Computational Maps in the Visual Cortex

Miikkulainen, Risto; Bednar, James A.; Choe, Yoonsuck; Sirosh, Joseph

doi:10.1007/0-387-28806-6

Cited by 50 publications

(6 citation statements)

References 477 publications

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“…Neural dynamics in the model is captured by the Laterally Interconnected Synergistically Self-Organizing Map (LISSOM) model which is a simple yet biologically plausible model to simulate cortical neural activity (Miikkulainen et al, 2005). The LISSOM model basically does feature extraction similar to the principal component analysis, thereby capable of forming topographic maps.…”

Section: Introductionmentioning

confidence: 99%

A Network Architecture for Bidirectional Neurovascular Coupling in Rat Whisker Barrel Cortex

Kumar

Khot

Chakravarthy

et al. 2021

Front. Comput. Neurosci.

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Neurovascular coupling is typically considered as a master-slave relationship between the neurons and the cerebral vessels: the neurons demand energy which the vessels supply in the form of glucose and oxygen. In the recent past, both theoretical and experimental studies have suggested that the neurovascular coupling is a bidirectional system, a loop that includes a feedback signal from the vessels influencing neural firing and plasticity. An integrated model of bidirectionally connected neural network and the vascular network is hence required to understand the relationship between the informational and metabolic aspects of neural dynamics. In this study, we present a computational model of the bidirectional neurovascular system in the whisker barrel cortex and study the effect of such coupling on neural activity and plasticity as manifest in the whisker barrel map formation. In this model, a biologically plausible self-organizing network model of rate coded, dynamic neurons is nourished by a network of vessels modeled using the biophysical properties of blood vessels. The neural layer which is designed to simulate the whisker barrel cortex of rat transmits vasodilatory signals to the vessels. The feedback from the vessels is in the form of available oxygen for oxidative metabolism whose end result is the adenosine triphosphate (ATP) necessary to fuel neural firing. The model captures the effect of the feedback from the vascular network on the neuronal map formation in the whisker barrel model under normal and pathological (Hypoxia and Hypoxia-Ischemia) conditions.

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Section: Introductionmentioning

confidence: 99%

A Network Architecture for Bidirectional Neurovascular Coupling in Rat Whisker Barrel Cortex

Kumar

Khot

Chakravarthy

et al. 2021

Front. Comput. Neurosci.

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“…Earlier models of direction selectivity (Miikkulainen et al, 2006) and pattern selectivity by Rust et al (2006) achieved motion sensitivity by either of two scenarios: (i) by giving the entire sequence as a stack of frames at a single time step, or (ii) a part of the stimulus is presented to the network via lagged cells. By contrast, the model proposed here has only access to the current frame.…”

Section: Discussionmentioning

confidence: 99%

“…In such a system all weights saturate at maximum value. To make learning rule more competitive and useful divisive normalization was proposed (Miikkulainen et al, 2006) where each weight is intended to scale down in proportion to its original value. They also stated that initially normalization terms were introduced for a computational reason (Rochester et al, 1956) but many works (Turrigiano, 1999) has uncovered a number of neural regulatory mechanisms within the cell that regulate the overall synaptic strength during adaptation.…”

Section: Model Architecturementioning

confidence: 99%

“…The Heeger model is non-linear and simulated the moving stimulus-response as the sum of the responses to a set of sequential stimuli evenly spaced in time, with an explicit time variable. Others showed that activity-dependent self-organization results in direction selectivity (Shouno and Kurata, 2001; Miikkulainen et al, 2006). Miikkulainen et al used intra-cortical circuitry to incorporate excitatory and inhibitory effects along with LGN lagged cells to achieve direction selectivity (Miikkulainen et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Some models (Somers et al, 1995) focus on explaining a single functional property like orientation selectivity or direction selectivity, therefore accounting only for a subset of visual neural behaviors. The models proposed by Miikkulainen et al (2006) attempt to explain diverse properties such as orientation selectivity, direction selectivity and ocular dominance of neuronal population in the Primary visual cortex which is the first stage in the motion pathway. Bichler et al proposed an interesting 2 layer feedforward fully connected neural network model that can learn temporally correlated features directly from vision sensor data using biologically plausible unsupervised STDP learning scheme (Bichler et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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A Model of Motion Processing in the Visual Cortex Using Neural Field With Asymmetric Hebbian Learning

2019

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Neurons in the dorsal pathway of the visual cortex are thought to be involved in motion processing. The first site of motion processing is the primary visual cortex (V1), encoding the direction of motion in local receptive fields, with higher order motion processing happening in the middle temporal area (MT). Complex motion properties like optic flow are processed in higher cortical areas of the Medial Superior Temporal area (MST). In this study, a hierarchical neural field network model of motion processing is presented. The model architecture has an input layer followed by either one or cascade of two neural fields (NF): the first of these, NF1, represents V1, while the second, NF2, represents MT. A special feature of the model is that lateral connections used in the neural fields are trained by asymmetric Hebbian learning, imparting to the neural field the ability to process sequential information in motion stimuli. The model was trained using various traditional moving patterns such as bars, squares, gratings, plaids, and random dot stimulus. In the case of bar stimuli, the model had only a single NF, the neurons of which developed a direction map of the moving bar stimuli. Training a network with two NFs on moving square and moving plaids stimuli, we show that, while the neurons in NF1 respond to the direction of the component (such as gratings and edges) motion, the neurons in NF2 (analogous to MT) responding to the direction of the pattern (plaids, square object) motion. In the third study, a network with 2 NFs was simulated using random dot stimuli (RDS) with translational motion, and show that the NF2 neurons can encode the direction of the concurrent dot motion (also called translational flow motion), independent of the dot configuration. This translational RDS flow motion is decoded by a simple perceptron network (a layer above NF2) with an accuracy of 100% on train set and 90% on the test set, thereby demonstrating that the proposed network can generalize to new dot configurations. Also, the response properties of the model on different input stimuli closely resembled many of the known features of the neurons found in electrophysiological studies.

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Phase and Amplitude Modulation in a Neural Oscillatory Model of the Orientation Map

Kumar

Kori

Elango

et al. 2018

Neural Information Processing

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Computational Maps in the Visual Cortex

Cited by 50 publications

References 477 publications

A Network Architecture for Bidirectional Neurovascular Coupling in Rat Whisker Barrel Cortex

A Network Architecture for Bidirectional Neurovascular Coupling in Rat Whisker Barrel Cortex

A Model of Motion Processing in the Visual Cortex Using Neural Field With Asymmetric Hebbian Learning

Phase and Amplitude Modulation in a Neural Oscillatory Model of the Orientation Map

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