Receptive field mechanisms of ganglion cells in the cat retina

On the basis of anatomical and physiological results of the vertebrate retina, a method is proposed for analysing the respective fields of ganglion cells in the cat retina. In the model, we assume the following: (a) Ganglion cells receive their input from bipolar and/or amacrine cells. (b) The nonlinearity of ganglion cell responses is due to the activities of transient type amacrine cells. The method has been proved to be effective. According to the results of this investigation, the receptive field properties of X type and Y type ganglion cells are heterogeneous. Thus, it may be considered that their receptive fields consist of center and surround mechanisms. The receptive field properties of X-cells are almost linear and the X-cells seem to receive most of their input from bipolar cells. On the other hand, the ones of Y-cells are highly nonlinear. Consequently, it is conceivable that the Y-cells receive their input mainly from transient type amacrine cells.     

Neural action fields for optic flow based navigation: a simulation study of the fly lobula plate network

Optic flow based navigation is a fundamental way of visual course control described in many different species including man. In the fly, an essential part of optic flow analysis is performed in the lobula plate, a retinotopic map of motion in the environment. There, the so-called lobula plate tangential cells possess large receptive fields with different preferred directions in different parts of the visual field. Previous studies demonstrated an extensive connectivity between different tangential cells, providing, in principle, the structural basis for their large and complex receptive fields. We present a network simulation of the tangential cells, comprising most of the neurons studied so far (22 on each hemisphere) with all the known connectivity between them. On their dendrite, model neurons receive input from a retinotopic array of Reichardt-type motion detectors. Model neurons exhibit receptive fields much like their natural counterparts, demonstrating that the connectivity between the lobula plate tangential cells indeed can account for their complex receptive field structure. We describe the tuning of a model neuron to particular types of ego-motion (rotation as well as translation around/along a given body axis) by its 'action field'. As we show for model neurons of the vertical system (VS-cells), each of them displays a different type of action field, i.e., responds maximally when the fly is rotating around a particular body axis. However, the tuning width of the rotational action fields is relatively broad, comparable to the one with dendritic input only. The additional intra-lobula-plate connectivity mainly reduces their translational action field amplitude, i.e., their sensitivity to translational movements along any body axis of the fly.     

End-to-end neural system identification with neural information flow

Neural information flow (NIF) provides a novel approach for system identification in neuroscience. It models the neural computations in multiple brain regions and can be trained end-to-end via stochastic gradient descent from noninvasive data. NIF models represent neural information processing via a network of coupled tensors, each encoding the representation of the sensory input contained in a brain region. The elements of these tensors can be interpreted as cortical columns whose activity encodes the presence of a specific feature in a spatiotemporal location. Each tensor is coupled to the measured data specific to a brain region via low-rank observation models that can be decomposed into the spatial, temporal and feature receptive fields of a localized neuronal population. Both these observation models and the convolutional weights defining the information processing within regions are learned end-to-end by predicting the neural signal during sensory stimulation. We trained a NIF model on the activity of early visual areas using a large-scale fMRI dataset recorded in a single participant. We show that we can recover plausible visual representations and population receptive fields that are consistent with empirical findings.     

The effect of intracortical competition on the formation of topographic maps in models of Hebbian learning

Correlation-based learning (CBL) models and self-organizing maps (SOM) are two classes of Hebbian models that have both been proposed to explain the activity-driven formation of cortical maps. Both models differ significantly in the way lateral cortical interactions are treated, leading to different predictions for the formation of receptive fields. The linear CBL models predict that receptive field profiles are determined by the average values and the spatial correlations of the second order of the afferent activity patterns, wheras SOM models map stimulus features. Here, we investigate a class of models which are characterized by a variable degree of lateral competition and which have the CBL and SOM models as limit cases. We show that there exists a critical value for intracortical competition below which the model exhibits CBL properties and above which feature mapping sets in. The class of models is then analyzed with respect to the formation of topographic maps between two layers of neurons. For Gaussian input stimuli we find that localized receptive fields and topographic maps emerge above the critical value for intracortical competition, and we calculate this value as a function of the size of the input stimuli and the range of the lateral interaction function. Additionally, we show that the learning rule can be derived via the optimization of a global cost function in a framework of probabilistic output neurons which represent a set of input stimuli by a sparse code. Received: 23 June 1999 / Accepted in revised form: 05 November 1999     

A multi-stage model for fundamental functional properties in primary visual cortex

Many neurons in mammalian primary visual cortex have properties such as sharp tuning for contour orientation, strong selectivity for motion direction, and insensitivity to stimulus polarity, that are not shared with their sub-cortical counterparts. Successful models have been developed for a number of these properties but in one case, direction selectivity, there is no consensus about underlying mechanisms. We here define a model that accounts for many of the empirical observations concerning direction selectivity. The model describes a single column of cat primary visual cortex and comprises a series of processing stages. Each neuron in the first cortical stage receives input from a small number of on-centre and off-centre relay cells in the lateral geniculate nucleus. Consistent with recent physiological evidence, the off-centre inputs to cortex precede the on-centre inputs by a small (～4 ms) interval, and it is this difference that confers direction selectivity on model neurons. We show that the resulting model successfully matches the following empirical data: the proportion of cells that are direction selective; tilted spatiotemporal receptive fields; phase advance in the response to a stationary contrast-reversing grating stepped across the receptive field. The model also accounts for several other fundamental properties. Receptive fields have elongated subregions, orientation selectivity is strong, and the distribution of orientation tuning bandwidth across neurons is similar to that seen in the laboratory. Finally, neurons in the first stage have properties corresponding to simple cells, and more complex-like cells emerge in later stages. The results therefore show that a simple feed-forward model can account for a number of the fundamental properties of primary visual cortex.     

Synaptic organization and ionic basis of on and off channels in mudpuppy retina. III. A model of ganglion cell receptive field organization based on chloride-free experiments

A chloride-free environment produces selective changes in the retinal network which include a separation of on and off channels. The identification of chloride-sensitive and insensitivie neuronal activity permits identification of some of the connections and intervening polarities of synaptic interactions which are expressed in ganglion cell receptive field organization. These experiments support previous suggestions that surround antagonism is dependent on horizontal cell activity. In addition they suggest a model of the neuronal connections which subserve on-center, off-center, and on-off ganglion cells. Experimental tests of the on-off ganglion cell model favor the idea that this type of ganglion cell receives inhibitory input from amacrine cells and excitatory activation from depolarizing and hyperpolarizing bipolar cells.     

A Model of Self-Organizing Head-Centered Visual Responses in Primate Parietal Areas

We present a hypothesis for how head-centered visual representations in primate parietal areas could self-organize through visually-guided learning, and test this hypothesis using a neural network model. The model consists of a competitive output layer of neurons that receives afferent synaptic connections from a population of input neurons with eye position gain modulated retinal receptive fields. The synaptic connections in the model are trained with an associative trace learning rule which has the effect of encouraging output neurons to learn to respond to subsets of input patterns that tend to occur close together in time. This network architecture and synaptic learning rule is hypothesized to promote the development of head-centered output neurons during periods of time when the head remains fixed while the eyes move. This hypothesis is demonstrated to be feasible, and each of the core model components described is tested and found to be individually necessary for successful self-organization.     

Influence of amacrine cells on receptive field organization of ganglion cells of the generalized vertebrate cone retina: Electronic simulation

Two classes of amacrine cells are simulated, small-field and large-field. Small-field amacrine cells are formed by input from a single bipolar cell, while large-field amacrine cell is formed by inputs from same 7 bipolar cells that form the ganglion cell. Only tonic amacrine cells are studied with both chromatic and luminosity types as well as double-and single-opponent receptive fields. Amacrine cells are used in both feedforward to ganglion cells and feedback to bipolar and horizontal cells. Feedback to bipolar cells or feedfoward to ganglion cells affected steady state levels in a predictable fashion. Negative feedback to bipolar cells and positive feedfoward to ganglion cells does not introduce transients to ganglion cells while negative feedback to horizontal cells and negative feedfoward does. Feedback to horizontal cells produces complex effects on bipolar, amacrine and ganglion cells dependent on such factors as center-surround field balance and negative feedback from luminosity type of horizontal cell to cones.     

Emergence of spatiotemporal receptive fields and its application to motion detection

A model of motion sensitivity as observed in some cells of area V1 of the visual cortex is proposed. Motion sensitivity is achieved by a combination of different spatiotemporal receptive fields, in particular, spatial and temporal differentiators. The receptive fields emerge if a Hebbian learning rule is applied to the network. Similar to a Linsker model the network has a spatially convergent, linear feedforward structure. Additionally, however, delays omnipresent in the brain are incorporated in the model. The emerging spatiotemporal receptive fields are derived explicitly by extending the approach of MacKay and Miller. The response characteristic of the network is calculated in frequency space and shows that the network can be considered as a spacetime filter for motion in one direction. The emergence of different types of receptive field requires certain structural constraints regarding the spatial and temporal arborisation. These requirements can be derived from the theoretical analysis and might be compared with neuroanatomical data. In this way an explicit link between structure and function of the network is established.     

Stochastically Gating Ion Channels Enable Patterned Spike Firing through Activity-Dependent Modulation of Spike Probability

The transformation of synaptic input into patterns of spike output is a fundamental operation that is determined by the particular complement of ion channels that a neuron expresses. Although it is well established that individual ion channel proteins make stochastic transitions between conducting and non-conducting states, most models of synaptic integration are deterministic, and relatively little is known about the functional consequences of interactions between stochastically gating ion channels. Here, we show that a model of stellate neurons from layer II of the medial entorhinal cortex implemented with either stochastic or deterministically gating ion channels can reproduce the resting membrane properties of stellate neurons, but only the stochastic version of the model can fully account for perithreshold membrane potential fluctuations and clustered patterns of spike output that are recorded from stellate neurons during depolarized states. We demonstrate that the stochastic model implements an example of a general mechanism for patterning of neuronal output through activity-dependent changes in the probability of spike firing. Unlike deterministic mechanisms that generate spike patterns through slow changes in the state of model parameters, this general stochastic mechanism does not require retention of information beyond the duration of a single spike and its associated afterhyperpolarization. Instead, clustered patterns of spikes emerge in the stochastic model of stellate neurons as a result of a transient increase in firing probability driven by activation of HCN channels during recovery from the spike afterhyperpolarization. Using this model, we infer conditions in which stochastic ion channel gating may influence firing patterns in vivo and predict consequences of modifications of HCN channel function for in vivo firing patterns.     

Sparse Coding Models Can Exhibit Decreasing Sparseness while Learning Sparse Codes for Natural Images

The sparse coding hypothesis has enjoyed much success in predicting response properties of simple cells in primary visual cortex (V1) based solely on the statistics of natural scenes. In typical sparse coding models, model neuron activities and receptive fields are optimized to accurately represent input stimuli using the least amount of neural activity. As these networks develop to represent a given class of stimulus, the receptive fields are refined so that they capture the most important stimulus features. Intuitively, this is expected to result in sparser network activity over time. Recent experiments, however, show that stimulus-evoked activity in ferret V1 becomes less sparse during development, presenting an apparent challenge to the sparse coding hypothesis. Here we demonstrate that some sparse coding models, such as those employing homeostatic mechanisms on neural firing rates, can exhibit decreasing sparseness during learning, while still achieving good agreement with mature V1 receptive field shapes and a reasonably sparse mature network state. We conclude that observed developmental trends do not rule out sparseness as a principle of neural coding per se: a mature network can perform sparse coding even if sparseness decreases somewhat during development. To make comparisons between model and physiological receptive fields, we introduce a new nonparametric method for comparing receptive field shapes using image registration techniques.     

Features and functions of nonlinear spatial integration by retinal ganglion cells

Ganglion cells in the vertebrate retina integrate visual information over their receptive fields. They do so by pooling presynaptic excitatory inputs from typically many bipolar cells, which themselves collect inputs from several photoreceptors. In addition, inhibitory interactions mediated by horizontal cells and amacrine cells modulate the structure of the receptive field. In many models, this spatial integration is assumed to occur in a linear fashion. Yet, it has long been known that spatial integration by retinal ganglion cells also incurs nonlinear phenomena. Moreover, several recent examples have shown that nonlinear spatial integration is tightly connected to specific visual functions performed by different types of retinal ganglion cells. This work discusses these advances in understanding the role of nonlinear spatial integration and reviews recent efforts to quantitatively study the nature and mechanisms underlying spatial nonlinearities. These new insights point towards a critical role of nonlinearities within ganglion cell receptive fields for capturing responses of the cells to natural and behaviorally relevant visual stimuli. In the long run, nonlinear phenomena of spatial integration may also prove important for implementing the actual neural code of retinal neurons when designing visual prostheses for the eye.     

Characteristics of dorsal horn neurons expressing subliminal responses to sural nerve stimulation

The present study was designed (1) to characterize the subliminal responses of dorsal horn neurons to stimulation of the sural nerve, and (2) to correlate the type of response to this stimulus with the responses to natural mechanical stimulation of the skin. To accomplish this, intracellular and extracellular recordings were carried out in L6 and L7 dorsal horn neurons in the cat. The excitatory responses of each cell to electrical stimulation of the sural nerve and to mechanical stimulation of the skin were noted. Of 35 dorsal horn cells recorded intracellularly, 11 responded with impulses to sural nerve stimulation, 9 responded with excitatory postsynaptic potentials (EPSPs) but not impulses, and 15 had no excitatory responses to this stimulus. The type of response to sural nerve stimulation was strongly correlated with receptive field modality. Most cells receiving an input from high-threshold cutaneous mechanoreceptors responded with impulses or gave no excitatory response to sural nerve stimulation, whereas most cells that had only low-threshold mechanoreceptor input responded with EPSPs only or gave no response. In cells with only low-threshold (LT) mechanoreceptive input, response to sural nerve stimulation was highly correlated with receptive field locus. Those LT cells with no excitatory responses to sural nerve stimulation had receptive fields confined to the foot and/or toes, whereas those that gave EPSPs had more proximal receptive fields. The possible significance of these data with reference to changes observed after lesions, such as increased response to sural nerve stimulation, increased receptive field size, and somatotopic reorganization, is discussed.     

15th Annual spring brain conference,march 10–13, 2004,summary report

The present study was designed (1) to characterize the subliminal responses of dorsal horn neurons to stimulation of the sural nerve, and (2) to correlate the type of response to this stimulus with the responses to natural mechanical stimulation of the skin. To accomplish this, intracellular and extracellular recordings were carried out in L⁶ and L⁷ dorsal horn neurons in the cat. The excitatory responses of each cell to electrical stimulation of the sural nerve and to mechanical stimulation of the skin were noted.

Of 35 dorsal horn cells recorded intracellularly, 11 responded with impulses to sural nerve stimulation, 9 responded with excitatory postsynaptic potentials (EPSPs) but not impulses, and 15 had no excitatory responses to this stimulus. The type of response to sural nerve stimulation was strongly correlated with receptive field modality. Most cells receiving an input from high-threshold cutaneous mechanoreceptors responded with impulses or gave no excitatory response to sural nerve stimulation, whereas most cells that had only low-threshold mechanoreceptor input responded with EPSPs only or gave no response. In cells with only low-threshold (LT) mechanoreceptive input, response to sural nerve stimulation was highly correlated with receptive field locus. Those LT cells with no excitatory responses to sural nerve stimulation had receptive fields confined to the foot and/or toes, whereas those that gave EPSPs had more proximal receptive fields. The possible significance of these data with reference to changes observed after lesions, such as increased response to sural nerve stimulation, increased receptive field size, and somatotopic reorganization, is discussed.     

Synaptic inputs to the ganglion cells in the tiger salamander retina

The postsynaptic potentials (PSPs) that form the ganglion cell light response were isolated by polarizing the cell membrane with extrinsic currents while stimulating at either the center or surround of the cell's receptive field. The time-course and receptive field properties of the PSPs were correlated with those of the bipolar and amacrine cells. The tiger salamander retina contains four main types of ganglion cell: "on" center, "off" center, "on-off", and a "hybrid" cell that responds transiently to center, but sustainedly, to surround illumination. The results lead to these inferences. The on-ganglion cell receives excitatory synpatic input from the on bipolars and that synapse is "silent" in the dark. The off-ganglion cell receives excitatory synaptic input from the off bipolars with this synapse tonically active in the dark. The on-off and hybrid ganglion cells receive a transient excitatory input with narrow receptive field, not simply correlated with the activity of any presynaptic cell. All cell types receive a broad field transient inhibitory input, which apparently originates in the transient amacrine cells. Thus, most, but not all, ganglion cell responses can be explained in terms of synaptic inputs from bipolar and amacrine cells, integrated at the ganglion cell membrane.     

Adaptation-dependent synchronous activity contributes to receptive field size change of bullfrog retinal ganglion cell

Nearby retinal ganglion cells of similar functional subtype have a tendency to discharge spikes in synchrony. The synchronized activity is involved in encoding some aspects of visual input. On the other hand, neurons always continuously adjust their activities in adaptation to some features of visual stimulation, including mean ambient light, contrast level, etc. Previous studies on adaptation were primarily focused on single neuronal activity, however, it is also intriguing to investigate the adaptation process in population neuronal activities. In the present study, by using multi-electrode recording system, we simultaneously recorded spike discharges from a group of dimming detectors (OFF-sustained type ganglion cells) in bullfrog retina. The changes in receptive field properties and synchronization strength during contrast adaptation were analyzed. It was found that, when perfused using normal Ringer's solution, single neuronal receptive field size was reduced during contrast adaptation, which was accompanied by weakening in synchronization strength between adjacent neurons' activities. When dopamine (1 μM) was applied, the adaptation-related receptive field area shrinkage and synchronization weakening were both eliminated. The activation of D1 receptor was involved in the adaptation-related modulation of synchronization and receptive field. Our results thus suggest that the size of single neuron's receptive field is positively related to the strength of its synchronized activity with its neighboring neurons, and the dopaminergic pathway is responsible for the modulation of receptive field property and synchronous activity of the ganglion cells during the adaptation process.     

Transformation of Stimulus Correlations by the Retina

Redundancies and correlations in the responses of sensory neurons may seem to waste neural resources, but they can also carry cues about structured stimuli and may help the brain to correct for response errors. To investigate the effect of stimulus structure on redundancy in retina, we measured simultaneous responses from populations of retinal ganglion cells presented with natural and artificial stimuli that varied greatly in correlation structure; these stimuli and recordings are publicly available online. Responding to spatio-temporally structured stimuli such as natural movies, pairs of ganglion cells were modestly more correlated than in response to white noise checkerboards, but they were much less correlated than predicted by a non-adapting functional model of retinal response. Meanwhile, responding to stimuli with purely spatial correlations, pairs of ganglion cells showed increased correlations consistent with a static, non-adapting receptive field and nonlinearity. We found that in response to spatio-temporally correlated stimuli, ganglion cells had faster temporal kernels and tended to have stronger surrounds. These properties of individual cells, along with gain changes that opposed changes in effective contrast at the ganglion cell input, largely explained the pattern of pairwise correlations across stimuli where receptive field measurements were possible.     

Electrical coupling of photoreceptors in retinal network models

The spatial width of photoreceptor receptive fields affects the processing of signals in neural networks of the retina. This effect has been examined using the simple recurrent and non-recurrent network models, where lateral interaction strength was adjusted to approximate a prescribed receptive field profile. The results indicate that the optimal performance of the networks is obtained with photoreceptor receptive fields wider than the ganglion cell separation. It is thus concluded that while electrical coupling of photoreceptors in the retina reduces the intrinsic noise in the system, it also improves the sampling efficiency of the laterally coupled neural network of the retina.     

Simulander: A neural network model for the orientation movement of salamanders

Simulander is a feedforward neural network simulating the orientation movement of salamanders. The orientation movement is part of the prey capture behavior; it is performed with the head alone. Simulander is a network which consists of 300 neurons incorporating several cytoarchitectonic and electrophysiological features of the salamander brain. The network is trained by means of an evolution strategy. Although only 100 tectum neurons with fairly large receptive fields are used (coarse coding), Simulander is able to localize an irregularly moving prey precisely. It is demonstrated that large receptive field neurons are important for successful prey localization. The removal of a model tectum hemisphere leads to a network which accounts for investigations made in living monocular salamanders. The model also yields an understanding of electrical stimulation experiments in toads.     

Laser microbeam axotomy and conduction block show that electrical transmission at a central synapse is distributed at multiple contacts

Touch (T) sensory neurons in the leech innervate defined regions of skin and synapse on other neurons, including other T cells, within the ganglionic neuropil. The cells' receptive fields in the periphery are comprised of a central region, innervated by thick axons, and adjoining regions (minor fields) innervated by thinner axons. Secondary branches, known to be sites of synapses, emerge from the thinner and thicker axons. Pairs of T cells appear to make up to 200 separate contacts distributed within the neuropil. When the T cell is hyperpolarized, as occurs during natural stimulation of the cell, action potentials generated in the minor field and travelling into the ganglion along the thin axons may fail to conduct at central branch points. Evidence is presented, using axon conduction block and laser axotomy of cells filled with 6-carboxy-fluorescein, that synapses between separate groups of branches can function independently. Thus, selective activation of branches of the thin anterior axon produced a synaptic potential 36 +/- 6% of control amplitude, which was consistent with counts of 39 +/- 6% of contacts made by these branches. Laser axotomy of postsynaptic neurons showed that the anterior contacts indeed made the principal or only contacts activated during anterior conduction block. The results show that conduction block can modulate transmission within the ganglion, and it operates by silencing particular contacts between cells.