Intracellular mechanisms of adaptation and self-regulation in self-organizing networks: The role of chemical transducers

This paper describes mechanisms of intracellular and intercellular adaptation that are due to spatial or temporal factors. The spatial mechanisms support self-regulating pattern formation that is capable of directing self-organization in a large class of systems, including examples of directed intercellular growth, transmitter production, and intracellular conductance changes. A balance between intracellular flows and counterflows causes adaptation. This balance can be shifted by environmental inputs. The decrease in Ca²⁺-modulated outward K⁺ conductance in certain molluscan nerve cells is a likely example. Examples wherein Ca²⁺ acts as a second messenger that shunts receptor sensitivity can also be discussed from this perspective. The systems differ in basic ways from recent diffusion models. Chemical transducers driven by membrane-bound intracellular signals can establish long-range intercellular interactions that compensate for variable intercellular distances and are invariant under developmental size changes; diffusional signals do not. The intracellular adaptational mechanisms are formally analogous to intercellular mechanisms that include cellular properties which are omitted in recent reaction-diffusion models of pattern formation. The cellular models use these properties to compute size-invariant properties despite wide variations in their intercellular signals. Mechanisms of temporal adaptation can be derived from the simplest laws of chemical transduction by using a correspondence principle. These mechanisms lead to such properties of intercellular signals as transient overshoot, antagonistic rebound, and an inverted U in sensitivity as intracellular signals or adaptation levels shift. Such effects are implicated in studies of behavioral, reinforcement, motor control, and cognitive coding. Supported in part by the National Science Foundation (NSF MCS 77-02958).     

Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors

This paper analyses a model for the parallel development and adult coding of neural feature detectors. The model was introduced in Grossberg (1976). We show how experience can retune feature detectors to respond to a prescribed convex set of spatial patterns. In particular, the detectors automatically respond to average features chosen from the set even if the average features have never been experienced. Using this procedure, any set of arbitrary spatial patterns can be recoded, or transformed, into any other spatial patterns (universal recoding), if there are sufficiently many cells in the network's cortex. The network is built from short term memory (STM) and long term memory (LTM) mechanisms, including mechanisms of adaptation, filtering, contrast enhancement, tuning, and nonspecific arousal. These mechanisms capture some experimental properties of plasticity in the kitten visual cortex. The model also suggests a classification of adult feature detector properties in terms of a small number of functional principles. In particular, experiments on retinal dynamics, including amarcrine cell function, are suggested.Supported in part by the Advanced Research Projects Agency under ONR Contract No. N00014-76-C-0185     

Ganzfeld Stimulation or Sleep Enhance Long Term Motor Memory Consolidation Compared to Normal Viewing in Saccadic Adaptation Paradigm

Adaptation of saccade amplitude in response to intra-saccadic target displacement is a type of implicit motor learning which is required to compensate for physiological changes in saccade performance. Once established trials without intra-saccadic target displacement lead to de-adaptation or extinction, which has been attributed either to extra-retinal mechanisms of spatial constancy or to the influence of the stable visual surroundings. Therefore we investigated whether visual deprivation (“Ganzfeld”-stimulation or sleep) can partially maintain this motor learning compared to free viewing of the natural surroundings. Thirty-five healthy volunteers performed two adaptation blocks of 100 inward adaptation trials – interspersed by an extinction block – which were followed by a two-hour break with or without visual deprivation (VD). Using additional adaptation and extinction blocks short and long (4 weeks) term memory of this implicit motor learning were tested. In the short term, motor memory tested immediately after free viewing was superior to adaptation performance after VD. In the long run, however, effects were opposite: motor memory and relearning of adaptation was superior in the VD conditions. This could imply independent mechanisms that underlie the short-term ability of retrieving learned saccadic gain and its long term consolidation. We suggest that subjects mainly rely on visual cues (i.e., retinal error) in the free viewing condition which makes them prone to changes of the visual stimulus in the extinction block. This indicates the role of a stable visual array for resetting adapted saccade amplitudes. In contrast, visual deprivation (GS and sleep), might train subjects to rely on extra-retinal cues, e.g., efference copy or prediction to remap their internal representations of saccade targets, thus leading to better consolidation of saccadic adaptation.     

On the dynamics of operant conditioning

Simple psychological postulates are presented which are used to derive possible anatomical and physiological substrates of operant conditioning. These substrates are compatible with much psychological data about operants. A main theme is that aspects of operant and respondent conditioning share a single learning process. Among the phenomena which arise are the following: UCS-activated arousal; formation of conditioned, or secondary, reinforcers; a non-specific arousal system distinct from sensory and motor representations whose activation is required for sensory processing; polyvalent cells responsive to the sum of CS and UCS inputs and anodal d.c. potential shifts; neural loci responsive to the combined effect of sensory events and drive deprivation; “go”-like or “now print”-like mechanisms which, for example, influence incentive-motivational increases in general activity; a mechanism for learning repetitively to press a bar which electrically stimulates suitable arousal loci in the absence of drive reduction; uniformly distributed potentials, driven by the CS, in the “cerebral cortex” of a trained network; the distinction between short-term and long-term memory, and the possibility of eliminating transfer from short-term to long-term memory in the absence of suitable arousal; networks that can learn and perform arbitrarily complex sequences of acts or sensory memories, without continuous control by sensory feedback, whose rate of performance can be regulated by the level of internal arousal; networks with idetic memory; network analogs of “therapeutic resistance” and “repression”; the possibility of conditioning the sensory feedback created by a motor act to the neural controls of this act, with consequences for sensory-motor adaptation and child development. This paper introduces explicit minimal anatomies and physiological rules that formally give rise to analogous phenomena. These networks consider only aspects of positive conditioning. They are derived from simple psychological facts.     

Adaptation supports short-term memory in a visual change detection task

The maintenance of short-term memories is critical for survival in a dynamically changing world. Previous studies suggest that this memory can be stored in the form of persistent neural activity or using a synaptic mechanism, such as with short-term plasticity. Here, we compare the predictions of these two mechanisms to neural and behavioral measurements in a visual change detection task. Mice were trained to respond to changes in a repeated sequence of natural images while neural activity was recorded using two-photon calcium imaging. We also trained two types of artificial neural networks on the same change detection task as the mice. Following fixed pre-processing using a pretrained convolutional neural network, either a recurrent neural network (RNN) or a feedforward neural network with short-term synaptic depression (STPNet) was trained to the same level of performance as the mice. While both networks are able to learn the task, the STPNet model contains units whose activity are more similar to the in vivo data and produces errors which are more similar to the mice. When images are omitted, an unexpected perturbation which was absent during training, mice often do not respond to the omission but are more likely to respond to the subsequent image. Unlike the RNN model, STPNet produces a similar pattern of behavior. These results suggest that simple neural adaptation mechanisms may serve as an important bottom-up memory signal in this task, which can be used by downstream areas in the decision-making process.     

Short-term memory of motor network performance via activity-dependent potentiation of Na+/K+ pump function

Brain networks memorize previous performance to adjust their output in light of past experience. These activity-dependent modifications generally result from changes in synaptic strengths or ionic conductances, and ion pumps have only rarely been demonstrated to play a dynamic role. Locomotor behavior is produced by central pattern generator (CPG) networks and modified by sensory and descending signals to allow for changes in movement frequency, intensity, and duration, but whether or how the CPG networks recall recent activity is largely unknown. In Xenopus frog tadpoles, swim bout duration correlates linearly with interswim interval, suggesting that the locomotor network retains a short-term memory of previous output. We discovered an ultraslow, minute-long afterhyperpolarization (usAHP) in network neurons following locomotor episodes. The usAHP is mediated by an activity- and sodium spike-dependent enhancement of electrogenic Na(+)/K(+) pump function. By integrating spike frequency over time and linking the membrane potential of spinal neurons to network performance, the usAHP plays a dynamic role in short-term motor memory. Because Na(+)/K(+) pumps are ubiquitously expressed in neurons of all animals and because sodium spikes inevitably accompany network activity, the usAHP may represent a phylogenetically conserved but largely overlooked mechanism for short-term memory of neural network function.     

Short-term and long-term memory in single cells

Many approaches have been used to study short- and long-term memory. Bacteria detect chemical gradients using a memory obtained by the combination of a fast excitation process and a slow adaptation process. This model system, which has the advantages of extensive genetic and biochemical information, shows no features of long-term memory. To study long-term memory, neural cell line systems have been developed that exhibit two phenomena associated with learning and memory, habituation and potentiation. The expression of these phenomena in clonal cell lines, devoid of synaptic connections, makes it possible to study the biochemical and molecular mechanisms that contribute to short-term and long-term memory.     

Interplay between Short- and Long-Term Plasticity in Cell-Assembly Formation

Various hippocampal and neocortical synapses of mammalian brain show both short-term plasticity and long-term plasticity, which are considered to underlie learning and memory by the brain. According to Hebb’s postulate, synaptic plasticity encodes memory traces of past experiences into cell assemblies in cortical circuits. However, it remains unclear how the various forms of long-term and short-term synaptic plasticity cooperatively create and reorganize such cell assemblies. Here, we investigate the mechanism in which the three forms of synaptic plasticity known in cortical circuits, i.e., spike-timing-dependent plasticity (STDP), short-term depression (STD) and homeostatic plasticity, cooperatively generate, retain and reorganize cell assemblies in a recurrent neuronal network model. We show that multiple cell assemblies generated by external stimuli can survive noisy spontaneous network activity for an adequate range of the strength of STD. Furthermore, our model predicts that a symmetric temporal window of STDP, such as observed in dopaminergic modulations on hippocampal neurons, is crucial for the retention and integration of multiple cell assemblies. These results may have implications for the understanding of cortical memory processes.     

Beta(2)-adrenergic receptors potentiate glucocorticoid receptor transactivation via G protein beta gamma-subunits and the phosphoinositide 3-kinase pathway

Glucocorticoid hormones influence manifold neuronal processes including learning, memory, and emotion via the glucocorticoid receptor (GR). Catecholamines further modulate these functions, although the underlying molecular mechanisms are poorly understood. Here, we show that epinephrine and norepinephrine potentiate ligand-dependent GR transactivation in a hippocampal cell line (HT22) via beta(2)-adrenergic receptors. This enhancement was strongest at low concentrations of glucocorticoids and was accompanied by increased GR binding to a glucocorticoid-responsive element (GRE). beta(2)-Adrenergic receptor-mediated GR enhancement was relayed via G protein beta gamma-subunits, insensitive to pertussis toxin and independent of protein kinase A (PKA). In contrast, the catecholamine-evoked GR enhancement was strongly reduced by wortmannin, suggesting a critical role for phosphoinositide 3-kinase (PI3-K). In agreement, epinephrine directly activated PI3-K in vivo. Similarly, stimulation of tyrosine kinase receptors coupled to PI3-K activation, e.g. receptors for insulin-like growth factor I (IGF-I) or fibroblast growth factor (FGF), increased GR transactivation. Further analysis indicated that G protein-coupled receptor (GPCR) and tyrosine kinase receptor signals converge on PI3-K through separate mechanisms. Blockade of GR enhancement by wortmannin was partially overcome by expression of the downstream-acting protein kinase B (PKB/Akt). Collectively, our findings demonstrate that GPCRs can regulate GR transactivation by stimulating PI3-K. This novel cross-talk may provide new insights into the molecular processes of learning and memory and the treatment of stress-related disorders.     

Stochastic Resonance Crossovers in Complex Networks

Here we numerically study the emergence of stochastic resonance as a mild phenomenon and how this transforms into an amazing enhancement of the signal-to-noise ratio at several levels of a disturbing ambient noise. The setting is a cooperative, interacting complex system modelled as an Ising-Hopfield network in which the intensity of mutual interactions or “synapses” varies with time in such a way that it accounts for, e.g., a kind of fatigue reported to occur in the cortex. This induces nonequilibrium phase transitions whose rising comes associated to various mechanisms producing two types of resonance. The model thus clarifies the details of the signal transmission and the causes of correlation among noise and signal. We also describe short-time persistent memory states, and conclude on the limited relevance of the network wiring topology. Our results, in qualitative agreement with the observation of excellent transmission of weak signals in the brain when competing with both intrinsic and external noise, are expected to be of wide validity and may have technological application. We also present here a first contact between the model behavior and psychotechnical data.     

Effects of beta-carboline alkaloids on the object recognition task in mice

beta-carboline alkaloids are found in several medicinal plants and display a variety of actions on the central nervous, muscular and cardiovascular systems. The aim of the present study was to evaluate the effects of systemic administration of beta-carboline alkaloids on object recognition in mice. Adult Swiss mice received an intra-peritoneal injection (i.p.) of alkaloids (1.0, 2.5 or 5.0 mg/kg) 30 min before training in an object recognition task. The fully aromatic beta-carbolines, harmine and harmol, induced an enhancement of short-term memory (STM) at all doses tested when compared to controls. Harmaline, a dihydro beta-carboline and inverse agonist of the MK-801 binding site on the N-methyl-d-aspartate (NMDA) receptor, also induced an enhancement of both short-term memory (STM) and long-term memory (LTM). These results demonstrate that systemic administration of beta-carboline alkaloids can improve object recognition memory in mice.     

Network Adaptation Improves Temporal Representation of Naturalistic Stimuli in Drosophila Eye: II Mechanisms

Retinal networks must adapt constantly to best present the ever changing visual world to the brain. Here we test the hypothesis that adaptation is a result of different mechanisms at several synaptic connections within the network. In a companion paper (Part I), we showed that adaptation in the photoreceptors (R1–R6) and large monopolar cells (LMC) of the Drosophila eye improves sensitivity to under-represented signals in seconds by enhancing both the amplitude and frequency distribution of LMCs'' voltage responses to repeated naturalistic contrast series. In this paper, we show that such adaptation needs both the light-mediated conductance and feedback-mediated synaptic conductance. A faulty feedforward pathway in histamine receptor mutant flies speeds up the LMC output, mimicking extreme light adaptation. A faulty feedback pathway from L2 LMCs to photoreceptors slows down the LMC output, mimicking dark adaptation. These results underline the importance of network adaptation for efficient coding, and as a mechanism for selectively regulating the size and speed of signals in neurons. We suggest that concert action of many different mechanisms and neural connections are responsible for adaptation to visual stimuli. Further, our results demonstrate the need for detailed circuit reconstructions like that of the Drosophila lamina, to understand how networks process information.     

Adaptation-induced plasticity of orientation tuning in adult visual cortex

A key emergent property of the primary visual cortex (V1) is the orientation selectivity of its neurons. The extent to which adult visual cortical neurons can exhibit changes in orientation selectivity is unknown. Here we use single-unit recording and intrinsic signal imaging in V1 of adult cats to demonstrate systematic repulsive shifts in orientation preference following short-term exposure (adaptation) to one stimulus orientation. In contrast to the common view of adaptation as a passive process by which responses around the adapting orientation are reduced, we show that changes in orientation tuning also occur due to response increases at orientations away from the adapting stimulus. Adaptation-induced orientation plasticity is thus an active time-dependent process that involves network interactions and includes both response depression and enhancement.     

Pattern regulation in reaction-diffusion systems-the problem of size invariance

Ordinary reaction-diffusion mechanisms do not account for size invariance properties of morphogenetic fields. We show that such a failure results from ignoring cell individuality. By considering purely topological factors, such as the number of intercellular contacts or the extent of the cell surface in contact with neighbouring cells, size invariance exists in reaction-diffusion systems. Our results are general, model independent and may be applied to any multi-unit ensemble exhibiting coherent behaviour.     

Transfer entropy in magnetoencephalographic data: quantifying information flow in cortical and cerebellar networks

The analysis of cortical and subcortical networks requires the identification of their nodes, and of the topology and dynamics of their interactions. Exploratory tools for the identification of nodes are available, e.g. magnetoencephalography (MEG) in combination with beamformer source analysis. Competing network topologies and interaction models can be investigated using dynamic causal modelling. However, we lack a method for the exploratory investigation of network topologies to choose from the very large number of possible network graphs. Ideally, this method should not require a pre-specified model of the interaction. Transfer entropy--an information theoretic implementation of Wiener-type causality--is a method for the investigation of causal interactions (or information flow) that is independent of a pre-specified interaction model. We analysed MEG data from an auditory short-term memory experiment to assess whether the reconfiguration of networks implied in this task can be detected using transfer entropy. Transfer entropy analysis of MEG source-level signals detected changes in the network between the different task types. These changes prominently involved the left temporal pole and cerebellum--structures that have previously been implied in auditory short-term or working memory. Thus, the analysis of information flow with transfer entropy at the source-level may be used to derive hypotheses for further model-based testing.     

A linear model for characterization of synchronization frequencies of neural networks

The synchronization frequency of neural networks and its dynamics have important roles in deciphering the working mechanisms of the brain. It has been widely recognized that the properties of functional network synchronization and its dynamics are jointly determined by network topology, network connection strength, i.e., the connection strength of different edges in the network, and external input signals, among other factors. However, mathematical and computational characterization of the relationships between network synchronization frequency and these three important factors are still lacking. This paper presents a novel computational simulation framework to quantitatively characterize the relationships between neural network synchronization frequency and network attributes and input signals. Specifically, we constructed a series of neural networks including simulated small-world networks, real functional working memory network derived from functional magnetic resonance imaging, and real large-scale structural brain networks derived from diffusion tensor imaging, and performed synchronization simulations on these networks via the Izhikevich neuron spiking model. Our experiments demonstrate that both of the network synchronization strength and synchronization frequency change according to the combination of input signal frequency and network self-synchronization frequency. In particular, our extensive experiments show that the network synchronization frequency can be represented via a linear combination of the network self-synchronization frequency and the input signal frequency. This finding could be attributed to an intrinsically-preserved principle in different types of neural systems, offering novel insights into the working mechanism of neural systems.     

Adaptive pattern classification and universal recoding: II. Feedback,expectation, olfaction,illusions

Part I of this paper describes a model for the parallel development and adult coding of neural feature detectors. It shows how any set of arbitrary spatial patterns can be recoded, or transformed, into any other spatial patterns (universal recoding), if there are sufficiently many cells in the network's cortex. This code is, however, unstable through time if arbitrarily many patterns can perturb a fixed number of cortical cells. This paper shows how to stabilize the code in the general case using feedback between cellular sites. A biochemically defined critical period is not necessary to stabilize the code, nor is it sufficient to ensure useful coding properties.We ask how short term memory can be reset in response to temporal sequences of spatial patterns. This leads to a context-dependent code in which no feature detector need uniquely characterize an input pattern; yet unique classification by the pattern of activity across feature detectors is possible. This property uses learned expectation mechanisms whereby unexpected patterns are temporarily suppressed and/or activate nonspecific arousal. The simplest case describes reciprocal interactions via trainable synaptic pathways (long term memory traces) between two recurrent on-center off-surround networks undergoing mass action (shunting) interactions. This unit can establish an adaptive resonance, or reverberation, between two regions if their coded patterns match, and can suppress the reverberation if their patterns do not match. This concept yields a model of olfactory coding within the olfactory bulb and prepyriform cortex. The resonance idea also includes the establishment of reverberation between conditioned reinforcers and generators of contingent negative variation if presently avialable sensory cues are compatible with the network's drive requirements at that time; and a search and lock mechanism whereby the disparity between two patterns can be minimized and the minimal disparity images locked into position. Stabilizing the code uses attentional mechanisms, in particular nonspecific arousal as a tuning and search device. We suggest that arousal is gated by a chemical transmitter system—for example, norepinephrine—whose relative states of accumulation at antagonistic pairs of on-cells and off-cells through time can shift the spatial pattern of STM activity across a field of feature detectors. For example, a sudden arousal increment in response to an un-expected pattern can reverse, or rebound, these relative activities, thereby suppressing incorrectly classified populations. The rebound mechanism has formal properties analogous to negative afterimages and spatial frequency adaptation.Supported in part by the Advanced Research Projects Agency under ONR Contract No. N00014-76-C-0185     

Signal transduction network motifs and biological memory

Memory is a ubiquitous phenomenon in biological systems, yet the mechanisms responsible for memory, and how to manipulate it at the subcellular level, remain poorly understood. Subjected to transient stimuli, biological systems can exhibit short early responses and/or prolonged (or permanent) late responses. Experimental evidence suggests that early responses (short-term memory) involve post-translational modification of existing proteins and/or their intracellular relocalization, whereas late responses (long-term memory) depend on new protein synthesis. Although this provides an intuitive explanation at the basic molecular level, it does little to clarify the important dynamics that actually maintain memory at the systems level. In this study, we use mathematical modeling to study dynamical mechanisms of biological memory. We first examined the response of four fundamental motifs (positive/negative feedforward and feedback) to external stimuli. Because motifs do not exist in isolation within the cell, we then combined these motifs to form signaling modules to understand how they confer biological memory. These motifs, and different combinations thereof, displayed distinct behavior in response to external stimuli. The principles described in this study have important implications for experimental approaches to identify the mechanisms for biological memory and for the development of therapeutic strategies to modulate signaling network responses in the setting of human disease.     

Stochastic Dynamics Underlying Cognitive Stability and Flexibility

Cognitive stability and flexibility are core functions in the successful pursuit of behavioral goals. While there is evidence for a common frontoparietal network underlying both functions and for a key role of dopamine in the modulation of flexible versus stable behavior, the exact neurocomputational mechanisms underlying those executive functions and their adaptation to environmental demands are still unclear. In this work we study the neurocomputational mechanisms underlying cue based task switching (flexibility) and distractor inhibition (stability) in a paradigm specifically designed to probe both functions. We develop a physiologically plausible, explicit model of neural networks that maintain the currently active task rule in working memory and implement the decision process. We simplify the four-choice decision network to a nonlinear drift-diffusion process that we canonically derive from a generic winner-take-all network model. By fitting our model to the behavioral data of individual subjects, we can reproduce their full behavior in terms of decisions and reaction time distributions in baseline as well as distractor inhibition and switch conditions. Furthermore, we predict the individual hemodynamic response timecourse of the rule-representing network and localize it to a frontoparietal network including the inferior frontal junction area and the intraparietal sulcus, using functional magnetic resonance imaging. This refines the understanding of task-switch-related frontoparietal brain activity as reflecting attractor-like working memory representations of task rules. Finally, we estimate the subject-specific stability of the rule-representing attractor states in terms of the minimal action associated with a transition between different rule states in the phase-space of the fitted models. This stability measure correlates with switching-specific thalamocorticostriatal activation, i.e., with a system associated with flexible working memory updating and dopaminergic modulation of cognitive flexibility. These results show that stochastic dynamical systems can implement the basic computations underlying cognitive stability and flexibility and explain neurobiological bases of individual differences.