Emiliano La Cara - Academia.edu (original) (raw)

Papers by Emiliano La Cara

Research paper thumbnail of An oscillatory neural network implementing the similarity and prior knowledge gestalt rules: object segmentation and retrieval

Research paper thumbnail of Simulation of similarity and previous knowledge Gestalt rules with coupled neural oscillators

3rd European Medical & Biological Engineering Conference, IFMBE European Conference on Biomedical Engineering (EMBEC '05), 2005

Research paper thumbnail of Simulation of tilt aftereffect of visual cells in the primary visual cortex

3rd European Medical & Biological Engineering Conference, IFMBE European Conference on Biomedical Engineering (EMBEC'05), 2005

Research paper thumbnail of Binding and segmentation of multiple objects through neural oscillators inhibited by contour information

Biological cybernetics, 2003

Temporal correlation of neuronal activity has been suggested as a criterion for multiple object r... more Temporal correlation of neuronal activity has been suggested as a criterion for multiple object recognition. In this work, a two-dimensional network of simplified Wilson-Cowan oscillators is used to manage the binding and segmentation problem of a visual scene according to the connectedness Gestalt criterion. Binding is achieved via original coupling terms that link excitatory units to both excitatory and inhibitory units of adjacent neurons. These local coupling terms are time independent, i.e., they do not require Hebbian learning during the simulations. Segmentation is realized by a two-layer processing of the visual image. The first layer extracts all object contours from the image by means of "retinal cells" with an "on-center" receptive field. Information on contour is used to selectively inhibit Wilson-Cowan oscillators in the second layer, thus realizing a strong separation among neurons in different objects. Accidental synchronism between oscillations in...

Research paper thumbnail of Modeling segmentation of a visual scene via neural oscillators: fragmentation, discovery of details and attention

Network: Computation in Neural Systems, 2004

The present study analyses the problem of binding and segmentation of a visual scene by means of ... more The present study analyses the problem of binding and segmentation of a visual scene by means of a network of neural oscillators, laying emphasis on the problems of fragmentation, perception of details at different scales and spatial attention. The work is based on a two-layer model: a second layer of Wilson-Cowan oscillators is inhibited by information from the first layer. Moreover, the model uses a global inhibitor (GI) to segment objects. Spatial attention consists of an excitatory input, surrounded by an inhibitory annulus. A single object is identified by synchronous oscillatory activity of neural groups. The main idea of this work is that segmentation of objects at different detail levels can be achieved by linking parameters of the GI (i.e. the threshold and the inhibition strength) with the dimension of the zone selected by attention and with the dimension of the smaller objects to be detected. Simulations show that three possible kinds of behavior can be attained with the model, through proper choice of the GI parameters and attention input: (i) large objects in the visual scene are perceived, while small details are suppressed; (ii) large objects are perceived, while details are assembled together to constitute a single 'noise term'; (iii) if attention is focused on a smaller area and the GI parameters modulated accordingly (i.e. the threshold and attention strength are reduced) details are individually perceived as separate objects. These results suggest that the GI and attention may represent two concurrent aspects of the same attentive mechanism, i.e. they should work together to provide flexible management of a visual scene at different levels of detail.

Research paper thumbnail of Travelling waves and EEG patterns during epileptic seizure: Analysis with an integrate-and-fire neural network

Journal of Theoretical Biology, 2006

Epilepsy is characterized by paradoxical patterns of neural activity. They may cause different ty... more Epilepsy is characterized by paradoxical patterns of neural activity. They may cause different types of electroencephalogram (EEG), which dynamically change in shape and frequency content during the temporal evolution of seizure. It is generally assumed that these epileptic patterns may originate in a network of strongly interconnected neurons, when excitation dominates over inhibition. The aim of this work is to use a neural network composed of 50 Â 50 integrate-and-fire neurons to analyse which parameter alterations, at the level of synapse topology, may induce network instability and epileptic-like discharges, and to study the corresponding spatiotemporal characteristics of electrical activity in the network. We assume that a small group of central neurons is stimulated by a depolarizing current (epileptic focus) and that neurons are connected via a Mexican-hat topology of synapses. A signal representative of cortical EEG (ECoG) is simulated by summing the membrane potential changes of all neurons. A sensitivity analysis on the parameters describing the synapse topology shows that an increase in the strength and in spatial extension of excitatory vs. inhibitory synapses may cause the occurrence of travelling waves, which propagate along the network. These propagating waves may cause EEG patterns with different shape and frequency, depending on the particular parameter set used during the simulations. The resulting model EEG signals include irregular rhythms with large amplitude and a wide frequency content, lowamplitude high-frequency rapid discharges, isolated or repeated bursts, and low-frequency quasi-sinusoidal patterns. A slow progressive temporal variation in a single parameter may cause the transition from one pattern to another, thus generating a highly non-stationary signal which resembles that observed during ECoG measurements. These results may help to elucidate the mechanisms at the basis of some epileptic discharges, and to relate rapid changes in EEG patterns with the underlying alterations at the network level.

Research paper thumbnail of Dependence of Visual Cell Properties on Intracortical Synapses Among Hypercolumns: Analysis by a Computer Model

Journal of Computational Neuroscience, 2005

The role of intracortical synapses in affecting the property of visual cells is investigated by m... more The role of intracortical synapses in affecting the property of visual cells is investigated by means of an original mathematical model of cortical circuitry in V1. The model represents a compromise between computational simplicity and physiological reliability. The model incorporates four different inputs into a cortical cell: thalamic input from the lateral geniculate nucleus, according to an even Gabor function; short-range inhibition confined within the hypercolumn; a long-range excitation, which emphasizes the properties of the input; and a long-range inhibition. In the model we assume that all cells receive a similar thalamic input, which differs simply according to its position in the retina and orientation preference. Simulations were performed, with different parameter values, to assess the main characteristics of cell response (i.e., the width and locations of subregions in the receptive field (RF), orientation tuning curve, and response to drifting and counterphase gratings) as a function of the strength and extension of intracortical excitatory synapses. Results suggest that, if intracortical excitation is confined within the hypercolumn, the cells exhibit the same properties as simple cells, both with regards to the width and shape of the RF, orientation tuning curve, and response to drifting and counterphase gratings. By contrast, if excitatory synapses extend beyond the hypercolumn with sufficient strength, the cells exhibit the typical characteristics of complex cells. A progressive shift from complex to simple cells can be realized with a monotonic variation in parameters. Simulations are also performed with a hierarchical model, to suggest possible experiments able to discriminate the present recurrent mechanism from the classical hierarchical one. Results support the assumptions of previous simpler models (Chance et al., 1999) and may help to understand and assess the role of intracortical synapses in rigorous quantitative terms.

Research paper thumbnail of Direction selectivity of simple cells in the primary visual cortex: Comparison of two alternative mathematical models. I: Response to drifting gratings

Computers in Biology and Medicine, 2007

Two models of a single hypercolumn in the primary visual cortex are presented, and used for the a... more Two models of a single hypercolumn in the primary visual cortex are presented, and used for the analysis of direction selectivity in simple cells. The two models differ as to the arrangement of inhibitory connections: in the first ("antiphase model") inhibition is in phase opposition with excitation, but with a similar orientation tuning; in the second ("in-phase model"), inhibition is in phase with excitation, but with broader orientation tuning. Simulation results, performed by using drifting gratings with different orientations, and different spatial and temporal frequencies, show that both models are able to explain the origin of direction preference of simple cells.

Research paper thumbnail of Object segmentation and recovery via neural oscillators implementing the similarity and prior knowledge gestalt rules

Biosystems, 2006

Object recognition requires the solution of the binding and segmentation problems, i.e., grouping... more Object recognition requires the solution of the binding and segmentation problems, i.e., grouping different features to achieve a coherent representation. Synchronization of neural activity in the gamma-band, associated with gestalt perception, has often been proposed as a putative mechanism to solve these problems, not only as to low-level processing, but also in higher cortical functions. In the present work, a network of Wilson-Cowan oscillators is used to segment simultaneous objects, and recover an object from partial or corrupted information, by implementing two gestalt rules: similarity and prior knowledge. The network consists of H different areas, each devoted to representation of a particular feature of the object, according to a topological organization. The similarity law is realized via lateral intra-area connections, arranged as a "Mexican-hat". Prior knowledge is realized via inter-area connections, which link properties belonging to a previously memorized object. A global inhibitor allows segmentation of several objects avoiding interference. Simulation results, performed using three simultaneous input objects, show that the network is able to detect an object even in difficult conditions (i.e., when some features are absent or shifted with respect to the original one). Moreover, the trade-off between sensitivity (capacity to detect true positives) and specificity (capacity to reject false positives) can be controlled acting on the extension of lateral synapses (i.e., on the level of accepted similarity). Finally, the network can also deal with correlated objects, i.e., objects which have some common features. Simulations performed using a different number of objects (2, 3, 4 or 5) suggest that the network is able to segment and recall up to four objects, but the oscillation frequency must increase, the lower the number of objects simultaneously present. The model, although quite simpler compared with neurophysiology, may represent a theoretical framework for the analysis of the relationships between object representation, memory, learning, and gamma-band activity. In particular, it extends previous studies on autoassociative memory since it exploits not only oscillatory dynamics, but also a topological organization of features.

Research paper thumbnail of A neural network for detection of orientation, velocity and direction of movement, based on physiological rules

Aim of this work is to present two neural network models for detection of velocity, orientation a... more Aim of this work is to present two neural network models for detection of velocity, orientation and direction of movement in visual images. Both models mimic a single hypercolumn in the primary visual cortex. They differ as to the arrangement of inhibitory circuitry: in the first ("anti-phase inhibition model") inhibition is in phase opposition with excitation, but with a similar orientation tuning; in the second ("in-phase inhibition model"), inhibition is in phase with excitation, but with larger orientation tuning. Simulation results, performed by using bars with different length and motion direction, show that the models can explain velocity tuning, orientation tuning and direction selectivity of simple cells quite well with a suitable choice of intracortical synapses. The models can be used to test the hypothesis on the disposition of cortical synapses, and could provide practical tools in order to carry out a primary analysis of the movement detection of in...

Research paper thumbnail of Velocity selectivity and axial response to moving bar stimuli in the primary visual cortex: comparison of distinct intracortical inhibition rules

Research paper thumbnail of Binding and segmentation of visual images by means of oscillatory neurons

A neural network based on Wilson-Cowan oscillators is used to perform object recognition in a two... more A neural network based on Wilson-Cowan oscillators is used to perform object recognition in a two-dimensional visual scene. The temporal correlation among groups of oscillating neurons is used as the main criterion to solve the classic binding and segmentation problem. The network uses an original pattern of short-range lateral excitations among adjacent neurons to achieve the binding problem, and an external inhibitory global neuron to provide segmentation of multiple objects in the same visual scene. The latter may represent an "attention mechanism" from neurons at a higher hierarchical level. Simulations performed by using multiple idealized figures (up to 4-5) in the presence of noise suggest that the network can satisfactorily recognize objects in most cases. However, the threshold and time constant of the attention mechanism depend on the complexity (number of objects and level of noise) of the scene under examination. The present results may be useful to improve our understanding of how distributed activities are integrated in the neural system to form single object perceptions. In perspective, the proposed model may find applications in practical algorithms for object recognition.

Research paper thumbnail of A neural network model of contours extraction based on orientation selectivity in the primary visual cortex: Applications on real images

Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

ABSTRACT

Research paper thumbnail of Comparison of different models of orientation selectivity based on distinct intracortical inhibition rules

Vision Research, 2004

Aim of this work is to present simple models of orientation selectivity in the visual cortex, whi... more Aim of this work is to present simple models of orientation selectivity in the visual cortex, which do not require massive computational effort. Three different models are compared, in order to gain deeper insight into the structure of cortical circuits generating inhibitory signals. All models represent a single hypercolumn. They differ as to the arrangement of inhibitory connections: in the first (''antiphase inhibition model'') inhibition is in phase opposition with excitation, but with a similar orientation tuning; in the second (''in-phase inhibition model''), inhibition is in phase with excitation, but with larger orientation tuning. In these two models the orientation width of inhibition increases with contrast. Finally, a third model (''center-surround model'') assumes that inhibition comes from the same cells providing excitation, hence the inhibition tuning is contrast-independent. All models, with suitable values of the intracortical synapse parameters, are able to mimic experimental results in the literature. A few differences are evident between the ''center-surround model'' and the other two, especially as to the dependence of cortical cell response on spatial frequency. The models can represent practical tools to test hypotheses on the disposition of cortical synapses avoiding massive computational efforts.

Research paper thumbnail of A model of contextual interactions and contour detection in primary visual cortex

Neural Networks, 2004

A new model of contour extraction and perceptual grouping in the primary visual cortex is present... more A new model of contour extraction and perceptual grouping in the primary visual cortex is presented and discussed. It differs from previous models since it incorporates four main mechanisms, according to recent physiological data: a feed-forward input from the lateral geniculate nucleus, characterized by Gabor elongated receptive fields; an inhibitory feed-forward input, maximally oriented in the orthogonal direction of the target cell, which suppresses non-optimal stimuli and warrants contrast invariance; an excitatory cortical feedback, which respects co-axial and co-modularity criteria; and a long-range isotropic feedback inhibition. Model behavior has been tested on artificial images with contours of different curvatures, in the presence of considerable noise or in the presence of broken contours, and on a few real images. A sensitivity analysis has also been performed on the role of intracortical synapses. Results show that the model can extract correct contours within acceptable time from image presentation (30-40 ms). The feed-forward input plays a major role to set an initial correct bias for the subsequent feedback and to ensure contrast-invariance. Long-range inhibition is essential to suppress noise, but it may suppress small contours due to excessive competition with greater contours. Cortical excitation sharpens the initial bias and improves saliency of the contours. Model results support the idea that contour extraction is one the primary steps in the visual processing stream, and that local processing in V1 is able to solve this task even in difficult conditions, without the participation of higher visual centers.

Research paper thumbnail of Binding and segmentation of visual images by means of oscillatory neurons

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

A neural network based on Wilson-Cowan oscillators is used to perform object recognition in a two... more A neural network based on Wilson-Cowan oscillators is used to perform object recognition in a two-dimensional visual scene. The temporal correlation among groups of oscillating neurons is used as the main criterion to solve the classic binding and segmentation problem. The network uses an original pattern of short-range lateral excitations among adjacent neurons to achieve the binding problem, and an external inhibitory global neuron to provide segmentation of multiple objects in the same visual scene. The latter may represent an "attention mechanism" from neurons at a higher hierarchical level. Simulations performed by using multiple idealized figures (up to 4-5) in the presence of noise suggest that the network can satisfactorily recognize objects in most cases. However, the threshold and time constant of the attention mechanism depend on the complexity (number of objects and level of noise) of the scene under examination. The present results may be useful to improve our understanding of how distributed activities are integrated in the neural system to form single object perceptions. In perspective, the proposed model may find applications in practical algorithms for object recognition.

Research paper thumbnail of A model of contour extraction including multiple scales, flexible inhibition and attention

Neural Networks, 2008

A mathematical model of contextual integration and contour extraction in the primary visual corte... more A mathematical model of contextual integration and contour extraction in the primary visual cortex developed in a recent work [Ursino, M., & La Cara, G. E. (2004). A model of contextual interactions and contour detection in primary visual cortex. Neural Networks, 17, 719-735] has been significantly improved to include two fundamental additional aspects, i.e., multi-scale decomposition and attention. The model incorporates two independent paths for visual processing corresponding to two different scales. Attention from higher hierarchical levels works by modifying different properties of the network: by selecting the portion of the image to be scrutinized and the appropriate scale, by modulating the threshold of a gating mechanism, and by modifying the width and/or strength of lateral inhibition. Through computer simulations of real complex and noisy black-and-white images, we demonstrate that appropriate selection of the above factors allows accurate analysis of image contours at different levels, from global perception of the overall objects without details, down to a fine examination of minute particulars (such as the lips in a face or the fingers of a hand). Attentive reconfiguration of lateral inhibition plays a key role in the analysis of images at different detail levels.

Research paper thumbnail of An oscillatory neural network implementing the similarity and prior knowledge gestalt rules: object segmentation and retrieval

Research paper thumbnail of Simulation of similarity and previous knowledge Gestalt rules with coupled neural oscillators

3rd European Medical & Biological Engineering Conference, IFMBE European Conference on Biomedical Engineering (EMBEC '05), 2005

Research paper thumbnail of Simulation of tilt aftereffect of visual cells in the primary visual cortex

3rd European Medical & Biological Engineering Conference, IFMBE European Conference on Biomedical Engineering (EMBEC'05), 2005

Research paper thumbnail of Binding and segmentation of multiple objects through neural oscillators inhibited by contour information

Biological cybernetics, 2003

Temporal correlation of neuronal activity has been suggested as a criterion for multiple object r... more Temporal correlation of neuronal activity has been suggested as a criterion for multiple object recognition. In this work, a two-dimensional network of simplified Wilson-Cowan oscillators is used to manage the binding and segmentation problem of a visual scene according to the connectedness Gestalt criterion. Binding is achieved via original coupling terms that link excitatory units to both excitatory and inhibitory units of adjacent neurons. These local coupling terms are time independent, i.e., they do not require Hebbian learning during the simulations. Segmentation is realized by a two-layer processing of the visual image. The first layer extracts all object contours from the image by means of "retinal cells" with an "on-center" receptive field. Information on contour is used to selectively inhibit Wilson-Cowan oscillators in the second layer, thus realizing a strong separation among neurons in different objects. Accidental synchronism between oscillations in...

Research paper thumbnail of Modeling segmentation of a visual scene via neural oscillators: fragmentation, discovery of details and attention

Network: Computation in Neural Systems, 2004

The present study analyses the problem of binding and segmentation of a visual scene by means of ... more The present study analyses the problem of binding and segmentation of a visual scene by means of a network of neural oscillators, laying emphasis on the problems of fragmentation, perception of details at different scales and spatial attention. The work is based on a two-layer model: a second layer of Wilson-Cowan oscillators is inhibited by information from the first layer. Moreover, the model uses a global inhibitor (GI) to segment objects. Spatial attention consists of an excitatory input, surrounded by an inhibitory annulus. A single object is identified by synchronous oscillatory activity of neural groups. The main idea of this work is that segmentation of objects at different detail levels can be achieved by linking parameters of the GI (i.e. the threshold and the inhibition strength) with the dimension of the zone selected by attention and with the dimension of the smaller objects to be detected. Simulations show that three possible kinds of behavior can be attained with the model, through proper choice of the GI parameters and attention input: (i) large objects in the visual scene are perceived, while small details are suppressed; (ii) large objects are perceived, while details are assembled together to constitute a single 'noise term'; (iii) if attention is focused on a smaller area and the GI parameters modulated accordingly (i.e. the threshold and attention strength are reduced) details are individually perceived as separate objects. These results suggest that the GI and attention may represent two concurrent aspects of the same attentive mechanism, i.e. they should work together to provide flexible management of a visual scene at different levels of detail.

Research paper thumbnail of Travelling waves and EEG patterns during epileptic seizure: Analysis with an integrate-and-fire neural network

Journal of Theoretical Biology, 2006

Epilepsy is characterized by paradoxical patterns of neural activity. They may cause different ty... more Epilepsy is characterized by paradoxical patterns of neural activity. They may cause different types of electroencephalogram (EEG), which dynamically change in shape and frequency content during the temporal evolution of seizure. It is generally assumed that these epileptic patterns may originate in a network of strongly interconnected neurons, when excitation dominates over inhibition. The aim of this work is to use a neural network composed of 50 Â 50 integrate-and-fire neurons to analyse which parameter alterations, at the level of synapse topology, may induce network instability and epileptic-like discharges, and to study the corresponding spatiotemporal characteristics of electrical activity in the network. We assume that a small group of central neurons is stimulated by a depolarizing current (epileptic focus) and that neurons are connected via a Mexican-hat topology of synapses. A signal representative of cortical EEG (ECoG) is simulated by summing the membrane potential changes of all neurons. A sensitivity analysis on the parameters describing the synapse topology shows that an increase in the strength and in spatial extension of excitatory vs. inhibitory synapses may cause the occurrence of travelling waves, which propagate along the network. These propagating waves may cause EEG patterns with different shape and frequency, depending on the particular parameter set used during the simulations. The resulting model EEG signals include irregular rhythms with large amplitude and a wide frequency content, lowamplitude high-frequency rapid discharges, isolated or repeated bursts, and low-frequency quasi-sinusoidal patterns. A slow progressive temporal variation in a single parameter may cause the transition from one pattern to another, thus generating a highly non-stationary signal which resembles that observed during ECoG measurements. These results may help to elucidate the mechanisms at the basis of some epileptic discharges, and to relate rapid changes in EEG patterns with the underlying alterations at the network level.

Research paper thumbnail of Dependence of Visual Cell Properties on Intracortical Synapses Among Hypercolumns: Analysis by a Computer Model

Journal of Computational Neuroscience, 2005

The role of intracortical synapses in affecting the property of visual cells is investigated by m... more The role of intracortical synapses in affecting the property of visual cells is investigated by means of an original mathematical model of cortical circuitry in V1. The model represents a compromise between computational simplicity and physiological reliability. The model incorporates four different inputs into a cortical cell: thalamic input from the lateral geniculate nucleus, according to an even Gabor function; short-range inhibition confined within the hypercolumn; a long-range excitation, which emphasizes the properties of the input; and a long-range inhibition. In the model we assume that all cells receive a similar thalamic input, which differs simply according to its position in the retina and orientation preference. Simulations were performed, with different parameter values, to assess the main characteristics of cell response (i.e., the width and locations of subregions in the receptive field (RF), orientation tuning curve, and response to drifting and counterphase gratings) as a function of the strength and extension of intracortical excitatory synapses. Results suggest that, if intracortical excitation is confined within the hypercolumn, the cells exhibit the same properties as simple cells, both with regards to the width and shape of the RF, orientation tuning curve, and response to drifting and counterphase gratings. By contrast, if excitatory synapses extend beyond the hypercolumn with sufficient strength, the cells exhibit the typical characteristics of complex cells. A progressive shift from complex to simple cells can be realized with a monotonic variation in parameters. Simulations are also performed with a hierarchical model, to suggest possible experiments able to discriminate the present recurrent mechanism from the classical hierarchical one. Results support the assumptions of previous simpler models (Chance et al., 1999) and may help to understand and assess the role of intracortical synapses in rigorous quantitative terms.

Research paper thumbnail of Direction selectivity of simple cells in the primary visual cortex: Comparison of two alternative mathematical models. I: Response to drifting gratings

Computers in Biology and Medicine, 2007

Two models of a single hypercolumn in the primary visual cortex are presented, and used for the a... more Two models of a single hypercolumn in the primary visual cortex are presented, and used for the analysis of direction selectivity in simple cells. The two models differ as to the arrangement of inhibitory connections: in the first ("antiphase model") inhibition is in phase opposition with excitation, but with a similar orientation tuning; in the second ("in-phase model"), inhibition is in phase with excitation, but with broader orientation tuning. Simulation results, performed by using drifting gratings with different orientations, and different spatial and temporal frequencies, show that both models are able to explain the origin of direction preference of simple cells.

Research paper thumbnail of Object segmentation and recovery via neural oscillators implementing the similarity and prior knowledge gestalt rules

Biosystems, 2006

Object recognition requires the solution of the binding and segmentation problems, i.e., grouping... more Object recognition requires the solution of the binding and segmentation problems, i.e., grouping different features to achieve a coherent representation. Synchronization of neural activity in the gamma-band, associated with gestalt perception, has often been proposed as a putative mechanism to solve these problems, not only as to low-level processing, but also in higher cortical functions. In the present work, a network of Wilson-Cowan oscillators is used to segment simultaneous objects, and recover an object from partial or corrupted information, by implementing two gestalt rules: similarity and prior knowledge. The network consists of H different areas, each devoted to representation of a particular feature of the object, according to a topological organization. The similarity law is realized via lateral intra-area connections, arranged as a "Mexican-hat". Prior knowledge is realized via inter-area connections, which link properties belonging to a previously memorized object. A global inhibitor allows segmentation of several objects avoiding interference. Simulation results, performed using three simultaneous input objects, show that the network is able to detect an object even in difficult conditions (i.e., when some features are absent or shifted with respect to the original one). Moreover, the trade-off between sensitivity (capacity to detect true positives) and specificity (capacity to reject false positives) can be controlled acting on the extension of lateral synapses (i.e., on the level of accepted similarity). Finally, the network can also deal with correlated objects, i.e., objects which have some common features. Simulations performed using a different number of objects (2, 3, 4 or 5) suggest that the network is able to segment and recall up to four objects, but the oscillation frequency must increase, the lower the number of objects simultaneously present. The model, although quite simpler compared with neurophysiology, may represent a theoretical framework for the analysis of the relationships between object representation, memory, learning, and gamma-band activity. In particular, it extends previous studies on autoassociative memory since it exploits not only oscillatory dynamics, but also a topological organization of features.

Research paper thumbnail of A neural network for detection of orientation, velocity and direction of movement, based on physiological rules

Aim of this work is to present two neural network models for detection of velocity, orientation a... more Aim of this work is to present two neural network models for detection of velocity, orientation and direction of movement in visual images. Both models mimic a single hypercolumn in the primary visual cortex. They differ as to the arrangement of inhibitory circuitry: in the first ("anti-phase inhibition model") inhibition is in phase opposition with excitation, but with a similar orientation tuning; in the second ("in-phase inhibition model"), inhibition is in phase with excitation, but with larger orientation tuning. Simulation results, performed by using bars with different length and motion direction, show that the models can explain velocity tuning, orientation tuning and direction selectivity of simple cells quite well with a suitable choice of intracortical synapses. The models can be used to test the hypothesis on the disposition of cortical synapses, and could provide practical tools in order to carry out a primary analysis of the movement detection of in...

Research paper thumbnail of Velocity selectivity and axial response to moving bar stimuli in the primary visual cortex: comparison of distinct intracortical inhibition rules

Research paper thumbnail of Binding and segmentation of visual images by means of oscillatory neurons

A neural network based on Wilson-Cowan oscillators is used to perform object recognition in a two... more A neural network based on Wilson-Cowan oscillators is used to perform object recognition in a two-dimensional visual scene. The temporal correlation among groups of oscillating neurons is used as the main criterion to solve the classic binding and segmentation problem. The network uses an original pattern of short-range lateral excitations among adjacent neurons to achieve the binding problem, and an external inhibitory global neuron to provide segmentation of multiple objects in the same visual scene. The latter may represent an "attention mechanism" from neurons at a higher hierarchical level. Simulations performed by using multiple idealized figures (up to 4-5) in the presence of noise suggest that the network can satisfactorily recognize objects in most cases. However, the threshold and time constant of the attention mechanism depend on the complexity (number of objects and level of noise) of the scene under examination. The present results may be useful to improve our understanding of how distributed activities are integrated in the neural system to form single object perceptions. In perspective, the proposed model may find applications in practical algorithms for object recognition.

Research paper thumbnail of A neural network model of contours extraction based on orientation selectivity in the primary visual cortex: Applications on real images

Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

ABSTRACT

Research paper thumbnail of Comparison of different models of orientation selectivity based on distinct intracortical inhibition rules

Vision Research, 2004

Aim of this work is to present simple models of orientation selectivity in the visual cortex, whi... more Aim of this work is to present simple models of orientation selectivity in the visual cortex, which do not require massive computational effort. Three different models are compared, in order to gain deeper insight into the structure of cortical circuits generating inhibitory signals. All models represent a single hypercolumn. They differ as to the arrangement of inhibitory connections: in the first (''antiphase inhibition model'') inhibition is in phase opposition with excitation, but with a similar orientation tuning; in the second (''in-phase inhibition model''), inhibition is in phase with excitation, but with larger orientation tuning. In these two models the orientation width of inhibition increases with contrast. Finally, a third model (''center-surround model'') assumes that inhibition comes from the same cells providing excitation, hence the inhibition tuning is contrast-independent. All models, with suitable values of the intracortical synapse parameters, are able to mimic experimental results in the literature. A few differences are evident between the ''center-surround model'' and the other two, especially as to the dependence of cortical cell response on spatial frequency. The models can represent practical tools to test hypotheses on the disposition of cortical synapses avoiding massive computational efforts.

Research paper thumbnail of A model of contextual interactions and contour detection in primary visual cortex

Neural Networks, 2004

A new model of contour extraction and perceptual grouping in the primary visual cortex is present... more A new model of contour extraction and perceptual grouping in the primary visual cortex is presented and discussed. It differs from previous models since it incorporates four main mechanisms, according to recent physiological data: a feed-forward input from the lateral geniculate nucleus, characterized by Gabor elongated receptive fields; an inhibitory feed-forward input, maximally oriented in the orthogonal direction of the target cell, which suppresses non-optimal stimuli and warrants contrast invariance; an excitatory cortical feedback, which respects co-axial and co-modularity criteria; and a long-range isotropic feedback inhibition. Model behavior has been tested on artificial images with contours of different curvatures, in the presence of considerable noise or in the presence of broken contours, and on a few real images. A sensitivity analysis has also been performed on the role of intracortical synapses. Results show that the model can extract correct contours within acceptable time from image presentation (30-40 ms). The feed-forward input plays a major role to set an initial correct bias for the subsequent feedback and to ensure contrast-invariance. Long-range inhibition is essential to suppress noise, but it may suppress small contours due to excessive competition with greater contours. Cortical excitation sharpens the initial bias and improves saliency of the contours. Model results support the idea that contour extraction is one the primary steps in the visual processing stream, and that local processing in V1 is able to solve this task even in difficult conditions, without the participation of higher visual centers.

Research paper thumbnail of Binding and segmentation of visual images by means of oscillatory neurons

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

A neural network based on Wilson-Cowan oscillators is used to perform object recognition in a two... more A neural network based on Wilson-Cowan oscillators is used to perform object recognition in a two-dimensional visual scene. The temporal correlation among groups of oscillating neurons is used as the main criterion to solve the classic binding and segmentation problem. The network uses an original pattern of short-range lateral excitations among adjacent neurons to achieve the binding problem, and an external inhibitory global neuron to provide segmentation of multiple objects in the same visual scene. The latter may represent an "attention mechanism" from neurons at a higher hierarchical level. Simulations performed by using multiple idealized figures (up to 4-5) in the presence of noise suggest that the network can satisfactorily recognize objects in most cases. However, the threshold and time constant of the attention mechanism depend on the complexity (number of objects and level of noise) of the scene under examination. The present results may be useful to improve our understanding of how distributed activities are integrated in the neural system to form single object perceptions. In perspective, the proposed model may find applications in practical algorithms for object recognition.

Research paper thumbnail of A model of contour extraction including multiple scales, flexible inhibition and attention

Neural Networks, 2008

A mathematical model of contextual integration and contour extraction in the primary visual corte... more A mathematical model of contextual integration and contour extraction in the primary visual cortex developed in a recent work [Ursino, M., & La Cara, G. E. (2004). A model of contextual interactions and contour detection in primary visual cortex. Neural Networks, 17, 719-735] has been significantly improved to include two fundamental additional aspects, i.e., multi-scale decomposition and attention. The model incorporates two independent paths for visual processing corresponding to two different scales. Attention from higher hierarchical levels works by modifying different properties of the network: by selecting the portion of the image to be scrutinized and the appropriate scale, by modulating the threshold of a gating mechanism, and by modifying the width and/or strength of lateral inhibition. Through computer simulations of real complex and noisy black-and-white images, we demonstrate that appropriate selection of the above factors allows accurate analysis of image contours at different levels, from global perception of the overall objects without details, down to a fine examination of minute particulars (such as the lips in a face or the fingers of a hand). Attentive reconfiguration of lateral inhibition plays a key role in the analysis of images at different detail levels.