Emergence of orientation-selective inhibition in the primary visual cortex: a Bayes–Markov computational model

The recent consensus is that virtually all aspects of response selectivity exhibited by the primary visual cortex are either created or sharpened by cortical inhibitory interneurons. Experimental studies have shown that there are cortical inhibitory cells that are driven by geniculate cells and that, like their cortical excitatory counterparts, are orientation selective, though less sharply tuned. The main goal of this article is to demonstrate how orientation-selective inhibition might be created by the circuitry of the primary visual cortex (striate cortex, V1) from its nonoriented geniculate inputs. To fulfill this goal, first, a Bayes–Markov computational model is developed for the V1 area dedicated to foveal vision. The developed model consists of three parts: (i) a two-layered hierarchical Markov random field that is assumed to generate the activity patterns of the geniculate and cortical inhibitory cells, (ii) a Bayesian computational goal that is formulated based on the maximum a posteriori (MAP) estimation principle, and (iii) an iterative, deterministic, parallel algorithm that leads the cortical circuitry to achieve its assigned computational goal. The developed model is not fully LGN driven and it is not implementable by the neural machinery of V1. The model, then, is transformed into a fully LGN-driven and physiologically plausible form. Computer simulation is used to demonstrate the performance of the developed models.     

A Feedback Model of Attention and Context Dependence in Visual Cortical Networks

We have modeled biologically realistic neural networks that may be involved in contextual modulation of stimulus responses, as reported in the neurophysiological experiments of Motter (1994a, 1994b) (Journal of Neuroscience, 14:2179–2189 and 2190–2199). The networks of our model are structured hierarchically with feedforward, feedback, and lateral connections, totaling several thousand cells and about 300,000 synapses. The contextual modulation, arising from attention cues, is explicitly modeled as a feedback signal coming from the highest-order cortical network. The feedback signal arises from mutually inhibitory neurons with different stimulus preferences. Although our model is probably the simplest one consistent with available anatomical and physiological evidence and ignores the complexities that may exist in high-level cortical networks such as the prefrontal cortex, it reproduces the experimental results quite well and offers some guidance for future experiments. We also report the unexpected observation of 40 Hz oscillations in the model.     

Anisotropic connectivity and cooperative phenomena as a basis for orientation sensitivity in the visual cortex

A computer simulation model of the neural circuitry underlying orientation sensitivity in cortical neurons is examined. The model consists of a network of 3000 neurons divided into two functionally distinct cell types: excitatory (E-cells) and inhibitory (I-cells). We demonstrate that both orientation sensitivity and shape selectivity can be accounted for by making the following assumptions: 1) thalamic afferents to a sheet of cortical neurons are retionotopically organized; 2) thalamic afferents come from a single neuron, or at most a few neurons, in the lateral geniculate nucleus; 3) cortical activity is cooperative, i.e. largely dependent on intracortical connections, some of which have anisotropies along directions parallel to the pial surface. Anisotropies are specified only by the distribution of cells which are postsynaptic to a particular neuron, without specifying the axonal or dendritic contributions. In this paper, orientation sensitivity arises through cooperative interactions among neurons having anisotropic excitatory, and isotropic inhibitory connections.     

A ring model for spatiotemporal properties of simple cells in the visual cortex

 A neural model is proposed for the spatiotemporal properties of simple cells in the visual cortex. In the model, several cortical cells are arranged on a ring, with mutual excitatory or inhibitory connections. The cells also receive excitatory inputs either from lagged and nonlagged cells of the lateral geniculate nucleus in one setting or from nonlagged cells in the other. Computer simulation shows that the cortical cells have spatiotemporally inseparable receptive fields in the former setting and separable fields in the latter; spatial profiles at a given time in the spatiotemporal fields are described with a Gabor function whose phase parameter varies regularly from 0 to 2π with rotation along the ring; the inseparable cells have directional selectivity as observed physiologically. Received: 13 November 1995 / Accepted in revised form: 1 July 1997     

Spontaneous and evoked activities of interpositus nucleus neurons before and after lesion of the cerebellar cortex

Spontaneous and evoked activities of nucleus interpositus neurons (IN) of the cerebellum were examined before and after cerebellar paravermal cortex lesions in cats anesthetized with alpha-chloralose. It was found that spontaneous activity increased dramatically following cortical ablation: before the lesion only 4% of cells encountered fired at a rate exceeding 80 impulses/sec., whereas up to 40% discharged at this rate postoperatively. Responses to paw stimulation were also altered: the initial excitation was lengthened from 8.5 to 15.8 msec; narrow; trough causing segmentation in this excitation, which seems to result from Purkinje cell inhibition, was absent; and the succeeding inhibitory period was reduced in duration by 50%. Also after the lesion there was a strong tendency for the neurons to discharge in bursts. It is suggested that changes in cell activity in the IN following cortical lesion unveil neural mechanisms of motor disturbances in lesioned cats.     

Estimation of neurophysiological parameters from the waking EEG using a biophysical model of brain dynamics

This paper presents the results from using electroencephalographic (EEG) data to estimate the values of key neurophysiological parameters using a detailed biophysical model of brain activity. The model incorporates spatial and temporal aspects of cortical function including axonal transmission delays, synapto-dendritic rates, range-dependent connectivities, excitatory and inhibitory neural populations, and intrathalamic, intracortical, corticocortical and corticothalamic pathways. Parameter estimates were obtained by fitting the model's theoretical spectrum to EEG spectra from each of 100 healthy human subjects. Statistical analysis was used to infer significant parameter variations occurring between eyes-closed and eyes-open states, and a correlation matrix was used to investigate links between the parameter variations and traditional measures of quantitative EEG (qEEG). Accurate fits to all experimental spectra were observed, and both inter-subject and between-state variability were accounted for by the variance in the fitted biophysical parameters, which were in turn consistent with known independent experimental and theoretical estimates. These values thus provide physiological information regarding the state. transitions (eyes-closed vs. eyes-open) and phenomena including cortical idling and alpha desynchronization. The parameters are also consistent with traditional qEEG, but are more informative, since they provide links to underlying physiological processes. To our knowledge, this is the first study where a detailed biophysical model of the brain is used to estimate neurophysiological parameters underlying the transitions in a broad range (0.25-50 Hz) of EEG spectra obtained from a large set of human data.     

Dietary Restriction Affects Neuronal Response Property and GABA Synthesis in the Primary Visual Cortex

Previous studies have reported inconsistent effects of dietary restriction (DR) on cortical inhibition. To clarify this issue, we examined the response properties of neurons in the primary visual cortex (V1) of DR and control groups of cats using in vivo extracellular single-unit recording techniques, and assessed the synthesis of inhibitory neurotransmitter GABA in the V1 of cats from both groups using immunohistochemical and Western blot techniques. Our results showed that the response of V1 neurons to visual stimuli was significantly modified by DR, as indicated by an enhanced selectivity for stimulus orientations and motion directions, decreased visually-evoked response, lowered spontaneous activity and increased signal-to-noise ratio in DR cats relative to control cats. Further, it was shown that, accompanied with these changes of neuronal responsiveness, GABA immunoreactivity and the expression of a key GABA-synthesizing enzyme GAD67 in the V1 were significantly increased by DR. These results demonstrate that DR may retard brain aging by increasing the intracortical inhibition effect and improve the function of visual cortical neurons in visual information processing. This DR-induced elevation of cortical inhibition may favor the brain in modulating energy expenditure based on food availability.     

A self-organizing neural network model for the development of complex cells

On the basis of recent neurophysiological findings on the mammalian visual cortex, a selforganizing neural network model is proposed for the understanding of the development of complex cells. The model is composed of two kinds of connections from LGN cells to a complex cell. One is direct excitatory connections and the other is indirect inhibitory connections via simple cells. Inhibitory synapses between simple cells and complex cells are assumed to be modifiable. The model was simulated on a computer to confirm its behavior.     

A neural network model for visual motion detection that can explain psychophysical and neurophysiological phenomena

This paper proposes a new neural network model for visual motion detection. The model can well explain both psychophysical findings (the changes of displacement thresholds with stimulus velocity and the perception of apparent motion) and neurophysiological findings (the selectivity for the direction and the velocity of a moving stimulus). To confirm the behavior of the model, numerical examinations were conducted. The results were consistent with both psychophysical and neurophysiological findings.     

Evoked impulse activity in neuronally isolated cortex

The reactions of neurons of the isolated cortex of one hemisphere to direct cortical stimulation were investigated in cats under Nembutal anesthesia. Isolation of the cortex was carried out by Khananashvili's method [10]. It is shown that phasic reactions develop in the isolated cortex in response to such stimulation: initial discharge, initial pause, first after-discharge, first after-pause, late after-discharges and pauses, as well as reactions of presumably inhibitory neurons. A majority of the cells (85%) which manifest background activity respond to direct electrical stimulation, and the frequency of the late after-reactions is twice as great as in the intact cortex. It is concluded that cortical elements of the isolated cortex retain their principal neurophysiological properties.Institute of Experimental Medicine, Academy of Medical Sciences of the USSR, Leningrad. Translated from Neirofiziologiya, Vol. 3, No. 3, pp. 236–244, May–June, 1971.     

Early Cortical Orientation Selectivity: How Fast Inhibition Decodes the Order of Spike Latencies

Following a flashed stimulus, I show that a simple neurophysiological mechanism in the primary visual system can generate orientation selectivity based on the first incoming spikes. A biological model of the lateral geniculate nucleus generates an asynchronous wave of spikes, with the most strongly activated neurons firing first. Geniculate activation leads to both the direct excitation of a cortical pyramidal cell and disynaptic feed-forward inhibition. The mechanism provides automatic gain control, so the cortical neurons respond over a wide range of stimulus contrasts. It also demonstrates the biological plausibility of a new computationally efficient neural code: latency rank order coding.     

Neuronal selectivity and local map structure in visual cortex

The organization of primary visual cortex (V1) into functional maps makes individual cells operate in a variety of contexts. For instance, some neurons lie in regions of fairly homogeneous orientation preference (iso-orientation domains), while others lie in regions with a variety of preferences (e.g., pinwheel centers). We asked whether this diversity in local map structure correlates with the degree of selectivity of spike responses. We used a combination of imaging and electrophysiology to reveal that neurons in regions of homogeneous orientation preference have much sharper tuning. Moreover, in both monkeys and cats, a common principle links the structure of the orientation map, on the spatial scale of dendritic integration, to the degree of selectivity of individual cells. We conclude that neural computation is not invariant across the cortical surface. This finding must factor into future theories of receptive field wiring and map development.     

Neural mechanisms for the recognition of biological movements

The visual recognition of complex movements and actions is crucial for the survival of many species. It is important not only for communication and recognition at a distance, but also for the learning of complex motor actions by imitation. Movement recognition has been studied in psychophysical, neurophysiological and imaging experiments, and several cortical areas involved in it have been identified. We use a neurophysiologically plausible and quantitative model as a tool for organizing and making sense of the experimental data, despite their growing size and complexity. We review the main experimental findings and discuss possible neural mechanisms, and show that a learning-based, feedforward model provides a neurophysiologically plausible and consistent summary of many key experimental results.     

Population based models of cortical drug response: insights from anaesthesia

A great explanatory gap lies between the molecular pharmacology of psychoactive agents and the neurophysiological changes they induce, as recorded by neuroimaging modalities. Causally relating the cellular actions of psychoactive compounds to their influence on population activity is experimentally challenging. Recent developments in the dynamical modelling of neural tissue have attempted to span this explanatory gap between microscopic targets and their macroscopic neurophysiological effects via a range of biologically plausible dynamical models of cortical tissue. Such theoretical models allow exploration of neural dynamics, in particular their modification by drug action. The ability to theoretically bridge scales is due to a biologically plausible averaging of cortical tissue properties. In the resulting macroscopic neural field, individual neurons need not be explicitly represented (as in neural networks). The following paper aims to provide a non-technical introduction to the mean field population modelling of drug action and its recent successes in modelling anaesthesia.     

Experimental-neuromodeling framework for understanding auditory object processing: integrating data across multiple scales.

In this article, we review a combined experimental-neuromodeling framework for understanding brain function with a specific application to auditory object processing. Within this framework, a model is constructed using the best available experimental data and is used to make predictions. The predictions are verified by conducting specific or directed experiments and the resulting data are matched with the simulated data. The model is refined or tested on new data and generates new predictions. The predictions in turn lead to better-focused experiments. The auditory object processing model was constructed using available neurophysiological and neuroanatomical data from mammalian studies of auditory object processing in the cortex. Auditory objects are brief sounds such as syllables, words, melodic fragments, etc. The model can simultaneously simulate neuronal activity at a columnar level and neuroimaging activity at a systems level while processing frequency-modulated tones in a delayed-match-to-sample task. The simulated neuroimaging activity was quantitatively matched with neuroimaging data obtained from experiments; both the simulations and the experiments used similar tasks, sounds, and other experimental parameters. We then used the model to investigate the neural bases of the auditory continuity illusion, a type of perceptual grouping phenomenon, without changing any of its parameters. Perceptual grouping enables the auditory system to integrate brief, disparate sounds into cohesive perceptual units. The neural mechanisms underlying auditory continuity illusion have not been studied extensively with conventional neuroimaging or electrophysiological techniques. Our modeling results agree with behavioral studies in humans and an electrophysiological study in cats. The results predict a particular set of bottom-up cortical processing mechanisms that implement perceptual grouping, and also attest to the robustness of our model.     

Neurophysiological response selectivity for conspecific songs over synthetic sounds in the auditory forebrain of non-singing female songbirds

Female choice plays a critical role in the evolution of male acoustic displays. Yet there is limited information on the neurophysiological basis of female songbirds’ auditory recognition systems. To understand the neural mechanisms of how non-singing female songbirds perceive behaviorally relevant vocalizations, we recorded responses of single neurons to acoustic stimuli in two auditory forebrain regions, the caudal lateral mesopallium (CLM) and Field L, in anesthetized adult female zebra finches (Taeniopygia guttata). Using various metrics of response selectivity, we found consistently higher response strengths for unfamiliar conspecific songs compared to tone pips and white noise in Field L but not in CLM. We also found that neurons in the left auditory forebrain had lower response strengths to synthetics sounds, leading to overall higher neural selectivity for song in neurons of the left hemisphere. This laterality effect is consistent with previously published behavioral data in zebra finches. Overall, our results from Field L are in parallel and from CLM are in contrast with the patterns of response selectivity reported for conspecific songs over synthetic sounds in male zebra finches, suggesting some degree of sexual dimorphism of auditory perception mechanisms in songbirds.     

Cortico-thalamic interplay and the security of operation of neural assemblies and temporal chains in the cerebral cortex

 Much evidence suggests that the mammalian thalamus is not merely a set of nuclei relaying signals to the cerebral cortex, but is engaged in two-way interplay with it. Three important features constrain ideas about the nature of this interplay: (i) thalamic projection neurones lack local axon collaterals; (ii) most cortico-thalamic projections have very long axonal conduction time; (iii) in the waking state the membrane potential of thalamic projections cells appears to be poised just beneath threshold for firing. It is proposed that cortico-thalamo-cortical pathways represent connections between different cortical loci which have higher security than the direct cortico-cortical route. Thus each thalamo-cortical projection neurone can have a singular and pivotal role in the activation of one or more cortical neural assemblies. The long delays of cortico-thalamic conduction suggest that the cortico-thalamo-cortical loop also plays a crucial role in the operation of time-structured neural assemblies (‘synfire chains’: Abeles), by providing a high-security link from one active node of the chain to nodes activated at a later time in the sequence. It is predicted that, in the waking animal, thalamic projection cells should have a response selectivity to complex percepts and concepts, conferred on them by the cortical assemblies in whose activation they participate. Received: 30 November 1995/Accepted: 3 June 1996     

Voice cells in the primate temporal lobe

Communication signals are important for social interactions and survival and are thought to receive specialized processing in the visual and auditory systems. Whereas the neural processing of faces by face clusters and face cells has been repeatedly studied [1-5], less is known about the neural representation of voice content. Recent functional magnetic resonance imaging (fMRI) studies have localized voice-preferring regions in the primate temporal lobe [6, 7], but the hemodynamic response cannot directly assess neurophysiological properties. We investigated the responses of neurons in an fMRI-identified voice cluster in awake monkeys, and here we provide the first systematic evidence for voice cells. "Voice cells" were identified, in analogy to "face cells," as neurons responding at least 2-fold stronger to conspecific voices than to "nonvoice" sounds or heterospecific voices. Importantly, whereas face clusters are thought to contain high proportions of face cells [4] responding broadly to many faces [1, 2, 4, 5, 8-10], we found that voice clusters contain moderate proportions of voice cells. Furthermore, individual voice cells exhibit high stimulus selectivity. The results reveal the neurophysiological bases for fMRI-defined voice clusters in the primate brain and highlight potential differences in how the auditory and?visual systems generate selective representations of communication signals.     

A model of the complex cell based on recent neurophysiological findings

A neural network model is proposed for the understanding of the receptive field properties of the complex cell. The model is based on recent neurophysiological findings on the visual cortical network. The model is proved to be functionally identical with Hubel's and Wiesel's hierarchy model though the two models are structurally quite different.