Encoding and retrieval in a CA1 microcircuit model of the hippocampus

Recent years have witnessed a dramatic accumulation of knowledge about the morphological, physiological and molecular characteristics, as well as connectivity and synaptic properties of neurons in the mammalian hippocampus. Despite these advances, very little insight has been gained into the computational function of the different neuronal classes; in particular, the role of the various inhibitory interneurons in encoding and retrieval of information remains elusive. Mathematical and computational models of microcircuits play an instrumental role in exploring microcircuit functions and facilitate the dissection of operations performed by diverse inhibitory interneurons. A model of the CA1 microcircuitry is presented using biophysical representations of its major cell types: pyramidal, basket, axo-axonic, bistratified and oriens lacunosummoleculare cells. Computer simulations explore the biophysical mechanisms by which encoding and retrieval of spatio-temporal input patterns are achieved by the CA1 microcircuitry. The model proposes functional roles for the different classes of inhibitory interneurons in the encoding and retrieval cycles

Neuromodulation of the feedforward dentate gyrus-CA3 microcircuit

The feedforward dentate gyrus-CA3 microcircuit in the hippocampus is thought to activate ensembles of CA3 pyramidal cells and interneurons to encode and retrieve episodic memories. The creation of these CA3 ensembles depends on neuromodulatory input and synaptic plasticity within this microcircuit. Here we review the mechanisms by which the neuromodulators aceylcholine, noradrenaline, dopamine, and serotonin reconfigure this microcircuit and thereby infer the net effect of these modulators on the processes of episodic memory encoding and retrieval

Prediction and memory: A predictive coding account

The hippocampus is crucial for episodic memory, but it is also involved in online prediction. Evidence suggests that a unitary hippocampal code underlies both episodic memory and predictive processing, yet within a predictive coding framework the hippocampal-neocortical interactions that accompany these two phenomena are distinct and opposing. Namely, during episodic recall, the hippocampus is thought to exert an excitatory influence on the neocortex, to reinstate activity patterns across cortical circuits. This contrasts with empirical and theoretical work on predictive processing, where descending predictions suppress prediction errors to ‘explain away’ ascending inputs via cortical inhibition. In this hypothesis piece, we attempt to dissolve this previously overlooked dialectic. We consider how the hippocampus may facilitate both prediction and memory, respectively, by inhibiting neocortical prediction errors or increasing their gain. We propose that these distinct processing modes depend upon the neuromodulatory gain (or precision) ascribed to prediction error units. Within this framework, memory recall is cast as arising from fictive prediction errors that furnish training signals to optimise generative models of the world, in the absence of sensory data

Interaction of inhibition and synaptic plasticity in a model of the hippocampal CA1 microcircuit

CNS*2011 : Twentieth Annual Computational Neuroscience Meeting, Stockholm, Sweden, 23-28 July 2011. Poster presentationInformatikos fakultetasTaikomosios informatikos katedraVytauto Didžiojo universiteta

Bio-inspired models of memory capacity, recall performance and theta phase precession

The hippocampus plays an important role in the encoding and retrieval of spatial and non-spatial memories. Much is known about the anatomical, physiological and molecular characteristics as well as the connectivity and synaptic properties of various cell types in the hippocampal circuits [1], but how these detailed properties of individual neurons give rise to the encoding and retrieval of memories remains unclear. Computational models play an instrumental role in providing clues on how these processes may take place. Here, we present three computational models of the region CA1 of the hippocampus at various levels of detail. Issues such as retrieval of memories as a function of cue loading, presentation frequency and learning paradigm, memory capacity, recall performance, and theta phase precession in the presence of dopamine neuromodulation and various types of inhibitory interneurons are addressed. The models lead to a number of experimentally testable predictions that may lead to a better understanding of the biophysical computations in the hippocampus

Quantitative Investigation Of Memory Recall Performance Of A Computational Microcircuit Model Of The Hippocampus

Memory, the process of encoding, storing, and maintaining information over time in order to influence future actions, is very important in our lives. Losing it, it comes with a great cost. Deciphering the biophysical mechanisms leading to recall improvement should thus be of outmost importance. In this study we embarked on the quest to improve computationally the recall performance of a bio-inspired microcircuit model of the mammalian hippocampus, a brain region responsible for the storage and recall of short-term declarative memories. The model consisted of excitatory and inhibitory cells. The cell properties followed closely what is currently known from the experimental neurosciences. Cells’ firing was timed to a theta oscillation paced by two distinct neuronal populations exhibiting highly regular bursting activity, one tightly coupled to the trough and the other to the peak of theta. An excitatory input provided to excitatory cells context and timing information for retrieval of previously stored memory patterns. Inhibition to excitatory cells acted as a non-specific global threshold machine that removed spurious activity during recall. To systematically evaluate the model’s recall performance against stored patterns, pattern overlap, network size and active cells per pattern, we selectively modulated feedforward and feedback excitatory and inhibitory pathways targeting specific excitatory and inhibitory cells. Of the different model variations (modulated pathways) tested, ‘model 1’ recall quality was excellent across all conditions. ‘Model 2’ recall was the worst. The number of ‘active cells’ representing a memory pattern was the determining factor in improving the model’s recall performance regardless of the number of stored patterns and overlap between them. As ‘active cells per pattern’ decreased, the model’s memory capacity increased, interference effects between stored patterns decreased, and recall quality improved

Dynamics and function of a CA1 model of the hippocampus during theta and ripples

The hippocampus is known to be involved in spatial learning in rats. Spatial learning involves the encoding and replay of temporally sequenced spatial information. Temporally sequenced spatial memories are encoded and replayed by the firing rate and phase of pyramidal cells and inhibitory interneurons with respect to ongoing network oscillations (theta and ripples). Understanding how the different hippocampal neuronal classes interact during these encoding and replay processes is of great importance. A computational model of the CA1 microcircuit [3], [4], [5] that uses biophysical representations of the major cell types, including pyramidal cells and four types of inhibitory interneurons is extended to address: (1) How are the encoding and replay (forward and reverse) of behavioural place sequences controlled in the CA1 microcircuit during theta and ripples? and (2) What roles do the various types of inhibitory interneurons play in these processes

Modeling hippocampal theta-coupled gamma oscillations in learning and memory

Two of the most researched domains in the hippocampus are the oscillatory activity and encoding and retrieval of patterns in the hippocampal CA1 and CA3 regions. They are, how- ever, not studied together; and hence, the objective of our work is to study the cross-frequency coupling of theta-coupled gamma oscillations in CA1 and CA3 regions of the hippocampus while encoding and retrieving information. We have studied the cross-frequency coupling of theta-coupled gamma oscillations both individually and in our newly-proposed integrated model of CA1-CA3 to analyze the effects of Schaffer collaterals and CA1 back-projection cells on CA1 and CA3 regions of the hippocampus. Due to lack of literary evidence, we have also contributed our hypotheses about the effects of CA1 back-projection cells on CA1 and CA3 cell-types. Moreover, we have developed a deterministic rule-based cellular automata library to study cross-frequency coupling in single-neuron level and population neuronal net- works at the same time. The discrete model is theta-oscillations-aware and hence encoding and retrieving of patterns takes place during the half-cycles of theta oscillations. We have extended the septo-hippocampal population firing rate model proposed by Den- ham and Borisyuk (2000) to study (i) the influence of inhibitory interneurons, specifically PV-containing basket cells (BCs) and bistratified cells (BSCs) on theta and theta-coupled gamma oscillations in both CA1 and CA3 hippocampal networks; (ii) to study Schaffer col- laterals from CA3 to CA1 and the influence of back-projection cells in CA1 on CA3; (iii) to analyze and compare the phases of cross-frequency coupling of theta-coupled gamma oscil- lations among the different cell types in CA1 and CA3 regions; (iv) to study the influence of external inputs on CA1 and CA3. In our simulations, with constant external inputs, we identify the parameter regions that generate theta oscillations and that BCs and BSCs in CA1 are in anti-phase, as seen experi- mentally by Klausberger et. al (2008). Slow-gamma oscillations are generated due to the ac- tivity of BSCs and BCs in CA1 and CA3, and they are propagated from CA3 to CA1 through the Schaffer collaterals, as seen in Klausberger et. al (2008) where BSCs were observed to synchronize PC activity during theta-coupled gamma oscillations in CA1. In CA3, increas- ing excitation of CA3 pyramidal cells results in theta oscillations without the slow-gamma coupling. Increasing excitatory input to CA1 pyramidal cells results in steady state and de- creasing the excitatory input, results in reduced oscillatory activity in both CA1 and CA3 due to Schaffer collaterals and the feedback projections from CA1 to CA3. This demonstrates that changes in input excitation can move the networks from oscillatory to non-oscillatory states, comparable to the differences seen in animals between exploratory and resting state. Further, Mizuseki et. al (2009) observed experimentally that CA1, CA3 and EC are out- of-theta-phase with each other and that the phase observed in CA1 pyramidal cells are not a result of a simple integration of phases from CA3, EC or the medial septum. We have thus, simulated theta-frequency sine-wave inputs from CA3 and EC of relative phases in the model and observed the same results in our CA1 individual and CA1-CA3 integrated model. To study encoding and retrieval of patterns in an oscillating model, we took an engineer- ing approach by developing a discrete modeling system using cellular automata (CA) derived from the models of Pytte et. al (1991) and Claverol et. al (2002). The aim of this model is to (i) replicate the oscillatory and phasic results obtained using the continuous modeling ap- proach and (ii) extend the same model to study storage and recall of patterns in CA1 taking a theta-oscillations-aware approach. Encoding and retrieval happen at different half-cycles of theta where information pro- cessing takes places in the sub-cycles of the slow-gamma oscillations in each half-cycle of theta oscillation (Cutsuridis et. al, 2010, Hasselmo et. al, 1996). A set of rules is developed to replicate this for the CA model of CA1. The encoding and retrieval half-cycles are identi- fied using the basket cell activity, and hence synaptic learning is enabled during the encoding half-cycle of theta, and is disabled during the recall half-cycle of theta oscillations. This is also a biologically realistic enhancement for studying learning and recall in theta-coupled gamma oscillations using a discrete cellular automata approach

Distributed Encoding of Spatial and Object Categories in Primate Hippocampal Microcircuits

The primate hippocampus plays critical roles in the encoding, representation, categorization and retrieval of cognitive information. Such cognitive abilities may use the transformational input-output properties of hippocampal laminar microcircuitry to generate spatial representations and to categorize features of objects, images, and their numeric characteristics. Four nonhuman primates were trained in a delayed-match-to-sample (DMS) task while multi-neuron activity was simultaneously recorded from the CA1 and CA3 hippocampal cell fields. The results show differential encoding of spatial location and categorization of images presented as relevant stimuli in the task. Individual hippocampal cells encoded visual stimuli only on specific types of trials in which retention of either, the Sample image, or the spatial position of the Sample image indicated at the beginning of the trial, was required. Consistent with such encoding, it was shown that patterned microstimulation applied during Sample image presentation facilitated selection of either Sample image spatial locations or types of images, during the Match phase of the task. These findings support the existence of specific codes for spatial and numeric object representations in primate hippocampus which can be applied on differentially signaled trials. Moreover, the transformational properties of hippocampal microcircuitry, together with the patterned microstimulation are supporting the practical importance of this approach for cognitive enhancement and rehabilitation, needed for memory neuroprosthetics