Long-term depression in rat CA1-subicular synapses depends on the G-protein coupled mACh receptors

The subiculum, which is the primary target of CA1 pyramidal neurons and sending efferent fibres to many brain regions, serves as a hippocampal interface in the neural information processes between hippocampal formation and neocortex. Long-term depression (LTD) is extensively studied in the hippocampus, but not at the CA1-subicular synaptic transmission. Using whole-cell EPSC recordings in the brain slices of young rats, we demonstrated that the pairing protocols of low frequency stimulation (LFS) at 3 Hz and postsynaptic depolarization of -50 mVelicited a reliable LTD in the subiculum. The LTD did not cause the changes of the paired-pulse ratio of EPSC. Furthermore, it did not depend on either NMDA receptors or voltage-gated calcium channels (VGCCs). Bath application of the G-protein coupled muscarinic acetylcholine receptors (mAChRs) antagonists, atropine or scopolamine, blocked the LTD, suggesting that mAChRs are involved in the LTD. It was also completely blocked by either the Ca2+ chelator BAPTA or the G-protein inhibitor GDP-beta-S in the intracellular solution. This type of LTD in the subiculum may play a particular role in the neural information processing between the hippocampus and neocortex. (c) 2005 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Stress evoked by opiate withdrawal facilitates hippocampal LTP in vivo

Stress impairs hippocampal long-term potentiation (LTP), but it is unknown whether the stress evoked by opiate withdrawal has the same effect. Here the authors report that opiate withdrawal for 4 days does not influence basal synaptic transmission, but re

Enriched environment experience overcomes the memory deficits and depressive-like behavior induced by early life stress

Stress in early life is believed to cause cognitive and affective disorders, and to disrupt hippocampal synaptic plasticity in adolescence into adult, but it is unclear whether exposure to enriched environment (EE) can overcome these effects. Here, we rep

Glycine receptor activation regulates short-term plasticity in CA1 area of hippocampal slices of rats

Functional glycine receptors (GlyRs) are enriched in the hippocampus, but their roles in synaptic transmission are unclear. In this study, we examined the effect of GlyR activation on paired-pulse stimulation of the whole-cell postsynaptic currents (PSCs)

Morphine withdrawal affects both delayed-escape behaviour in Morris water maze and hippocampal NR2A/2B expression ratio

Repeated low-dose morphine treatment facilitates delayed-escape behaviour of hippocampus-dependent Morris water maze and morphine withdrawal influences hippocampal NMDA receptor-dependent synaptic plasticity. Here, we examined whether and how morphine wit

GABA Transporter-1 Activity Modulates Hippocampal Theta Oscillation and Theta Burst Stimulation-Induced Long-Term Potentiation

The network oscillation and synaptic plasticity are known to be regulated by GABAergic inhibition, but how they are affected by changes in the GABA transporter activity remains unclear. Here we show that in the CA1 region of mouse hippocampus, pharmacolog

Chronic Morphine Treatment Impaired Hippocampal Long-Term Potentiation and Spatial Memory via Accumulation of Extracellular Adenosine Acting on Adenosine A(1) Receptors

Chronic exposure to opiates impairs hippocampal long-term potentiation (LTP) and spatial memory, but the underlying mechanisms remain to be elucidated. Given the well known effects of adenosine, an important neuromodulator, on hippocampal neuronal excitability and synaptic plasticity, we investigated the potential effect of changes in adenosine concentrations on chronic morphine treatment-induced impairment of hippocampal CA1 LTP and spatial memory. We found that chronic treatment in mice with either increasing doses (20-100 mg/kg) of morphine for 7 d or equal daily dose (20 mg/kg) of morphine for 12 d led to a significant increase of hippocampal extracellular adenosine concentrations. Importantly, we found that accumulated adenosine contributed to the inhibition of the hippocampal CA1 LTP and impairment of spatial memory retrieval measured in the Morris water maze. Adenosine A(1) receptor antagonist 8-cyclopentyl-1,3-dipropylxanthine significantly reversed chronic morphine-induced impairment of hippocampal CA1 LTP and spatial memory. Likewise, adenosine deaminase, which converts adenosine into the inactive metabolite inosine, restored impaired hippocampal CA1 LTP. We further found that adenosine accumulation was attributable to the alteration of adenosine uptake but not adenosine metabolisms. Bidirectional nucleoside transporters (ENT2) appeared to play a key role in the reduction of adenosine uptake. Changes in PKC-alpha/beta activity were correlated with the attenuation of the ENT2 function in the short-term (2 h) but not in the long-term (7 d) period after the termination of morphine treatment. This study reveals a potential mechanism by which chronic exposure to morphine leads to impairment of both hippocampal LTP and spatial memory.

Study on the reproductive biology of Pinctada fucata (Gould) (Mollusca: Pteriidae)

Annual cycle of gonad development and spawning in pearl oyster, Pinctada ficata (Gould) in Nakhiloo, Northeast Persian Gulf, was investigated over two years from August 1994 to June 1996. Gonadal condition was assessed by staging criteria to describe gametogenic development from histological preparations of randomly collected individuals of all sizes. A bimodal gametogenic pattern with summer and autumn spawning periods was evident throughout the study. Gametogensis commenced in November-December which proceeded by major gonadal maturation during February-April. Summer spawning was observed from April to July with major spawning at the latter end. During spawning peak in July, low level of gametogensis was noticed. Gametogenic activity was picked up again in August-September which proceeded by autumn spawning from September to December. Towards the end of spawning season, incidence of gonadal inactivation increased. Minimum level of gonadal activity was observed in November. Temperature regime appears to have influential role in regulation of gametogenic and spawning processes. Gonadal development and spawning trends were similar in both sexes. P. radiaata was found to be protandrous hermaphrodite which matured as a male at shell height greater than 20 mm. Biseivality was uncommon and the sex ratio was about 1:1. Ultrastructure of gametes were investigated in the Pictada fucata (Gould). "Auxiliary cells" closely accociated with developing oocytes were observed. Each oocyte seems to be associated with only one secretory cell. which is characterized by an abundant rough endoplasmic reticulum at the onset of vitellogenesis. Contact between this cell and a developing oocytes is maintained by a desmosome-like junction which can be observed when the vitelline coat is formed. these "auxiliary or nursing cells" seem to play a tropic role in vitellogenesis, and may be involved in the formation of the vitelline coat of the oocytes. Oocytic degeneration is observed in this species, it is a continuous phenomenon of varing intensity throughout the year. The ultrastructural changes resulting in lysis of the oocyte are described. Mature spermatozoa consist of a broad, cap-shaped acrosomal vesicle, subacrosomal material, a round nucleus, two triplet substructure centrioles surrounded by four spherical mitochondria, and a flagellum anchored to the distal centriole and plasma membrane. Spermatozoa of Plucata closley resemble to those of other investigated Pteriidae. Changes in proximate composition of soft tissue and gonadal cycle of Pinctada fucata was studied. Mobilization and utilization of stored reserves are apparent during gametogenesis and gonadal maturation. Protein reserves are utilized during spermatogenesis while reserved carbohydrates form the main energy donor in oogenesis. The role of lipid as am.: energy reserve is second to that of carbohydrate.

Perceptual learning, roving and the unsupervised bias

Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when . certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervised neural network models can cope easily with roving conditions. What makes humans so inferior compared to these models? As experimental and conceptual work has shown, human perceptual learning is neither supervised nor unsupervised but reward-based learning. Reward-based learning suffers from the so-called unsupervised bias, i.e., to prevent synaptic " drift" , the . average reward has to be exactly estimated. However, this is impossible when two or more stimulus types with different rewards are presented during training (and the reward is estimated by a running average). For this reason, we propose no learning occurs in roving conditions. However, roving hinders perceptual learning only for combinations of similar stimulus types but not for dissimilar ones. In this latter case, we propose that a critic can estimate the reward for each stimulus type separately. One implication of our analysis is that the critic cannot be located in the visual system. © 2011 Elsevier Ltd.

Active dendrites: Adaptation to spike-based communication

Computational analyses of dendritic computations often assume stationary inputs to neurons, ignoring the pulsatile nature of spike-based communication between neurons and the moment-to-moment fluctuations caused by such spiking inputs. Conversely, circuit computations with spiking neurons are usually formalized without regard to the rich nonlinear nature of dendritic processing. Here we address the computational challenge faced by neurons that compute and represent analogue quantities but communicate with digital spikes, and show that reliable computation of even purely linear functions of inputs can require the interplay of strongly nonlinear subunits within the postsynaptic dendritic tree.Our theory predicts a matching of dendritic nonlinearities and synaptic weight distributions to the joint statistics of presynaptic inputs. This approach suggests normative roles for some puzzling forms of nonlinear dendritic dynamics and plasticity.

Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans.

Theories of instrumental learning are centred on understanding how success and failure are used to improve future decisions. These theories highlight a central role for reward prediction errors in updating the values associated with available actions. In animals, substantial evidence indicates that the neurotransmitter dopamine might have a key function in this type of learning, through its ability to modulate cortico-striatal synaptic efficacy. However, no direct evidence links dopamine, striatal activity and behavioural choice in humans. Here we show that, during instrumental learning, the magnitude of reward prediction error expressed in the striatum is modulated by the administration of drugs enhancing (3,4-dihydroxy-L-phenylalanine; L-DOPA) or reducing (haloperidol) dopaminergic function. Accordingly, subjects treated with L-DOPA have a greater propensity to choose the most rewarding action relative to subjects treated with haloperidol. Furthermore, incorporating the magnitude of the prediction errors into a standard action-value learning algorithm accurately reproduced subjects' behavioural choices under the different drug conditions. We conclude that dopamine-dependent modulation of striatal activity can account for how the human brain uses reward prediction errors to improve future decisions.

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks

Cortical neurons receive balanced excitatory and inhibitory synaptic currents. Such a balance could be established and maintained in an experience-dependent manner by synaptic plasticity at inhibitory synapses. We show that this mechanism provides an explanation for the sparse firing patterns observed in response to natural stimuli and fits well with a recently observed interaction of excitatory and inhibitory receptive field plasticity. The introduction of inhibitory plasticity in suitable recurrent networks provides a homeostatic mechanism that leads to asynchronous irregular network states. Further, it can accommodate synaptic memories with activity patterns that become indiscernible from the background state but can be reactivated by external stimuli. Our results suggest an essential role of inhibitory plasticity in the formation and maintenance of functional cortical circuitry.

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.

Code-specific policy gradient rules for spiking neurons

Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning rules emerge from a policy gradient approach depending on which features of the spike trains are assumed to influence the reward signals, i.e., depending on which neural code is in effect. We use the framework of Williams (1992) to derive learning rules for arbitrary neural codes. For illustration, we present policy-gradient rules for three different example codes - a spike count code, a spike timing code and the most general "full spike train" code - and test them on simple model problems. In addition to classical synaptic learning, we derive learning rules for intrinsic parameters that control the excitability of the neuron. The spike count learning rule has structural similarities with established Bienenstock-Cooper-Munro rules. If the distribution of the relevant spike train features belongs to the natural exponential family, the learning rules have a characteristic shape that raises interesting prediction problems.