Project B-T4 - Cellular Integration of Hippocampal Sharp-Wave Ripple Currents
In mammals, neuronal representations of space are most prominent in the hippocampal formation. It is both the site of an allocentric space representation (the cognitive map) (O'Keefe and Nadel, 1971) and the brain region that is critically involved in the formation of episodic autobiographic memories. These two functions are generally linked by assuming memory episodes to be built from sequences of activity patterns that would correspond to spatial trajectories of an animal running through an environment. This idea is now well-established and supported by many studies in freely moving rodents.
Classically, hippocampal activity sequences have been analyzed that occur mostly during sharp waves but independently of the movement of the animal, and therefore are more generally referred to as “offline” sequences (Buhry et al., 2011). The functional purpose of these sequences is unclear. Initially, they were thought to be linked with memory consolidation (Mölle et al., 2009). In fact, disrupting awake sharp-wave ripples in rats was shown to indeed impair memory performance (Jadhav et al., 2012). The finding of other sequence orders (backward replay, Foster and Wilson, 2006; preplay, Diba and Buzsaki, 2007; preplay of yet unknown trajectories, Dragoi and Tonegawa, 2011) or non-memory like contents, however, agrees little with the memory consolidation hypothesis. Therefore, as a different hypothesis it was proposed that these offline sequences are required to prepare the hippocampus for novel experiences (Mehta, 2007; Dragoi and Tonegawa, 2011). To further elucidate the functional role of hippocampal offline sequences, we require an in-depth understanding of the biophysical mechanisms generating them. In this project we plan to test one possible candidate mechanism of hippocampal sequence generation by a combination of theoretical modeling and in-vitro electrophysiology.
The theoretical part of this proposal aims at building a dynamical model of network in which many sequences are stored in the recurrent synaptic connections. Network mechanisms studied in in-vitro models of sharp-wave ripples (Maier et al., 2011) will be the starting point to make such a model physiologically plausible. In the experimental part, we will perform in-vitro recordings from mouse CA1 pyramidal cells, in which we try to emulate the synaptic conductances from Maier et al. 2011 by dynamic clamp and electrical and/or optical stimulation. These experiments will allow us to test model predictions on the integration of these synaptic currents by the cell membrane. Together the project will show whether the hypothesis of ripples reflecting synaptically stored activity sequences is plausible taking into account the biophysics of the local neuronal network.
Work program
Theoretical Part: Dynamics of a Sequence-Replaying Network. Here, we aim to develop a continuous-time model of sequence replay during the sharp-wave ripple state. Constructing such a model requires solving several severe problems. One is how action potentials can be generated in neurons during a high-conductance state, as it is present for pyramidal cells in sharp waves: Spike generation in a high-conductance state is difficult because the voltage amplitude linearly scales with input resistance. The massive bombardment particularly by somatic inhibitory synaptic inputs (~80% of inhibitory synapses are at somatic or proximal regions; Megias et al., 2001) dramatically reduces the input resistance and therefore makes the soma barely excitable). Another problem to be solved in constructing a physiologically plausible replay model is to find out under which constraints the discrete-time models (Leibold and Kempter, 2006; Kammerer et al., 2013) can be realized with biophysically realistic neuron models: In the replay model proposed in Leibold and Kempter (2006) the discrete time-steps are assumed to reflect cycles of the ripple oscillation.
Experimental Part: Dynamic-Clamp Recordings in hippocampal CA1 Pyramidal Neurons. We will use dynamic-clamp stimulation to mimic the inhibitory synaptic conductances at the soma of mouse CA1 pyramidal cells. The inhibitory conductance stimuli will be derived from the input statistics obtained in Maier et al. (2011). Excitatory synaptic inputs will be stimulated from afferent fibers in the stratum oriens to mimic the recurrent excitatory inputs in CA1.
In a first step we will quantify the stimulated, pharmacologically isolated excitatory currents/potentials. Then we will combine the afferent fiber and dynamic clamp stimulation. These experiments will show whether the str. oriens axon collaterals can exert control over the inhibitory somatic oscillation. In case the fiber stimulation generates too small excitatory drive we will repeat the experiments using optogenetic activation of the excitatory inputs.
References
Buhry L, Azizi AH, Cheng S. (2011) Reactivation, replay, and preplay: how it might all fit together. Neural Plast. 2011:203462.
Dragoi G, Tonegawa S. (2011) Preplay of future place cell sequences by hippocampal cellular assemblies. Nature 469:397-401.
Diba K, Buzsáki G. (2007) Forward and reverse hippocampal place-cell sequences during ripples. Nat Neurosci. 10:1241-2.
Foster DJ, Wilson MA. (2006) Reverse replay of behavioural sequences in hippocampal place cells during the awake state. Nature. 440:680-3.
Jadhav SP, Kemere C, German PW, Frank LM. (2012) Awake hippocampal sharp-wave ripples support spatial memory. Science. 336:1454-8.
Kammerer A, Tejero-Cantero Á, Leibold C.(2013) Inhibition enhances memory capacity: optimal feedback, transient replay and oscillations. J Comput Neurosci. 34:125-36.
Lee AK, Wilson MA. (2002) Memory of sequential experience in the hippocampus during slow wave sleep. Neuron 36:1183-94.
Leibold C, Kempter R. (2006) Memory capacity for sequences in a recurrent network with biological constraints. Neural Comput. 18:904-41.
Maier N, Tejero-Cantero Á, Dorrn AL, Winterer J, Beed PS, Morris G, Kempter R, Poulet JF, Leibold C, Schmitz D. (2011) Coherent phasic excitation during hippocampal ripples. Neuron 72:137-52.
Megías M, Emri Z, Freund TF, Gulyás AI. (2001) Total number and distribution of inhibitory and excitatory synapses on hippocampal CA1 pyramidal cells. Neuroscience 102:527-40.
Mehta MR. (2007) Cortico-hippocampal interaction during up-down states and memory consolidation. Nat Neurosci. 10:13-5.
Mölle M, Eschenko O, Gais S, Sara SJ, Born J. (2009) The influence of learning on sleep slow oscillations and associated spindles and ripples in humans and rats. Eur J Neurosci. 29:1071-81.
O'Keefe J, Nadel L (1978) The Hippocampus as a Cognitive Map, Oxford University Press.
Willshaw DJ, Buneman OP, Longuet-Higgins HC. (1969) Non-holographic associative memory. Nature 222:960-2.
