Models for Evaluating Sequentail Memories in a Recurrent Neuronal Network
Final Report Abstract
The hippocampus is a brain structure crucially involved in the formation of episodic and autobiographic memories. In rodent models, hippocampal neurons replay previously experienced activity sequences. Such replay predominantly occurs in association with the field potential phenomenon of sharp-wave ripple complexes. In this project we studied the synaptic mechanisms underlying sharp-wave ripple complexes in vitro and elaborated on theoretical neuronal network models of sequence replay. The analysis of the physiological recordings revealed that excitatory synaptic currents arrive at CA1 pyramidal cells phase-locked to the fast (~200 Hz) ripple oscillation. This reflects synchronous activity assemblies of pyramidal cells (or their axons). In addition to excitatory currents, we also found that the inhibition arrives at ripple frequency, however, its phase drifts during the course of the sharp wave relative to the excitatory PSCs. This relative phase drift constitutes a dynamical activity control mechanism that probably accounts for the termination of the sharp wave. In our modelling work we extended a previous model of sequence replay in that we included feedback inhibition. We could show that the optimal feedback is a linear correction of the firing threshold and that, if one also allows inhibition to be dynamic, replay coincides with oscillations on the time scale of a sharp wave. A further result of this project was the development of a network model (similar to the previous one) which allowed to elucidate the possible role of synaptic tagging in memory consolidation. The synaptic tagging hypothesis assumes that a plasticity stimulus initially sets the synapse in a transient potentiated or depotentiated state that decays if the initial learning is not followed by a consolidating stimulus. The latter acts only on synapses that have been tagged by the first stimulus and make those synapses more stable. We could show that the tagging idea is a feasible mechanism to evaluate memories in a network model and to most efficiently use the synaptic resources of the network such that only the few synapses that are necessary to retrieve the memories are in stable states, whereas the majority of synapses is relatively easily changeable thus making the network highly plastic and the memories stable at the same time.
Publications
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Inhibition Enhances Memory Capacity: Optimal Feedback, Transient Replay and Oscillations. J Comput Neurosci (online first)
Kammerer A; Tejero-Cantero A; Leibold C
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(2011) Coherent phasic excitation during hippocampal ripples. Neuron 72:137-152
Maier N; Tejero-Cantero A; Dorrn A; Winterer J; Beed P; Morris G; Kempter R; Poulet JF; Leibold C; Schmitz D
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(2011) Synaptic Tagging, Evaluation of Memories, and the Distal Reward Problem. Learn Mem 18: 58-70
Päpper M; Kempter R; Leibold C
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(2012) Of Memories and Ripples: Functional and mechanistic Aspects of Memory Sequences During Hippocampal Ripples. PhD Dissertation at the Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München
Tejero Cantero, A
