Symmetric Synaptic Connections Research Articles

Endomorphin-2 Decreases Excitatory Synaptic Transmission in the Spinal Ventral Horn of the Rat.

Motor impairment is one of the serious side-effects of morphine, which is an exogenous agonist of the μ-opioid receptor (MOR) as well as a widely used analgesic drug in clinical practice for chronic pain treatment. Endomorphins (EMs, including EM-1 and EM-2), the most effective and specific endogenous agonists of the MOR, exert more potent analgesia in acute and neuropathic pain than other opiates, such as morphine. Although EMs had fewer side-effects comparing to other opiates, motor impairment was still one unwanted reaction which limited its clinical application. In order to prevent and treat the motor impairment, it is critical to reveal the neural mechanisms underlying such locomotion disorder. The purpose of the present study was to reveal the neural mechanisms underlying the effects of EM-2 on the activity of motoneurons in the spinal ventral horn. First, we examine the distribution of EM-2-immunoreactive (IR) primary afferent fibers and their synaptic connections with the motoneurons innervating the skeletal muscles of the lower limb revealed by sciatic nerve retrograde tracing. The results showed that EM-2-IR fibers and terminals were sparsely observed in lamina IX and they formed symmetric synaptic connections with the motoneurons within lamina IX of the spinal ventral horn. Then, whole-cell patch-clamp technique was used to observe the effects of EM-2 on the spontaneous excitatory postsynaptic current (sEPSC) of motoneurons in lamina IX. The results showed that EM-2 could decrease both the frequency and amplitude of the sEPSC of the motoneurons in lamina IX, which was reversed by the MOR antagonist CTOP. These results indicate that EM-2-IR fibers originated from primary afferent fibers form symmetric synaptic connections with motoneurons innervating skeletal muscles of the lower limbs in lamina IX of the spinal ventral horn and EM-2 might exert inhibitory effects on the activities of these motoneurons through both presynaptic and postsynaptic mechanisms.

Spatially asymmetric reorganization of inhibition establishes a motion-sensitive circuit

Spatial asymmetries in neural connectivity have an important role in creating basic building blocks of neuronal processing. A key circuit module of directionally selective (DS) retinal ganglion cells is a spatially asymmetric inhibitory input from starburst amacrine cells. It is not known how and when this circuit asymmetry is established during development. Here we photostimulate mouse starburst cells targeted with channelrhodopsin-2 (refs 6-8) while recording from a single genetically labelled type of DS cell. We follow the spatial distribution of synaptic strengths between starburst and DS cells during early postnatal development before these neurons can respond to a physiological light stimulus, and confirm connectivity by monosynaptically restricted trans-synaptic rabies viral tracing. We show that asymmetry develops rapidly over a 2-day period through an intermediate state in which random or symmetric synaptic connections have been established. The development of asymmetry involves the spatially selective reorganization of inhibitory synaptic inputs. Intriguingly, the spatial distribution of excitatory synaptic inputs from starburst cells is significantly more symmetric than that of the inhibitory inputs at the end of this developmental period. Our work demonstrates a rapid developmental switch from a symmetric to asymmetric input distribution for inhibition in the neural circuit of a principal cell.

Coexistence of uncorrelated and correlated attractors in a nonmonotonic neural network

A neural network with correlated attractors ala Griniasty et al (1993 Conversion of temporal correlations between stimuli to spatial correlations between attractors Neural Comp. 5 1-17) is studied for a nonmonotonic response function in the cases of both finite and extensive memory loading. In the finite loading case with symmetric synaptic connections, the number of neighbouring memory patterns correlated with an attractor tends to be decreased as the degree of nonmonotonicity is increased. To study the extensive loading case, we generalize the previously proposed self-consistent signal-to-noise analysis to associative memory models with a general class of local Hebbian synaptic connections. Applying the results to the present model, we derive analytic expressions for the equilibrium order parameters. New finding includes the coexistence of uncorrelated and correlated attractors in the obtained phase diagram. This fact, which is confirmed by extensive numerical simulations, may have significant implications for long-term visual memory.

Retrieval properties of analog neural networks and the nonmonotonicity of transfer functions

Characteristic properties of associative memory networks with continuous-time dynamics are extensively studied for a certain class of nonmonotonic transfer functions by means of the self-consistent signal-to-noise analysis (SCSNA) and numerical simulations. The conventional Hebb-type symmetric synaptic connections with unbiased random patterns are assumed. Although the occurrence of instability, including an oscillatory one, makes the storage capacity fall below the upper bound for storage ratio obtained by the SCSNA, the storage capacity remains as large as 0.4 in the optimal cases. It is also noted that noise in the local fields (i.e., the inputs to neurons) can vanish for certain cases of nonmonotonic transfer functions even with an extensive number of stored patterns. Implication of the present results is the possibility of improving the network performances by the achievement of errorless retrieval and enhancement of storage capacity with the use of nonmonotonic transfer functions.

System dynamics simulation of Hopfield neural networks

Abstract Hopfield networks are a class of neural network models where non-linear graded response neurons organized into networks with effectively symmetric synaptic connections are able to implement interesting algorithms, thereby introducing the concept of information storage in the stable states of dynamical systems. In addition to opening up the possibility of using system dynamics as a vehicle to gain potentially useful insights into the behaviour of such networks, especially in the field or nonelectrical engineering, we study the dynamics of the state-space trajectory as well as time domain evolution of sensitivities of the states with respect to circuit parameters.

Beta-endorphin innervation of dopamine neurons in the rat hypothalamus: a light and electron microscopic double immunostaining study.

Pharmacological data suggest that opiates, acting indirectly via the catecholaminergic system, are involved in the inhibition of LH release and the stimulation of PRL secretion. The aim of this study was to demonstrate on the ultrastructural level whether beta-endorphin-immunoreactive fibers form synaptic contacts with hypothalamic dopaminergic neurons. Light and electron microscopic double immunostaining experiments were performed on vibratome sections prepared from the hypothalamus of acrolein-fixed female rat brains. Immunoreactivity for beta-endorphin was visualized by a dark blue to black nickel ammonium sulfate-intensified diaminobenzidine reaction, and in a consecutive immunostaining procedure, the tyrosine hydroxylase-immunoreactive dopamine cells were labeled with the brown diaminobenzidine reaction product. Under the light microscope, beta-endorphin axon terminals were found to contact dopamine cell bodies and dendrites throughout the hypothalamus. The majority of opiate target dopamine neurons were found in the periventricular area, retrochiasmatic area, and lateral part of the zona incerta. A much smaller number was observed in the dorsomedial hypothalamic nucleus and the anterior hypothalamus, and only a very few dopamine cells could be detected in contact with beta-endorphin axons in the arcuate nucleus (particularly in the posterior part where the beta-endorphin cells are located) and the medial part of the zona incerta. After light microscopic examination and color photography, the double immunostained sections were embedded for correlated electron microscopy to verify and characterize the putative synaptic connections. Electron microscopy revealed symmetric synaptic connections between beta-endorphin-immunoreactive boutons and tyrosine hydroxylase-immunopositive cell bodies and dendrites. These results together with the observation of dopamine innervation of LHRH-producing neurons and progesterone receptor-containing cells indicate that neurons of the hypothalamic dopaminergic system probably mediate opiate effects on hypophyseal hormone secretion.

Beta-endorphin innervation of dopamine neurons in the rat hypothalamus: a light and electron microscopic double immunostaining study

Pharmacological data suggest that opiates, acting indirectly via the catecholaminergic system, are involved in the inhibition of LH release and the stimulation of PRL secretion. The aim of this study was to demonstrate on the ultrastructural level whether beta-endorphin-immunoreactive fibers form synaptic contacts with hypothalamic dopaminergic neurons. Light and electron microscopic double immunostaining experiments were performed on vibratome sections prepared from the hypothalamus of acrolein-fixed female rat brains. Immunoreactivity for beta-endorphin was visualized by a dark blue to black nickel ammonium sulfate-intensified diaminobenzidine reaction, and in a consecutive immunostaining procedure, the tyrosine hydroxylase-immunoreactive dopamine cells were labeled with the brown diaminobenzidine reaction product. Under the light microscope, beta-endorphin axon terminals were found to contact dopamine cell bodies and dendrites throughout the hypothalamus. The majority of opiate target dopamine neurons were found in the periventricular area, retrochiasmatic area, and lateral part of the zona incerta. A much smaller number was observed in the dorsomedial hypothalamic nucleus and the anterior hypothalamus, and only a very few dopamine cells could be detected in contact with beta-endorphin axons in the arcuate nucleus (particularly in the posterior part where the beta-endorphin cells are located) and the medial part of the zona incerta. After light microscopic examination and color photography, the double immunostained sections were embedded for correlated electron microscopy to verify and characterize the putative synaptic connections. Electron microscopy revealed symmetric synaptic connections between beta-endorphin-immunoreactive boutons and tyrosine hydroxylase-immunopositive cell bodies and dendrites. These results together with the observation of dopamine innervation of LHRH-producing neurons and progesterone receptor-containing cells indicate that neurons of the hypothalamic dopaminergic system probably mediate opiate effects on hypophyseal hormone secretion.

Synaptic interactions between GABAergic neurons and trigeminothalamic cells in the rat trigeminal Nucleus Caudalis

Synaptic interactions between GABAergic neurons and thalamic projecting cells within the trigeminal nucleus caudalis were examined using a combined method of GABA immunohistochemistry and retrograde WGA-HRP labeling of the trigeminothalamic pathway. Results showed that GABA-positive neurons and projecting cells were separate but closely intermingled within the spinal trigeminal nucleus. GABAergic axon terminals formed symmetric synaptic connections with the cell bodies and dendrites of trigeminothalamic neurons. In turn, some WGA-HRP filled axon terminals, presumed to originate from axon collaterals of the projecting neurons, formed synaptic connections with GABA containing neurons. The results suggest that in the spinal trigeminal nucleus there is a reciprocal modulation between GABA neurons and trigeminothalamic cells.

High-speed division unit using asymmetric neural network architecture

A neural network with symmetric synaptic connections has been shown to be an effective computational unit. However, this model does not always guarantee global convergence nor yield a fast settling time. An asymmetric model without these problems is introduced in the letter. A high-speed divider illustrates its computational capabilities.

Recognition of general patterns using neural networks

The Hopfield model of neural network stores memory in its symmetric synaptic connections and can only learn to recognize sets of nearly "orthogonal" patterns. A new algorithm is put forth to permit the recognition of general ("non-orthogonal") patterns. The algorithm specifies the construction of the new network's memory matrix Tij, which is, in general, asymmetrical and contains the Hopfield neural network (Hopfield 1982) as a special case. We find further that in addition to this new algorithm for general pattern recognition, there exists in fact a large class of Tij memory matrices which permit the recognition of non-orthogonal patterns. The general form of this class of Tij memory matrix is presented, and the projection matrix neural network (Personnaz et al. 1985) is found as a special case of this general form. This general form of memory matrix extends the library of memory matrices which allow a neural network to recognize non-orthogonal patterns. A neural network which followed this general form of memory matrix was modeled on a computer and successfully recognized a set of non-orthogonal patterns. The new network also showed a tolerance for altered and incomplete data. Through this new method, general patterns may be taught to the neural network.

Computing with neural circuits: a model.

A new conceptual framework and a minimization principle together provide an understanding of computation in model neural circuits. The circuits consist of nonlinear graded-response model neurons organized into networks with effectively symmetric synaptic connections. The neurons represent an approximation to biological neurons in which a simplified set of important computational properties is retained. Complex circuits solving problems similar to those essential in biology can be analyzed and understood without the need to follow the circuit dynamics in detail. Implementation of the model with electronic devices will provide a class of electronic circuits of novel form and function.