Two dynamic processes for the induction of long-term potentiation in hippocampal CA1 neurons

A context-sensitive mechanism in hippocampal CA1 networks

This paper presents a possible context-sensitive mechanism in a neural network and at single neuron levels based on the experiments of hippocampal CA1 and their theoretical models. First, the spatiotemporal learning rule (STLR, non-Hebbian) and the Hebbian rule (HEBB) are experimentally shown to coexist in dendrite-soma interactions in single hippocampal pyramidal cells of CA1. Second, the functional differences between STLR and HEBB are theoretically shown in pattern separation and pattern completion. Third, the interaction between STLR and HEBB in neural levels is proposed to play an important role in forming a selective context determined by value information, which is related to expected reward and behavioral estimation.

Dual synaptic plasticity in the hippocampus: Hebbian and spatiotemporal learning dynamics

We assume that Hebbian learning dynamics (HLD) and spatiotemporal learning dynamics (SLD) are involved in the mechanism of synaptic plasticity in the hippocampal neurons. While HLD is driven by pre- and postsynaptic spike timings through the backpropagating action potential, SLD is evoked by presynaptic spike timings alone. Since the backpropagation attenuates as it nears the distal dendrites, we assume an extreme case as a neuron model where HLD exists only at proximal dendrites and SLD exists only at the distal dendrites. We examined how the synaptic weights change in response to three types of synaptic inputs in computer simulations. First, in response to a Poisson train having a constant mean frequency, the synaptic weights in HLD and SLD are qualitatively similar. Second, SLD responds more rapidly than HLD to synchronous input patterns, while each responds to them. Third, HLD responds more rapidly to more frequent inputs, while SLD shows fluctuating synaptic weights. These results suggest an encoding hypothesis in that a transient synchronous structure in spatiotemporal input patterns will be encoded into distal dendrites through SLD and that persistent synchrony or firing rate information will be encoded into proximal dendrites through HLD.

Spatial clustering property and its self-similarity in membrane potentials of hippocampal CA1 pyramidal neurons for a spatio-temporal input sequence

To clarify how the information of spatiotemporal sequence of the hippocampal CA3 affects the postsynaptic membrane potentials of single pyramidal cells in the hippocampal CA1, the spatio-temporal stimuli was delivered to Schaffer collaterals of the CA3 through a pair of electrodes and the post-synaptic membrane potentials were recorded using the patch-clamp recording method. The input-output relations were sequentially analyzed by applying two measures; "spatial clustering" and its "self-similarity" index. The membrane potentials were hierarchically clustered in a self-similar manner to the input sequences. The property was significantly observed at two and three time-history steps. In addition, the properties were maintained under two different stimulus conditions, weak and strong current stimulation. The experimental results are discussed in relation to theoretical results of Cantor coding, reported by Tsuda (Behav Brain Sci 24(5):793-847, 2001) and Tsuda and Kuroda (Jpn J Indust Appl Math 18:249-258, 2001; Cortical dynamics, pp 129-139, Springer-Verlag, 2004).

Interaction between the Spatiotemporal Learning Rule (STLR) and Hebb type (HEBB) in single pyramidal cells in the hippocampal CA1 Area

The spatiotemporal learning rule (STLR), proposed as a non-Hebb type by Tsukada et al. (Neural Networks 9 (1996) 1357 and Tsukada and Pan (Biol. cyberm 92 (2005) 139), 2005), consists of two distinctive factors; "cooperative plasticity without a cell spike," and "its temporal summation". On the other hand, Hebb (The organization of behavior. John Wiley, New York, 1949) proposed the idea (HEBB) that synaptic modification is strengthened only if the pre- and post-cell are activated simultaneously. We have shown, experimentally, that both STLR and HEBB coexist in single pyramidal cells of the hippocampal CA1 area. The functional differences between STLR and HEBB in dendrite (local)-soma (global) interactions in single pyramidal cells of CA1 and the possibility of pattern separation, pattern completion and reinforcement learning were discussed.

A model of the hippocampal-cortical memory system

Based on physiological evidence, we propose a theoretical model of the hippocampal-cortical memory system. The model consists of the following components: the sensory system, the hippocampus (short-term memory), and the association cortex (long-term memory). A series of key codes (local information) is supplied from the sensory system, while context (global information) is inputted from the hippocampus. The two inputs interact dynamically in the association cortex. The interactive neurons work as a detector of coincidence. The cortical network learns the memory information through the coincidence window and, finally, stores it in the form of attractors. This local-global information works as an addressor to designate the stored location of the memory in the association cortex and accelerates the process of storing and retrieving memory information.

Spatiotemporal visualization of long-term potentiation and depression in the hippocampal CA1 area

Long-term potentiation (LTP) in the CA1 area of the hippocampus depends critically on the statistical characteristics of its stimulus. The ability of optical imaging to record spatial distribution has made it possible to examine systematically the effect of higher-order statistical characteristics, such as the correlation between successive pairs of inter-stimulus intervals (ISIs) on the induction of LTP. Therefore, the function of frequency (first-order) and temporal pattern (second-order) was examined using this imaging technique. To investigate the dependence of LTP on frequency, periodic stimuli with the same number of pulses were applied at different frequencies (1-10 Hz, n=200) to Schaffer commissural-collateral fibers. While stimulus frequencies from 2-10 Hz induced LTP of varying magnitudes and low-frequency stimuli (1 Hz) induced long-term depression (LTD), spatial distribution remained consistent. These results suggest that induction frequency has a greater effect on the magnitude of LTP than on its spatial distribution. By employing nonperiodic stimuli at the same mean frequency (2 Hz), the effect of varying the temporal structure of a stimulus was also investigated. As the correlation of successive ISIs was increased from negative to positive, not only did the magnitude of LTP increase, there was also a statistically significant change in the spatial distribution of LTP. Interestingly, when a strong negatively correlated stimulus was applied, both LTP and LTD were simultaneously observed in the CA1 area. It was also found that the magnitude of LTP 200-300 mum distal to the cellular layer was larger than that of the LTP induced proximal (<100 microm) to that layer. These results support the hypothesis that the spatial distribution of LTP throughout the hippocampus relies principally on the temporal patterning of input stimulation. This insight into the structure of the CA1 neural network may reveal the importance of stimulus timing events in the spatial encoding of memories.

The spatiotemporal learning rule and its efficiency in separating spatiotemporal patterns

The hippocampus plays an important role in the course of establishing long-term memory, i.e., to make short-term memory of spatially and temporally associated input information. In 1996 (Tsukada et al. 1996), the spatiotemporal learning rule was proposed based on differences observed in hippocampal long-term potentiation (LTP) induced by various spatiotemporal pattern stimuli. One essential point of this learning rule is that the change of synaptic weight depends on both spatial coincidence and the temporal summation of input pulses. We applied this rule to a single-layered neural network and compared its ability to separate spatiotemporal patterns with that of other rules, including the Hebbian learning rule and its extended rules. The simulated results showed that the spatiotemporal learning rule had the highest efficiency in discriminating spatiotemporal pattern sequences, while the Hebbian learning rule (including its extended rules) was sensitive to differences in spatial patterns.