. In addition, a number of published gene sequences were used to develop markers for mapping. The ‘Ralls Janet’ map consisted of 346 markers (178 AFLPs, 95 RAPDs, 54 SSRs, 18 ESTs, and the S locus) in 17 linkage groups, with a total length of 1082 cM, while that of ‘Delicious’ comprised 300 markers (120 AFLPs, 81 RAPDs, 64 SSRs, 32 ESTs, and the S, Rf, and MdACS-1 loci) on 17 linkage groups spanning 1031 cM. These maps are amenable to comparisons with previously published maps of ‘Fiesta’ and ‘Discovery’ (Liebhard et al., Mol Breed 10:217–241, 2002; Liebhard et al., Theor Appl Genet 106:1497–1508, 2003a) because several of the SSRs (one to three markers per linkage group) were used in all of the maps. Distorted marker segregation was observed in three and two regions of the ‘Ralls Janet’ and ‘Delicious’ maps, respectively. These regions were localized in different parts of the genome from those in previously reported apple linkage maps. This marker distortion may be dependent on the combinations of cultivars used for map construction.     

Temperature controls a latitudinal gradient in the proportion of watershed nitrogen exported to coastal ecosystems

Increased export of biologically available nitrogen (N) to the coastal zone is strongly linked to eutrophication, which is a major problem in coastal marine ecosystems (NRC (2000) Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution. National Academy Press, Washington, DC; Bricker et al. (1999) National Estuarine Eutrophication Assessment. Effects of nutrient enrichment in the nation’s estuaries. NOAA-NOS Special Projects Office, Silver Spring, MD). However, not all of the nitrogen input to a watershed is exported to the coast (Howarth et al. (1996) Biogeochemistry 35:75–139; Jordan and Weller (1996) Bioscience 46:655–664). Global estimates of nitrogen export to coasts have been taken to be 25% of watershed input, based largely on northeastern U.S. observations (Galloway et al. (2004) Biogeochemistry 70:153–226; Boyer et al. (2006) Global Biogeochem Cycle 20:Art. No. GB1S91). We applied the N budgeting methodology developed for the International SCOPE Nitrogen project (Howarth et al. (1996) Biogeochemistry 35:75–139; Boyer et al. (2002) Biogeochemistry 57:137–169) to 12 watersheds in the southeastern U.S., and compared them with estimates of N export for 16 watersheds in the northeastern U.S. (Boyer et al. (2002) Biogeochemistry 57:137–169). In southeastern watersheds, average N export was only 9% of input, suggesting the need for downward revision of global estimates. The difference between northern and southern watersheds is not a function of the absolute value of N inputs, which spanned a comparable range and were positively related to export in both cases. Rather, the proportion of N exported was significantly related to average watershed temperature (% N export = 58.41 e^{−0.11 * temperature}; R ² = 0.76), with lower proportionate nitrogen export in warmer watersheds. In addition, we identified a threshold in proportionate N export at 38°N latitude that corresponds to a reported breakpoint in the rate of denitrification at 10–12°C. We hypothesize that temperature, by regulating denitrification, results in increased proportionate N export at higher latitudes. Regardless of the mechanism, these observations suggest that temperature increases associated with future climate change may well reduce the amount of nitrogen that reaches estuaries, which will have implications for coastal eutrophication.     

A cooperation and competition based simple cell receptive field model and study of feed-forward linear and nonlinear contributions to orientation selectivity

We present a model for development of orientation selectivity in layer IV simple cells. Receptive field (RF) development in the model, is determined by diffusive cooperation and resource limited competition guided axonal growth and retraction in geniculocortical pathway. The simulated cortical RFs resemble experimental RFs. The receptive field model is incorporated in a three-layer visual pathway model consisting of retina, LGN and cortex. We have studied the effect of activity dependent synaptic scaling on orientation tuning of cortical cells. The mean value of hwhh (half width at half the height of maximum response) in simulated cortical cells is 58° when we consider only the linear excitatory contribution from LGN. We observe a mean improvement of 22.8° in tuning response due to the non-linear spiking mechanisms that include effects of threshold voltage and synaptic scaling factor.     

Sexual selection for syntax and kin selection for semantics: problems and prospects

The evolution of human language, and the kind of thought the communication of which requires it, raises considerable explanatory challenges. These systems of representation constitute a radical discontinuity in the natural world. Even species closely related to our own appear incapable of either thought or talk with the recursive structure, generalized systematicity, and task-domain neutrality that characterize human talk and the thought it expresses. W. Tecumseh Fitch’s proposal (2004, in press) that human language is descended from a sexually selected, prosodic proto-language that approximated its syntactic complexity, and later acquired semantics thanks to kin selection for its use as a means of pedagogical transmission, has the promise of meeting these explanatory challenges. However, Fitch’s theory raises two problems of its own: (1) according to Boyd and Richerson (1996, Proc. Br. Acad. 88: 77–93), circumstances in which pedagogy is adaptive are inevitably rare in nature, and (2) it is unlikely that our non-discursive precursors had generally systematic, task-domain neutral thoughts to communicate to their offspring. I propose solutions to these problems. Pedagogy would be favored in a population where complex rituals dominated diverse aspects of life. Prosodic proto-language could emerge as the medium of pedagogic transmission. As this medium was used to teach a greater variety of tasks, it would become increasingly general and domain neutral. The presence and importance of such a system of communication in hominid populations could then drive, via Baldwinian mechanisms, the evolution of a kind of ‘thinking for speaking’ (Slobin 1991, Pragmatics 1: 7–25) characterized by recursive structure, generalized systematicity, and task-domain neutrality.     

Elemental and configural olfactory coding by antennal lobe neurons of the honeybee (<Emphasis Type="Italic">Apis mellifera</Emphasis>)

When smelling an odorant mixture, olfactory systems can be analytical (i.e. extract information about the mixture elements) or synthetic (i.e. creating a configural percept of the mixture). Here, we studied elemental and configural mixture coding in olfactory neurons of the honeybee antennal lobe, local neurons in particular. We conducted intracellular recordings and stimulated with monomolecular odorants and their coherent or incoherent binary mixtures to reproduce a temporally dynamic environment. We found that about half of the neurons responded as ‘elemental neurons’, i.e. responses evoked by mixtures reflected the underlying feature information from one of the components. The other half responded as ‘configural neurons’, i.e. responses to mixtures were clearly different from responses to their single components. Elemental neurons divided in late responders (above 60 ms) and early responder neurons (below 60 ms), whereas responses of configural coding neurons concentrated in-between these divisions. Latencies of neurons with configural responses express a tendency to be faster for coherent stimuli which implies employment in different processing circuits.