Inhibitory synapses between striatal projection neurons support efficient enhancement of cortical signals: A computational model

The function of lateral inhibitory synapses between striatal projection neurons is currently poorly understood. This paper puts forward a model suggesting that inhibitory collaterals can be used to enhance the incoming cortical signals. In particular, we propose that lateral inhibition between projection neurons performs a signal-enhancing process that resembles the image processing technique of “unsharp masking”, where a blurred copy is used to enhance and sharpen an input image. The paper also presents the results of computer simulations deomsntrating that the proposed mechanisms is compatible with known properties of striatal projection neurons, and outperforms alternative models of lateral inhibition. Finally, this paper illustrates the advantages of the proposed model and discusses the relevance of these conclusions for existing computational models of the basal ganglia and their role in cognition.     

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.     

Molecular annotation of integrative feeding neural circuits

The identity of higher-order neurons and circuits playing an associative role to control feeding is unknown. We injected pseudorabies virus, a retrograde tracer, into masseter muscle, salivary gland, and tongue of BAC-transgenic mice expressing GFP in specific neural populations and identified several CNS regions that project multisynaptically to the periphery. MCH and orexin neurons were identified in the lateral hypothalamus, and Nurr1 and Cnr1 in the amygdala and insular/rhinal cortices. Cholera toxin β tracing showed that insular Nurr1(+) and Cnr1(+) neurons project to the amygdala or lateral hypothalamus, respectively. Finally, we show that cortical Cnr1(+) neurons show increased Cnr1 mRNA and c-Fos expression after fasting, consistent with a possible role for Cnr1(+) neurons in feeding. Overall, these studies define a general approach for identifying specific molecular markers for neurons in complex neural circuits. These markers now provide a means for functional studies of specific neuronal populations in feeding or other complex behaviors.     

Matching the modules: cortical maps and long-range intrinsic connections in visual cortex during development.

Visual cortical neurons exhibit a high degree of response selectivity and are grouped into small columns according to their response preferences. The columns are located at regularly spaced intervals covering the whole cortical representation of the visual field with a modular system of feature-selective neurons. The selectivity of these cells and their modular arrangement is thought to emerge from interactions in the network of specific intracortical and thalamocortical connections. Understanding the ontogenesis of this complex structure and contributions of intrinsic and extrinsic, experience-dependent mechanisms during cortical development can provide new insights into the way the visual cortex processes information about the environment. Available data about the development of connections and response properties in the visual cortex suggest that maturation proceeds in two distinct steps. In the first phase, mechanisms inherent to the cortex establish a crude framework of interconnected neural modules which exhibit the basic but still immature traits of the adult state. Relevant mechanisms in this phase are assumed to consist of molecular cues and patterns of spontaneous neural activity in cortical and corticothalamic interconnections. In a second phase, the primordial layout becomes refined under the control of visual experience establishing a fine-tuned network of connections and mature response properties.     

Axonal Dynamics of Excitatory and Inhibitory Neurons in Somatosensory Cortex

Cortical topography can be remapped as a consequence of sensory deprivation, suggesting that cortical circuits are continually modified by experience. To see the effect of altered sensory experience on specific components of cortical circuits, we imaged neurons, labeled with a genetically modified adeno-associated virus, in the intact mouse somatosensory cortex before and after whisker plucking. Following whisker plucking we observed massive and rapid reorganization of the axons of both excitatory and inhibitory neurons, accompanied by a transient increase in bouton density. For horizontally projecting axons of excitatory neurons there was a net increase in axonal projections from the non-deprived whisker barrel columns into the deprived barrel columns. The axon collaterals of inhibitory neurons located in the deprived whisker barrel columns retracted in the vicinity of their somata and sprouted long-range projections beyond their normal reach towards the non-deprived whisker barrel columns. These results suggest that alterations in the balance of excitation and inhibition in deprived and non-deprived barrel columns underlie the topographic remapping associated with sensory deprivation.     

Sustained oscillations generated by mutually inhibiting neurons with adaptation

Autonomic oscillatory activities exist in almost every living thing and most of them are produced by rhythmic activities of the corresponding neural systems (locomotion, respiration, heart beat, etc.). This paper mathematically discusses sustained oscillations generated by mutual inhibition of the neurons which are represented by a continuous-variable model with a kind of fatigue or adaptation effect. If the neural network has no stable stationary state for constant input stimuli, it will generate and sustain some oscillation for any initial state and for any disturbance. Some sufficient conditions for that are given to three types of neural networks: lateral inhibition networks of linearly arrayed neurons, symmetric inhibition networks and cyclic inhibition networks. The result suggests that the adaptation of the neurons plays a very important role for the appearance of the oscillations. Some computer simulations of rhythic activities are also presented for cyclic inhibition networks consisting of a few neurons.     

A phenomenological theory of spatially structured local synaptic connectivity

The structure of local synaptic circuits is the key to understanding cortical function and how neuronal functional modules such as cortical columns are formed. The central problem in deciphering cortical microcircuits is the quantification of synaptic connectivity between neuron pairs. I present a theoretical model that accounts for the axon and dendrite morphologies of pre- and postsynaptic cells and provides the average number of synaptic contacts formed between them as a function of their relative locations in three-dimensional space. An important aspect of the current approach is the representation of a complex structure of an axonal/dendritic arbor as a superposition of basic structures—synaptic clouds. Each cloud has three structural parameters that can be directly estimated from two-dimensional drawings of the underlying arbor. Using empirical data available in literature, I applied this theory to three morphologically different types of cell pairs. I found that, within a wide range of cell separations, the theory is in very good agreement with empirical data on (i) axonal–dendritic contacts of pyramidal cells and (ii) somatic synapses formed by the axons of inhibitory interneurons. Since for many types of neurons plane arborization drawings are available from literature, this theory can provide a practical means for quantitatively deriving local synaptic circuits based on the actual observed densities of specific types of neurons and their morphologies. It can also have significant implications for computational models of cortical networks by making it possible to wire up simulated neural networks in a realistic fashion.     

Top-Down Inputs Enhance Orientation Selectivity in Neurons of the Primary Visual Cortex during Perceptual Learning

Perceptual learning has been used to probe the mechanisms of cortical plasticity in the adult brain. Feedback projections are ubiquitous in the cortex, but little is known about their role in cortical plasticity. Here we explore the hypothesis that learning visual orientation discrimination involves learning-dependent plasticity of top-down feedback inputs from higher cortical areas, serving a different function from plasticity due to changes in recurrent connections within a cortical area. In a Hodgkin-Huxley-based spiking neural network model of visual cortex, we show that modulation of feedback inputs to V1 from higher cortical areas results in shunting inhibition in V1 neurons, which changes the response properties of V1 neurons. The orientation selectivity of V1 neurons is enhanced without changing orientation preference, preserving the topographic organizations in V1. These results provide new insights to the mechanisms of plasticity in the adult brain, reconciling apparently inconsistent experiments and providing a new hypothesis for a functional role of the feedback connections.     

A zonal model of cortical functions

A model of cortical functions is developed with the object of simulating the observed behavior of individual neurons organized in unit circuits and functional systems of the cerebellum, the cerebrum and the hippocampal formation. The neuronal model is capable of representing refractory and potentiated states, as well as the firing and lowest resting states. The unit circuits of each system consist of all common types of cells with known synaptic connections. In the cerebral system these unit circuits are interconnected to form columns as well as zones. A new discrete neural network equation, which takes account of interactions with the extracellular field, is proposed to simulate electrical activity in these circuits. A coherent theory of cortical activity and functions is derived that accounts for many of the observed phenomena, including those associated with the development of long-term potentiation and sequential memory. Three appendices are devoted to the theory of extracellular interactions, the derivation of non-linear network equations, and a computer program to simulate learning in the cortex.     

Feedforward Inhibition and Synaptic Scaling – Two Sides of the Same Coin?

Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.     

Reelin sets the pace of neocortical neurogenesis

Migration of neurons during cortical development is often assumed to rely on purely post-proliferative reelin signaling. However, Notch signaling, long known to regulate neural precursor formation and maintenance, is required for the effects of reelin on neuronal migration. Here, we show that reelin gain-of-function causes a higher expression of Notch target genes in radial glia and accelerates the production of both neurons and intermediate progenitor cells. Converse alterations correlate with reelin loss-of-function, consistent with reelin controlling Notch signaling during neurogenesis. Ectopic expression of reelin in isolated clones of progenitors causes a severe reduction in neuronal differentiation. In mosaic cell cultures, reelin-primed progenitor cells respond to wild-type cells by further decreasing neuronal differentiation, consistent with an increased sensitivity to lateral inhibition. These results indicate that reelin and Notch signaling cooperate to set the pace of neocortical neurogenesis, a prerequisite for proper neuronal migration and cortical layering.     

Modeling perceptual learning with multiple interacting elements: A neural network model describing early visual perceptual learning

We introduce a neural network model of an early visual cortical area, in order to understand better results of psychophysical experiments concerning perceptual learning during odd element (pop-out) detection tasks (Ahissar and Hochstein, 1993, 1994a).The model describes a network, composed of orientation selective units, arranged in a hypercolumn structure, with receptive field properties modeled from real monkey neurons. Odd element detection is a final pattern of activity with one (or a few) salient units active. The learning algorithm used was the Associative reward-penalty (Ar-p) algorithm of reinforcement learning (Barto and Anandan, 1985), following physiological data indicating the role of supervision in cortical plasticity.Simulations show that network performance improves dramatically as the weights of inter-unit connections reach a balance between lateral iso-orientation inhibition, and facilitation from neighboring neurons with different preferred orientations. The network is able to learn even from chance performance, and in the presence of a large amount of noise in the response function. As additional tests of the model, we conducted experiments with human subjects in order to examine learning strategy and test model predictions.     

Temporally regulated asymmetric neurogenesis causes left-right difference in the zebrafish habenular structures

The habenular neurons on both sides of the zebrafish diencephalon show an asymmetric (laterotopic) axonal projection pattern into the interpeduncular nucleus. We previously revealed that the habenula could be subdivided into medial and lateral subnuclei, and a prominent left-right difference in the size ratio of these subnuclei accounts for the asymmetry in its neural connectivity. In the present study, birth date analysis showed that neural precursors for the lateral subnuclei were born at earlier stages than those for the medial subnuclei. More neurons for the early-born lateral subnuclei were generated on the left side, while more neurons for the late-born medial subnuclei were generated on the right side. Genetic hyperactivation and repression of Notch signaling revealed that differential timing determines both specificity and asymmetry in the neurogenesis of neural precursors for the habenular subnuclei.     

Mesoscopic Segregation of Excitation and Inhibition in a Brain Network Model

Neurons in the brain are known to operate under a careful balance of excitation and inhibition, which maintains neural microcircuits within the proper operational range. How this balance is played out at the mesoscopic level of neuronal populations is, however, less clear. In order to address this issue, here we use a coupled neural mass model to study computationally the dynamics of a network of cortical macrocolumns operating in a partially synchronized, irregular regime. The topology of the network is heterogeneous, with a few of the nodes acting as connector hubs while the rest are relatively poorly connected. Our results show that in this type of mesoscopic network excitation and inhibition spontaneously segregate, with some columns acting mainly in an excitatory manner while some others have predominantly an inhibitory effect on their neighbors. We characterize the conditions under which this segregation arises, and relate the character of the different columns with their topological role within the network. In particular, we show that the connector hubs are preferentially inhibitory, the more so the larger the node''s connectivity. These results suggest a potential mesoscale organization of the excitation-inhibition balance in brain networks.     

System principles in the organization of neurons carrying out cortical inhibition

Some new data on neuronal and synaptic organization of sensorimotor cortical area in cat are obtained by a complex of morphological and electrophysiological methods. These data permit considering that direct afferent inhibition is ensured by thalamo-cortical neurons and neurons forming the callosal and association links. The recurrent and lateral inhibition are structurally realized through the ascending recurrent axon collaterals of pyramidal neurons forming links either with short-axon or with long-axon interneurons. Cortico-thalamic (cortico-fugal) inhibition may be performed either via descending cortico-thalamic neurons or via cortico-cortical ipsi- and contralateral neurons. The above mentioned neuronal chains may be considered as structural elements of more complex neuronal sets which ensure the inhibition at the cortical inputs, outputs and intracortically.     

Dissociating ocular dominance column development and ocular dominance plasticity: a neurotrophic model

 Recent experimental data indicate that both neurotrophic factors (NTFs) and intracortical inhibitory circuitry are implicated in the development and plasticity of ocular dominance columns. We extend a neurotrophic model of developmental synaptic plasticity, which previously failed to account correctly for the differences between monocular deprivation and binocular deprivation, and show that the inclusion of lateral cortical inhibition is indeed necessary in understanding the effects of visual deprivation in the model. In particular, we argue that monocular deprivation causes a differential shift in the balance between inhibition and excitation in cortical columns, down-regulating NTFs in deprived-eye columns and up-regulating NTFs in undeprived-eye columns; during binocular deprivation, however, no such shift occurs. We thus postulate that the response to visual deprivation is at the level of the cortical circuit, while the mechanisms of afferent segregation are at the molecular or cellular level. Such a dissociation is supported by recent experimental work challenging the assumption that columnar organisation develops in an activity-dependent, competitive fashion. Our extended model also questions recent attempts to distinguish between heterosynaptic and homosynaptic models of synaptic plasticity. Received: 17 April 2001 / Accepted in revised form: 7 November 2001     

Bayesian inference in populations of cortical neurons: a model of motion integration and segmentation in area MT

A major issue in cortical physiology and computational neuroscience is understanding the interaction between extrinsic signals from feedforward connections and intracortical signals from lateral connections. We propose here a computational model for motion perception based on the assumption that the local cortical circuits in the medio-temporal area (area MT) implement a Bayesian inference principle. This approach establishes a functional balance between feedforward and lateral, excitatory and inhibitory, inputs. The model reproduces most of the known properties of the neurons in area MT in response to moving stimuli. It accounts for important motion perception phenomena including motion transparency, spatial and temporal integration/segmentation. While integrating several properties of previously proposed models, it makes specific testable predictions concerning, in particular, temporal properties of neurons and the architecture of lateral connections in area MT. In addition, the proposed mechanism is consistent with the known properties of local cortical circuits in area V1. This suggests that Bayesian inference may be a general feature of information processing in cortical neuron populations. Received: 3 December 1997 / Accepted in revised form: 21 July 1998     

Electric responses of the cerebral cortex to central excitation and inhibition

An attempt was made to evaluate critically the extent to which the background electrocorticogram, neuronal impulse activity, and evoked potentials reflect the state of cortical excitation and inhibition. It was shown that during electrocorticogram desynchronization, firing neurons predominated in the surface (mainly afferent) layers, while inhibited neurons were in the majority in the lower layers of the cortex. Consequently, desynchronization does not reflect diffuse excitation of the cortex and cannot be taken as an index of central excitation. Slow electrocortical waves cannot be used as indicators of an inhibitory state, even though they may be associated with processes leading to the development of inhibition. Under the effects of different stimuli, the number of neurons participating in impulse condition, and the number of neurons temporarily inhibiting impulse activity in the projection cortical area were stable (ratio 2:1). It was found that the correlation between impulse discharges of neuronal pairs increases during both central excitation and central inhibition. Nonetheless, differences between cortical excitation and inhibition were seen in the reorganization of neuronal columns. The use of evoked potentials to determine cortical excitation or inhibition is complicated by the fact that the amplitude of evoked-potential components reflects the divergent influences of many factors. It was shown that conditional excitation diminished the evoked potential to a light stimulus in the projection cortical area, but caused it to increase in the region of the motor analyzer. The elaboration of a conditional inhibition (extinction) is accompanied by the growth of an evoked potential to a stimulus in the primary cortical area, and by its repression in the region of the motor analyzer. In this case, a large delayed negative wave appears in the evoked potential.This report was presented at the All-Union Symposium on Electric Responses of the Cerebral Cortex to Afferent Stimuli, Kiev, October, 1969.Rostov-on-Don State University. Translated from Neirofiziologiya, Vol. 2, No. 2, pp. 140–154, March–April, 1970.     

Neural mechanisms in disorders of movement

1. Experimental models of ballism, chorea and Parkinson's disease have been developed in the primate, and the underlying neural mechanisms which mediate these disorders of movement have been investigated using the 2-deoxyglucose uptake technique. 2. In ballism, the subthalamic nucleus is either lesioned or underactive. Because of the excitatory nature of subthalamic efferent fibres, this leads to abnormal underactivity of neurons in the medical segment of the globus pallidus which project to the ventral anterior and ventral lateral nuclei of the thalamus, and to the pedunculopontine nucleus of the caudal midbrain. 3. In chorea, there is underactivity of GABAergic striatal (putaminal) neurons which project to the lateral segment of the globus pallidus. This leads to overacting of lateral pallidal neurons and, thus, physiological inhibition of the subthalamic nucleus. Common neural mechanisms, therefore, underlie the appearance of dyskinesia in ballism and chorea. 4. In parkinsonism, there is overactivity of putaminal neurons projecting to the lateral pallidal segment. This results in excessive inhibition of lateral pallidal neurons and, as a consequence, disinhibition of the subthalamic nucleus. Overactivity of the subthalamic nucleus provides excessive drive upon medial pallidal neurons projecting to thalamic and pedunculopontine nuclei.