Analysis of interference in quasi-holographic neuronal models of memory]

Three-layer models with neural plasticity are considered. The weights of connections have both positive and negative values, which are chosen randomly with equal probability. The connections are symmetric relative to the layer of associative neurons. The models describe processes analogous to holographic ones, and the basic properties of the models correspond to the properties of brain memory. The random choice of weights providing the noncorrelatedness of separate recordings gives rise to disturbances. It was shown that in passing from a full-connected model to non-full-connected and semi-full-connected models, the disturbances increase and limitations arise. The degree of the increase in disturbances depends upon the type of the model, the methods of recording and methods of the use of the models, and the mechanisms of the origination of disturbances.     

Transitory memory retrieval in a biologically plausible neural network model

A number of memory models have been proposed. These all have the basic structure that excitatory neurons are reciprocally connected by recurrent connections together with the connections with inhibitory neurons, which yields associative memory (i.e., pattern completion) and successive retrieval of memory. In most of the models, a simple mathematical model for a neuron in the form of a discrete map is adopted. It has not, however, been clarified whether behaviors like associative memory and successive retrieval of memory appear when a biologically plausible neuron model is used. In this paper, we propose a network model for associative memory and successive retrieval of memory based on Pinsky-Rinzel neurons. The state of pattern completion in associative memory can be observed with an appropriate balance of excitatory and inhibitory connection strengths. Increasing of the connection strength of inhibitory interneurons changes the state of memory retrieval from associative memory to successive retrieval of memory. We investigate this transition.     

Modeling the Seasonal Adaptation of Circadian Clocks by Changes in the Network Structure of the Suprachiasmatic Nucleus

The dynamics of circadian rhythms needs to be adapted to day length changes between summer and winter. It has been observed experimentally, however, that the dynamics of individual neurons of the suprachiasmatic nucleus (SCN) does not change as the seasons change. Rather, the seasonal adaptation of the circadian clock is hypothesized to be a consequence of changes in the intercellular dynamics, which leads to a phase distribution of electrical activity of SCN neurons that is narrower in winter and broader during summer. Yet to understand this complex intercellular dynamics, a more thorough understanding of the impact of the network structure formed by the SCN neurons is needed. To that effect, we propose a mathematical model for the dynamics of the SCN neuronal architecture in which the structure of the network plays a pivotal role. Using our model we show that the fraction of long-range cell-to-cell connections and the seasonal changes in the daily rhythms may be tightly related. In particular, simulations of the proposed mathematical model indicate that the fraction of long-range connections between the cells adjusts the phase distribution and consequently the length of the behavioral activity as follows: dense long-range connections during winter lead to a narrow activity phase, while rare long-range connections during summer lead to a broad activity phase. Our model is also able to account for the experimental observations indicating a larger light-induced phase-shift of the circadian clock during winter, which we show to be a consequence of higher synchronization between neurons. Our model thus provides evidence that the variations in the seasonal dynamics of circadian clocks can in part also be understood and regulated by the plasticity of the SCN network structure.     

A Model of Self-Organizing Head-Centered Visual Responses in Primate Parietal Areas

We present a hypothesis for how head-centered visual representations in primate parietal areas could self-organize through visually-guided learning, and test this hypothesis using a neural network model. The model consists of a competitive output layer of neurons that receives afferent synaptic connections from a population of input neurons with eye position gain modulated retinal receptive fields. The synaptic connections in the model are trained with an associative trace learning rule which has the effect of encouraging output neurons to learn to respond to subsets of input patterns that tend to occur close together in time. This network architecture and synaptic learning rule is hypothesized to promote the development of head-centered output neurons during periods of time when the head remains fixed while the eyes move. This hypothesis is demonstrated to be feasible, and each of the core model components described is tested and found to be individually necessary for successful self-organization.     

主动联想记忆神经网络模型

在信息编码能提高联想记忆的存贮能力和脑内存在主动活动机制的启发下,提出一个主动联想记忆模型。模型包括两个神经网络,其一为输入和输出网络,另一个为在学习时期能自主产生兴奋模式的主动网络。两个网络的神经元之间有突触联系。由于自主产生的兴奋模式与输入无关,并可能接近于相互正交,因此,本模型有较高的存贮能力。初步分析和计算机仿真证明：本模型确有比通常联想记忆模型高的存贮能力,特别是在输入模式间有高度相关情况下、最后,对提出的模型与双向自联想记忆和光学全息存贮机制的关系作了讨论。     

Vector representation of associative learning

I. P. Pavlov [12] has shown that conditioned reflexes are selective both with respect to conditioned stimuli and to conditioned reflexes elicited by those conditioned stimuli. At the neuronal level selective aspects of conditioned stimuli are based on detectors selectively tuned to respective stimuli. The selective aspects of conditioned reflexes are due to command neurons representing specific unconditioned reflexes. It can be assumed that conditioned reflexes result from association between selective detectors and specific command neurons. The detectors activated by a conditioned stimulus constitute a combination of excitations--a detector excitation vector. The detector excitation vector acts on a command neuron via a set of plastic synapses--a synaptic weight vector. Plastic synapses are modified in the process of learning making command neuron selectively tuned to a specific conditioned stimulus. The selective tuning of a particular command neuron to a specific excitation vector referred to a conditioned stimulus is a basis of associative learning. The probabilities of conditioned reflexes elicited by conditioned and differential stimuli implicitly contain information concerning excitation vectors that encode respective stimuli. Contribution of the vector code to associative learning was explored combining differential color conditioning with intracellular recording from color-coding neurons. It was shown that colors in carps and monkeys are represented on a hypersphere in the four-dimensional space similar to human color space. The basis of the color space is constituted by red-green, blue-yellow, brightness and darkness neurons.     

Functional model of biological neural networks

A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.     

Associative learning in a network model of Hermissenda crassicornis

A time-varying Resistance-Capacitance (RC) circuit computer model was constructed based on known membrane and synaptic properties of the visualvestibular network of the marine snail Hermissenda crassicornis. Specific biophysical properties and synaptic connections of identified neurons are represented as lumped parameters (circuit elements) in the model; in the computer simulation, differential equations are approximated by difference equations. The model's output, membrane potential, an indirect measure of firing frequency, closely parallels the behavioral and electrophysiologic outputs of Hermissenda in response to the same input stimuli presented during and after associative learning. The parallelism of the computer modeled and the biologic outputs suggests that the model captures the features necessary and sufficient for associative learning.     

Associative fear learning enhances sparse network coding in primary sensory cortex

Several models of associative learning predict that stimulus processing changes during association formation. How associative learning reconfigures neural circuits in primary sensory cortex to "learn" associative attributes of a stimulus remains unknown. Using 2-photon in vivo calcium imaging to measure responses of networks of neurons in primary somatosensory cortex, we discovered that associative fear learning, in which whisker stimulation is paired with foot shock, enhances sparse population coding and robustness of the conditional stimulus, yet decreases total network activity. Fewer cortical neurons responded to stimulation of the trained whisker than in controls, yet their response strength was enhanced. These responses were not observed in mice exposed to a nonassociative learning procedure. Our results define how the cortical representation of a sensory stimulus is shaped by associative fear learning. These changes are proposed to enhance efficient sensory processing after associative learning.     

Activity-dependent matching of excitatory and inhibitory inputs during refinement of visual receptive fields

The receptive field (RF) of single visual neurons undergoes progressive refinement during development. It remains largely unknown how the excitatory and inhibitory inputs on single developing neurons are refined in a coordinated manner to allow the formation of functionally correct circuits. Using whole-cell voltage-clamp recording from Xenopus tectal neurons, we found that RFs determined by excitatory and inhibitory inputs in more mature tectal neurons are spatially matched, with each spot stimulus evoking balanced synaptic excitation and inhibition. This emerges during development through a gradual reduction in the RF size and a transition from disparate to matched topography of excitatory and inhibitory inputs to the tectal neurons. Altering normal spiking activity of tectal neurons by either blocking or elevating GABA(A) receptor activity significantly impeded the developmental reduction and topographic matching of RFs. Thus, appropriate inhibitory activity is essential for the coordinated refinement of excitatory and inhibitory connections.     

Models of heterogeneous dopamine signaling in an insect learning and memory center

The Drosophila mushroom body exhibits dopamine dependent synaptic plasticity that underlies the acquisition of associative memories. Recordings of dopamine neurons in this system have identified signals related to external reinforcement such as reward and punishment. However, other factors including locomotion, novelty, reward expectation, and internal state have also recently been shown to modulate dopamine neurons. This heterogeneity is at odds with typical modeling approaches in which these neurons are assumed to encode a global, scalar error signal. How is dopamine dependent plasticity coordinated in the presence of such heterogeneity? We develop a modeling approach that infers a pattern of dopamine activity sufficient to solve defined behavioral tasks, given architectural constraints informed by knowledge of mushroom body circuitry. Model dopamine neurons exhibit diverse tuning to task parameters while nonetheless producing coherent learned behaviors. Notably, reward prediction error emerges as a mode of population activity distributed across these neurons. Our results provide a mechanistic framework that accounts for the heterogeneity of dopamine activity during learning and behavior.     

Patterns of spatio-temporal correlations in the neural activity of the cat motor cortex during trained forelimb movements

In order to study how neurons in the primary motor cortex (MI) are dynamically linked together during skilled movement, we recorded simultaneously from many cortical neurons in cats trained to perform a reaching and retrieval task using their forelimbs. Analysis of task-related spike activity in the MI of the hemisphere contralateral to the reaching forelimb (in identified forelimb or hindlimb representations) recorded through chronically implanted microwires, was followed by pairwise evaluation of temporally correlated activity in these neurons during task performance using shuffle corrected cross-correlograms. Over many months of recording, a variety of task-related modulations of neural activities were observed in individual efferent zones. Positively correlated activity (mainly narrow peaks at zero or short latencies) was seen during task performance frequently between neurons recorded within the forelimb representation of MI, rarely within the hindlimb area of MI, and never between forelimb and hindlimb areas. Correlated activity was frequently observed between neurons with different patterns of task-related activity or preferential activity during different task elements (reaching, feeding, etc.), and located in efferent zones with dissimilar representation as defined by intracortical microstimulation. The observed synchronization of action potentials among selected but functionally varied groups of MI neurons possibly reflects dynamic recruitment of network connections between efferent zones during skilled movement.     

Neuronal signalling of fear memory

The learning and remembering of fearful events depends on the integrity of the amygdala, but how are fear memories represented in the activity of amygdala neurons? Here, we review recent electrophysiological studies indicating that neurons in the lateral amygdala encode aversive memories during the acquisition and extinction of Pavlovian fear conditioning. Studies that combine unit recording with brain lesions and pharmacological inactivation provide evidence that the lateral amygdala is a crucial locus of fear memory. Extinction of fear memory reduces associative plasticity in the lateral amygdala and involves the hippocampus and prefrontal cortex. Understanding the signalling of aversive memory by amygdala neurons opens new avenues for research into the neural systems that support fear behaviour.     

Callosal connections in the cat parietal cortex during postnatal ontogenesis

By means of the method based on the retrograde axonal transport of horseradish peroxidase topography, quantitative and qualitative composition of homotopic neurons in the cat cerebral parietal associative cortex performing callosal connections have been studied. When comparing the data of the experiment with those previously obtained on distribution of the axonal terminals in the comissural neurons, certain places are revealed where concentration of the homotopic callosal connections of the parietal cortex field 7 take place. A morphological characteristic of the longaxonal pyramidal and stellate neurons forming these connections is presented.     

Bulbocortical interplay in olfactory information processing via synchronous oscillations

Emergence of synchronous oscillatory activity is an inherent feature of the olfactory systems of insects, mollusks and mammals. A class of simple computational models of the mammalian olfactory system consisting of olfactory bulb and olfactory cortex is constructed to explore possible roles of the related neural circuitry in olfactory information processing via synchronous oscillations. In the models, the bulbar neural circuitry is represented by a chain of oscillators and that of cortex is analogous to an associative memory network with horizontal synaptic connections. The models incorporate the backprojection from cortical units to the bulbar oscillators in particular ways. They exhibit rapid and robust synchronous oscillations in the presence of odorant stimuli, while they show either nonoscillatory states or propagating waves in the absence of stimuli, depending on the values of model parameters. In both models, the backprojection is shown to enhance the establishment of large-scale synchrony. The results suggest that the modulation of neural activity through centrifugal inputs may play an important role at the early stage of cortical information processing.     

NMDA receptors in dopaminergic neurons are crucial for habit learning

Dopamine is crucial for habit learning. Activities of midbrain dopaminergic neurons are regulated by the cortical and subcortical signals among which glutamatergic afferents provide excitatory inputs. Cognitive implications of glutamatergic afferents in regulating and engaging dopamine signals during habit learning, however, remain unclear. Here, we show that mice with dopaminergic neuron-specific NMDAR1 deletion are impaired in a variety of habit-learning tasks, while normal in some other dopamine-modulated functions such as locomotor activities, goal-directed learning, and spatial reference memories. In vivo neural recording revealed that dopaminergic neurons in these mutant mice could still develop the cue-reward association responses; however, their conditioned response robustness was drastically blunted. Our results suggest that integration of glutamatergic inputs to DA neurons by NMDA receptors, likely by regulating associative activity patterns, is a crucial part of the cellular mechanism underpinning habit learning.     

Backward signal from medial temporal lobe in neural circuit reorganization of primate inferotemporal cortex

Neuropsychological theories proposed a critical role of the interaction between the medial temporal lobe and neocortex in the formation of long-term memory for facts and events, which has often been tested by learning of a series of paired words or figures in humans. We identify neural mechanisms of this long-term memory formation process by single-unit recording and molecular biological methods in an animal model of visual pair-association task in monkeys. In our previous studies, we found a group of neurons that manifested selective responses to both of the paired associates (pair-coding neuron) in the anterior inferior temporal (IT) cortex. It provides strong evidence that single IT neurons acquire the response-selectivity through associative learning, and suggests that the reorganized neural circuits for the pair-coding neurons serve as the memory engram of the pair-association learning. In this article, we investigated further mechanisms of the neural circuit reorganization. First, we tested the role of the backward connections from the medial temporal lobe to IT cortex. lbotenic acid was injected unilaterally into the entorhinal and perirhinal cortex which provided massive backward projections ipsilaterally to IT cortex. We found that the limbic lesion disrupted the associative code of the IT neurons between the paired associates, without impairing the visual response to each stimulus. Second, we ask why the limbic-neocortical interactions are so important. We hypothesize that limbic neurons would undergo rapid modification of synaptic connectivity and provide backward signals that guide reorganization of neocortical neural circuits. We then investigated the molecular basis of such rapid synaptic modifiability by detecting the expression of immediate-early genes. We found strong expression of zif268 during the learning of a new set of paired associates, most intensively in area 36 of the perirhinal cortex. All these results with visual pair-association task support our hypothesis, and demonstrate that the ‘consolidation’ process, which was first proposed on the basis of clinico-psychological evidence, can now be examined in the primate with neurophysiolocical and molecular biological approaches.     

Associative neural network model for the generation of temporal patterns. Theory and application to central pattern generators.

Cyclic patterns of motor neuron activity are involved in the production of many rhythmic movements, such as walking, swimming, and scratching. These movements are controlled by neural circuits referred to as central pattern generators (CPGs). Some of these circuits function in the absence of both internal pacemakers and external feedback. We describe an associative neural network model whose dynamic behavior is similar to that of CPGs. The theory predicts the strength of all possible connections between pairs of neurons on the basis of the outputs of the CPG. It also allows the mean operating levels of the neurons to be deduced from the measured synaptic strengths between the pairs of neurons. We apply our theory to the CPG controlling escape swimming in the mollusk Tritonia diomedea. The basic rhythmic behavior is shown to be consistent with a simplified model that approximates neurons as threshold units and slow synaptic responses as elementary time delays. The model we describe may have relevance to other fixed action behaviors, as well as to the learning, recall, and recognition of temporally ordered information.     

Neuronal Activity of the Subthalamic Nucleus in Patients with Parkinson’s Disease

The discharge activity of 637 neurons of the human subthalamic nucleus (STN), which were extracellularly recorded during twelve stereotactic surgeries in patients with Parkinson’s disease, has been analyzed. On the basis of the parameters of interspike intervals (ISIs), we have distinguished three major patterns of spontaneous neuronal activity: bursting neurons, regular tonic and irregular tonic neurons. Parametric analysis has enabled us to determine the values of basic parameters in the activity of these three distinguished types of neurons. It has been shown that the representativeness and the activity parameters of three different patterns change in the dorsoventral direction of the STN from the motor to the associative regions. The results will allow researchers to perform targeted search of pathological neuronal activity patterns associated with the motor symptoms of Parkinsonism.