A Calcium-Dependent Plasticity Rule for HCN Channels Maintains Activity Homeostasis and Stable Synaptic Learning

Theoretical and computational frameworks for synaptic plasticity and learning have a long and cherished history, with few parallels within the well-established literature for plasticity of voltage-gated ion channels. In this study, we derive rules for plasticity in the hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, and assess the synergy between synaptic and HCN channel plasticity in establishing stability during synaptic learning. To do this, we employ a conductance-based model for the hippocampal pyramidal neuron, and incorporate synaptic plasticity through the well-established Bienenstock-Cooper-Munro (BCM)-like rule for synaptic plasticity, wherein the direction and strength of the plasticity is dependent on the concentration of calcium influx. Under this framework, we derive a rule for HCN channel plasticity to establish homeostasis in synaptically-driven firing rate, and incorporate such plasticity into our model. In demonstrating that this rule for HCN channel plasticity helps maintain firing rate homeostasis after bidirectional synaptic plasticity, we observe a linear relationship between synaptic plasticity and HCN channel plasticity for maintaining firing rate homeostasis. Motivated by this linear relationship, we derive a calcium-dependent rule for HCN-channel plasticity, and demonstrate that firing rate homeostasis is maintained in the face of synaptic plasticity when moderate and high levels of cytosolic calcium influx induced depression and potentiation of the HCN-channel conductance, respectively. Additionally, we show that such synergy between synaptic and HCN-channel plasticity enhances the stability of synaptic learning through metaplasticity in the BCM-like synaptic plasticity profile. Finally, we demonstrate that the synergistic interaction between synaptic and HCN-channel plasticity preserves robustness of information transfer across the neuron under a rate-coding schema. Our results establish specific physiological roles for experimentally observed plasticity in HCN channels accompanying synaptic plasticity in hippocampal neurons, and uncover potential links between HCN-channel plasticity and calcium influx, dynamic gain control and stable synaptic learning.

Functional requirements for reward-modulated spike-timing-dependent plasticity.

Recent experiments have shown that spike-timing-dependent plasticity is influenced by neuromodulation. We derive theoretical conditions for successful learning of reward-related behavior for a large class of learning rules where Hebbian synaptic plasticity is conditioned on a global modulatory factor signaling reward. We show that all learning rules in this class can be separated into a term that captures the covariance of neuronal firing and reward and a second term that presents the influence of unsupervised learning. The unsupervised term, which is, in general, detrimental for reward-based learning, can be suppressed if the neuromodulatory signal encodes the difference between the reward and the expected reward-but only if the expected reward is calculated for each task and stimulus separately. If several tasks are to be learned simultaneously, the nervous system needs an internal critic that is able to predict the expected reward for arbitrary stimuli. We show that, with a critic, reward-modulated spike-timing-dependent plasticity is capable of learning motor trajectories with a temporal resolution of tens of milliseconds. The relation to temporal difference learning, the relevance of block-based learning paradigms, and the limitations of learning with a critic are discussed.

Slowness: an objective for spike-timing-dependent plasticity?

Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the fact that object identities typically vary more slowly in time than contextual variables or noise. Here, we study the question of how this "temporal stability" or "slowness" approach can be implemented within the limits of biologically realistic spike-based learning rules. We first show that slow feature analysis, an algorithm that is based on slowness, can be implemented in linear continuous model neurons by means of a modified Hebbian learning rule. This approach provides a link to the trace rule, which is another implementation of slowness learning. Then, we show analytically that for linear Poisson neurons, slowness learning can be implemented by spike-timing-dependent plasticity (STDP) with a specific learning window. By studying the learning dynamics of STDP, we show that for functional interpretations of STDP, it is not the learning window alone that is relevant but rather the convolution of the learning window with the postsynaptic potential. We then derive STDP learning windows that implement slow feature analysis and the "trace rule." The resulting learning windows are compatible with physiological data both in shape and timescale. Moreover, our analysis shows that the learning window can be split into two functionally different components that are sensitive to reversible and irreversible aspects of the input statistics, respectively. The theory indicates that irreversible input statistics are not in favor of stable weight distributions but may generate oscillatory weight dynamics. Our analysis offers a novel interpretation for the functional role of STDP in physiological neurons.

A Spiking Neural Network Model of the Medial Superior Olive using Spike Timing Dependent Plasticity for Sound Localisation

Sound localization can be defined as the ability to identify the position of an input sound source and is considered a powerful aspect of mammalian perception. For low frequency sounds, i.e., in the range 270 Hz-1.5 KHz, the mammalian auditory pathway achieves this by extracting the Interaural Time Difference between sound signals being received by the left and right ear. This processing is performed in a region of the brain known as the Medial Superior Olive (MSO). This paper presents a Spiking Neural Network (SNN) based model of the MSO. The network model is trained using the Spike Timing Dependent Plasticity learning rule using experimentally observed Head Related Transfer Function data in an adult domestic cat. The results presented demonstrate how the proposed SNN model is able to perform sound localization with an accuracy of 91.82% when an error tolerance of +/-10 degrees is used. For angular resolutions down to 2.5 degrees , it will be demonstrated how software based simulations of the model incur significant computation times. The paper thus also addresses preliminary implementation on a Field Programmable Gate Array based hardware platform to accelerate system performance.

Photoperiod-dependent plasticity of circadian pacemaker center in the brain of the Madeira cockroach Rhyparobia maderae

To various degrees, insects in nature adapt to and live with two fundamental environmental rhythms around them: (1) the daily rhythm of light and dark, and (2) the yearly seasonal rhythm of the changing photoperiod (length of light per day). It is hypothesized that two biological clocks evolved in organisms on earth which allow them to harmonize successfully with the two environmental rhythms: (1) the circadian clock, which orchestrates circadian rhythms in physiology and behavior, and (2) the photoperiodic clock, which allows for physiological adaptations to changes in photoperiod during the course of the year (insect photoperiodism). The circadian rhythm is endogenous and continues in constant conditions, while photoperiodism requires specific light inputs of a minimal duration. Output pathways from both clocks control neurosecretory cells which regulate growth and reproduction. This dissertation focuses on the question whether different photoperiods change the network and physiology of the circadian clock of an originally equatorial cockroach species. It is assumed that photoperiod-dependent plasticity of the cockroach circadian clock allows for adaptations in physiology and behavior without the need for a separate photoperiodic clock circuit. The Madeira cockroach Rhyparobia maderae is a well established circadian clock model system. Lesion and transplantation studies identified the accessory medulla (aMe), a small neuropil with about 250 neurons, as the cockroach circadian pacemaker. Among them, the pigment-dispersing factor immunoreactive (PDF-ir) neurons anterior to the aMe (aPDFMes) play a key role as inputs to and outputs of the circadian clock system. The aim of my doctoral thesis was to examine whether and how different photoperiods modify the circadian clock system. With immunocytochemical studies, three-dimensional (3D) reconstruction, standardization and Ca2+-imaging technique, my studies revealed that raising cockroaches in different photoperiods changed the neuronal network of the circadian clock (Wei and Stengl, 2011). In addition, different photoperiods affected the physiology of single, isolated circadian pacemaker neurons. This thesis provides new evidence for the involvement of the circadian clock in insect photoperiodism. The data suggest that the circadian pacemaker system of the Madeira cockroach has the plasticity and potential to allow for physiological adaptations to different photoperiods. Therefore, it may express also properties of a photoperiodic clock.

Spine Ca2+ signaling in spike-timing-dependent plasticity

Calcium is a second messenger, which can trigger the modification of synaptic efficacy. We investigated the question of whether a differential rise in postsynaptic Ca2+ ([Ca2+]i) alone is sufficient to account for the induction of long-term potentiation (LTP) and long-term depression (LTD) of EPSPs in the basal dendrites of layer 2/3 pyramidal neurons of the somatosensory cortex. Volume-averaged [Ca2+]i transients were measured in spines of the basal dendritic arbor for spike-timing-dependent plasticity induction protocols. The rise in [Ca2+]i was uncorrelated to the direction of the change in synaptic efficacy, because several pairing protocols evoked similar spine [Ca2+]i transients but resulted in either LTP or LTD. The sequence dependence of near-coincident presynaptic and postsynaptic activity on the direction of changes in synaptic strength suggested that LTP and LTD were induced by two processes, which were controlled separately by postsynaptic [Ca2+]i levels. Activation of voltage-dependent Ca2+ channels before metabotropic glutamate receptors (mGluRs) resulted in the phospholipase C-dependent (PLC-dependent) synthesis of endocannabinoids, which acted as a retrograde messenger to induce LTD. LTP required a large [Ca2+]i transient evoked by NMDA receptor activation. Blocking mGluRs abolished the induction of LTD and uncovered the Ca2+-dependent induction of LTP. We conclude that the volume-averaged peak elevation of [Ca2+]i in spines of layer 2/3 pyramids determines the magnitude of long-term changes in synaptic efficacy. The direction of the change is controlled, however, via a mGluR-coupled signaling cascade. mGluRs act in conjunction with PLC as sequence-sensitive coincidence detectors when postsynaptic precede presynaptic action potentials to induce LTD. Thus presumably two different Ca2+ sensors in spines control the induction of spike-timing-dependent synaptic plasticity.

Effects of somatosensory stimulation on use-dependent plasticity in chronic stroke

BACKGROUND AND PURPOSE: There is a need to develop strategies to enhance the beneficial effects of motor training, including use-dependent plasticity (UDP), in neurorehabilitation. Peripheral nerve stimulation (PNS) modulates motor cortical excitability in healthy humans and could influence training effects in stroke patients. METHODS: We compared the ability of PNS applied to the (1) arm, (2) leg, and (3) idle time to influence training effects in the paretic hand in 7 chronic stroke patients. The end point measure was the magnitude of UDP. RESULTS: UDP was more prominent with arm stimulation (increased by 22.8%) than with idle time (by 2.9%) or leg stimulation (by 6.4%). CONCLUSIONS: PNS applied to the paretic limb paired with motor training enhances training effects on cortical plasticity in stroke patients.

Spike timing dependent plasticity: a consequence of more fundamental learning rules.

Spike timing dependent plasticity (STDP) is a phenomenon in which the precise timing of spikes affects the sign and magnitude of changes in synaptic strength. STDP is often interpreted as the comprehensive learning rule for a synapse - the "first law" of synaptic plasticity. This interpretation is made explicit in theoretical models in which the total plasticity produced by complex spike patterns results from a superposition of the effects of all spike pairs. Although such models are appealing for their simplicity, they can fail dramatically. For example, the measured single-spike learning rule between hippocampal CA3 and CA1 pyramidal neurons does not predict the existence of long-term potentiation one of the best-known forms of synaptic plasticity. Layers of complexity have been added to the basic STDP model to repair predictive failures, but they have been outstripped by experimental data. We propose an alternate first law: neural activity triggers changes in key biochemical intermediates, which act as a more direct trigger of plasticity mechanisms. One particularly successful model uses intracellular calcium as the intermediate and can account for many observed properties of bidirectional plasticity. In this formulation, STDP is not itself the basis for explaining other forms of plasticity, but is instead a consequence of changes in the biochemical intermediate, calcium. Eventually a mechanism-based framework for learning rules should include other messengers, discrete change at individual synapses, spread of plasticity among neighboring synapses, and priming of hidden processes that change a synapse's susceptibility to future change. Mechanism-based models provide a rich framework for the computational representation of synaptic plasticity.

Learning -dependent plasticity of movement representations in the primate primary motor cortex

Primary motor cortex (M1) is involved in the production of voluntary movement and contains a complete functional representation, or map, of the skeletal musculature. This functional map can be altered by pathological experiences, such as peripheral nerve injury or stroke, by pharmacological manipulation, and by behavioral experience. The process by which experience-dependent alterations of cortical function occur is termed plasticity. In this thesis, plasticity of M1 functional organization as a consequence of behavioral experience was examined in adult primates (squirrel monkeys). Maps of movement representations were derived under anesthesia using intracortical microstimulation, whereby a microelectrode was inserted into the cortex to electrically stimulate corticospinal neurons at low current levels and evoke movements of the forelimb, principally of the hand. Movement representations were examined before and at several times after training on behavioral tasks that emphasized use of the fingers. Two behavioral tasks were utilized that dissociated the repetition of motor activity from the acquisition of motor skills. One task was easy to perform, and as such promoted repetitive motor activity without learning. The other task was more difficult, requiring the acquisition of motor skills for successful performance. Kinematic analysis indicated that monkeys used a consistent set of forelimb movements during pellet extractions. Functional mapping revealed that repetitive motor activity during the easier task did not produce plastic changes in movement representations. Instead, map plasticity, in the form of selective expansions of task-related movement representations, was only produced following skill acquisition on the difficult task. Additional studies revealed that, in general, map plasticity persisted without further training for up to three months, in parallel with the retention of task-related motor skills. Also, extensive additional training on the small well task produced further improvements in performance, and further changes in movement maps. In sum, these experiments support the following three conclusions regarding the role of M1 in motor learning. First, behaviorally-driven plasticity is learning-dependent, not activity-dependent. Second, plastic changes in M1 functional representations represent a neural correlate of acquired motor skills. Third, the persistence of map plasticity suggests that M1 is part of the neural substrate for the memory of motor skills. ^

Columnar distribution of serotonin-dependent plasticity within kitten striate cortex

Recent studies have identified the potential for an important role for serotonin (5-HT) receptors in the developmental plasticity of the kitten visual cortex. 5-HT2C receptors are transiently expressed in a patchy fashion in the visual cortex of kittens between 30–80 days of age complementary to patches demarcated by cytochrome oxidase staining. 5-HT, operating via 5-HT2C receptors, increases cortical synaptic plasticity as assessed both in brain slices and in vivo. Herein, we report that bath application of 5-HT substantially increases the probability of long-term potentiation within 5-HT2C receptor-rich zones of cortex, but this effect is not observed in the 5-HT2C receptor-poor zones. Instead, in these zones, 5-HT application increases the probability of long-term depression. These location-specific effects of 5-HT may promote the formation of compartment-specific cortical responses.