Modeling the hippocampus : finely controlled memory storage using spiking neurons

The hippocampus, an area in the temporal lobe of the mammalian brain, participates in the storage of personal memories and life events. As such traumatic memories and the consequent symptoms of post-traumatic stress are thought to be stored or at least processedin the hippocampus. While a fundamental understanding of a traumatic memory is still elusive, studying the physiology and functional properties of the hippocampus are anessential first step. Towards that goal, I developed a detailed computational model of the hippocampus. The model included the important effects of the neuromodulator Acetylcholine that switches the hippocampal network between the memory encoding state and the memory retrieval state. In the first study, I examined the mechanisms for controlling runaway excitation in the model. The results indicated different mechanisms for controlling runaway excitation in the memory encoding state as opposed to the memory retrieval state of the circuit. These findings produced the first functionally-based categorization of seizures in animals and humans, and may inspire specific treatments for these types of seizures. The second study examined the underpinnings of the rhythmic activity of the hippocampus. These oscillations in the theta range (4-12 Hz) are theorize to play a major role in the memory functions and in processing sequences of events and actions in both place and time. We found the generation of theta rhythmic activity to be best described as a product of multiple interacting generators. Importantly, we found differences in theta generation depending on the functional state of the hippocampus. Finally, the third study detailed the rules of the complex interactions between these multiple theta generators in the circuit. Our results shed more light on the role of specific components in the hippocampal circuit to maintain its function in both health and disease states

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

Modulation of prefrontal couplings by prior belief-related responses in ventromedial prefrontal cortex

Humans and other animals can maintain constant payoffs in an uncertain environment by steadily re-evaluating and flexibly adjusting current strategy, which largely depends on the interactions between the prefrontal cortex (PFC) and mediodorsal thalamus (MD). While the ventromedial PFC (vmPFC) represents the level of uncertainty (i.e., prior belief about external states), it remains unclear how the brain recruits the PFC-MD network to re-evaluate decision strategy based on the uncertainty. Here, we leverage non-linear dynamic causal modeling on fMRI data to test how prior belief-dependent activity in vmPFC gates the information flow in the PFC-MD network when individuals switch their decision strategy. We show that the prior belief-related responses in vmPFC had a modulatory influence on the connections from dorsolateral PFC (dlPFC) to both, lateral orbitofrontal (lOFC) and MD. Bayesian parameter averaging revealed that only the connection from the dlPFC to lOFC surpassed the significant threshold, which indicates that the weaker the prior belief, the less was the inhibitory influence of the vmPFC on the strength of effective connections from dlPFC to lOFC. These findings suggest that the vmPFC acts as a gatekeeper for the recruitment of processing resources to re-evaluate the decision strategy in situations of high uncertainty

Thalamus: a brain-inspired algorithm for biologically-plausible continual learning and disentangled representations

Animals thrive in a constantly changing environment and leverage the temporal structure to learn well-factorized causal representations. In contrast, traditional neural networks suffer from forgetting in changing environments and many methods have been proposed to limit forgetting with different trade-offs. Inspired by the brain thalamocortical circuit, we introduce a simple algorithm that uses optimization at inference time to generate internal representations of the current task dynamically. The algorithm alternates between updating the model weights and a latent task embedding, allowing the agent to parse the stream of temporal experience into discrete events and organize learning about them. On a continual learning benchmark, it achieves competitive end average accuracy by mitigating forgetting, but importantly, by requiring the model to adapt through latent updates, it organizes knowledge into flexible structures with a cognitive interface to control them. Tasks later in the sequence can be solved through knowledge transfer as they become reachable within the well-factorized latent space. The algorithm meets many of the desiderata of an ideal continually learning agent in open-ended environments, and its simplicity suggests fundamental computations in circuits with abundant feedback control loops such as the thalamocortical circuits in the brain.Comment: Published ICLR 202

An integrative model of the intrinsic hippocampal theta rhythm.

Hippocampal theta oscillations (4-12 Hz) are consistently recorded during memory tasks and spatial navigation. Despite several known circuits and structures that generate hippocampal theta locally in vitro, none of them were found to be critical in vivo, and the hippocampal theta rhythm is severely attenuated by disruption of external input from medial septum or entorhinal cortex. We investigated these discrepancies that question the sufficiency and robustness of hippocampal theta generation using a biophysical spiking network model of the CA3 region of the hippocampus that included an interconnected network of pyramidal cells, inhibitory basket cells (BC) and oriens-lacunosum moleculare (OLM) cells. The model was developed by matching biological data characterizing neuronal firing patterns, synaptic dynamics, short-term synaptic plasticity, neuromodulatory inputs, and the three-dimensional organization of the hippocampus. The model generated theta power robustly through five cooperating generators: spiking oscillations of pyramidal cells, recurrent connections between them, slow-firing interneurons and pyramidal cells subnetwork, the fast-spiking interneurons and pyramidal cells subnetwork, and non-rhythmic structured external input from entorhinal cortex to CA3. We used the modeling framework to quantify the relative contributions of each of these generators to theta power, across different cholinergic states. The largest contribution to theta power was that of the divergent input from the entorhinal cortex to CA3, despite being constrained to random Poisson activity. We found that the low cholinergic states engaged the recurrent connections in generating theta activity, whereas high cholinergic states utilized the OLM-pyramidal subnetwork. These findings revealed that theta might be generated differently across cholinergic states, and demonstrated a direct link between specific theta generators and neuromodulatory states

Episodic memory supports the acquisition of structured task representations

Generalization to new tasks requires learning of task representations that accurately reflect the similarity structure of the task space. Here, we argue that episodic memory (EM) plays an essential role in this process by stabilizing task representations, thereby supporting the accumulation of structured knowledge. We demonstrate this using a neural network model that infers task representations that minimize the current task's objective function; crucially, the model can retrieve previously encoded task representations from EM and use these to initialize the task inference process. With EM, the model succeeds in learning the underlying task structure; without EM, task representations drift and the network fails to learn the structure. We further show that EM errors can support structure learning by promoting the activation of similar task representations in tasks with similar sensory inputs. Overall, this model provides a novel account of how EM supports the acquisition of structured task representations

Role of sensory input distribution and intrinsic connectivity in lateral amygdala during auditory fear conditioning: A computational study

We propose a novel reduced order neuronal network modeling framework that includes an enhanced firing rate model and a corresponding synaptic calcium-based synaptic learning rule. Specifically, we propose enhancements to the Wilson-Cowan firing rate neuron model that permits full spike frequency adaptation seen in biological LA neurons, while being sufficiently general to accommodate other spike frequency patterns. We also report a technique to incorporate calcium-dependent plasticity in the synapses of the network using a regression scheme to link firing rate to postsynaptic calcium. Together, the single cell model and the synaptic learning scheme constitute a general framework to develop computationally efficient neuronal networks that employ biologically-realistic synaptic learning. The reduced order modeling framework was validated using a previously reported biophysical conductance-based neuronal network model of a rodent lateral amygdala (LA) that modeled features of Pavlovian conditioning and extinction of auditory fear (Li et al., 2009). The framework was then used to develop a larger LA network model to investigate the roles of tone and shock distributions and of intrinsic connectivity in auditory fear learning. The model suggested combinations of tone and shock densities that would provide experimental estimates of tone responsive and conditioned cell proportions. Furthermore, it provided several insights including how intrinsic connectivity might help distribute sensory inputs to produce conditioned responses in cells that do not directly receive both tone and shock inputs, and how a balance between potentiation of excitation and inhibition prevents stimulus generalization during fear learning

Intrinsic mechanisms stabilize encoding and retrieval circuits differentially in a hippocampal network model

Acetylcholine regulates memory encoding and retrieval by inducing the hippocampus to switch between pattern separation and pattern completion modes. However, both processes can introduce significant variations in the level of network activity and potentially cause a seizure-like spread of excitation. Thus, mechanisms that keep network excitation within certain bounds are necessary to prevent such instability. We developed a biologically realistic computational model of the hippocampus to investigate potential intrinsic mechanisms that might stabilize the network dynamics during encoding and retrieval. The model was developed by matching experimental data, including neuronal behavior, synaptic current dynamics, network spatial connectivity patterns, and short-term synaptic plasticity. Furthermore, it was constrained to perform pattern completion and separation under the effects of acetylcholine. The model was then used to investigate the role of short-term synaptic depression at the recurrent synapses in CA3, and inhibition by basket cell (BC) interneurons and oriens lacunosum-moleculare (OLM) interneurons in stabilizing these processes. Results showed that when CA3 was considered in isolation, inhibition solely by BCs was not sufficient to control instability. However, both inhibition by OLM cells and short-term depression at the recurrent CA3 connections stabilized the network activity. In the larger network including the dentate gyrus, the model suggested that OLM inhibition could control the network during high cholinergic levels while depressing synapses at the recurrent CA3 connections were important during low cholinergic states. Our results demonstrate that short-term plasticity is a critical property of the network that enhances its robustness. Furthermore, simulations suggested that the low and high cholinergic states can each produce runaway excitation through unique mechanisms and different pathologies. Future studies aimed at elucidating the circuit mechanisms of epilepsy could benefit from considering the two modulatory states separately