The human brain is a complex, self-organizing system where neurons transmit information via ion channels and communicate through synapses. Mathematical models have helped researchers explore how local population dynamics and brain network topology influence overall brain activity and synaptic/structural neuroplasticity as well as the brain’s ability to reorganize in response to various stimuli. This research aims to create a large-scale spiking neural network (SNN) model to investigate mechanisms of memory formation, storage, and retrieval, focusing on the hippocampus. Using a Hodgkin-Huxley-based model, the study will explore dopamine's effects on synaptic plasticity and test various connectivity patterns to find optimal configurations for memory processes. Additionally, the project will computationally assess the impact of emotional states on the excitation-inhibition balance in hippocampal circuits, aiming to enhance our understanding of neural mechanisms underlying memory.

The human brain is a complex, self-organizing system. Researchers aim to understand its core principles to improve brain models, explore disease neurobiology, and develop treatments. Neurons, the brain's basic units, transmit information via voltage-gated ion channels and communicate through chemical and electrical synapses. Neuronal networks generate brain functions such as perception, thought, and memory. Pathological activity patterns, linked to disorders like epilepsy and migraines, often result from genetic mutations affecting ion channels and synaptic function. [Russell et al. (2013) Annu. Rev. Neurosci. 36:25]. Neuronal activity in individual nodes is typically modelled by a system of ordinary differential equations. Such types of mathematical models provide a theoretical framework for investigating the impact of the localized population dynamics combined with the complex topology of the brain network on the overall brain activity [Deco et al. (2008) PLoS Comput Biol 4:e1000092; Sanz-Leon et al. (2015) NeuroImage 111:385) and its dynamics [Popovych et al. (2021) NeuroImage 236:118201; Manos et al. (2023) Front. Comput. Neurosci. 17:295395; Courson et al. (2024) Front. Comput. Neurosci. 18:1360009].
Neuroplasticity, also known as neural or brain plasticity, refers to the brain’s ability to adapt and reorganize in response to factors such as learning, environmental changes, practice, or stress [Voss et al. (2017) Front. Psychol. 8:1657]. This adaptability occurs through both synaptic plasticity, where neurons modify synaptic transmission, and structural plasticity, involving the formation or retraction of neural connections and changes in cell morphology [Kehayas & Holtmaat (2017) The Rewiring Brain 3-26]. In [Manos et al. (2021) Front. Physiol. 12:716556] the authors computationally explored long-term changes in structural plasticity triggered by external stimulation to optimize stimulation dosage while in [Anil et al. (2023) PLoS Comput. Biol. 19:e1011027] the authors simulated a recurrent neural network with homeostatic structural plasticity, revealing its importance in stimulation protocols.
Recent studies have underscored the hippocampus's susceptibility to neuroplastic changes in neurodegenerative diseases, impacting cognitive and emotional functions [Weerasinghe et al. (2022) Int. J. Mol. Sci. 23:3349]. Synaptic structural alterations, crucial for learning and memory, have been observed under high-resolution time-lapse imaging [Ma and Ta (2022) Semin. Cell Biol. 125:84]. In the prefrontal cortex, spiking activity patterns supporting cognitive tasks like working memory can be learned and replayed using biologically realistic plasticity rules [Sarazin et al. (2021) Front Neural Circuits 15:648538]. Models of hippocampal circuits, including CA1, CA3, and DG regions, provide insights into long-term memory mechanisms [Chua and Tan (2017) AAAI Tech. Rep. SS-17-07]. Additionally, silent cell assemblies contribute to memory persistence, influenced by neuromodulation that balances synaptic connectivity [Gallinaro et al. (2021) PLoS Comput. Biol. 17:e1009593; Fuchsberger & Paulsen (2022) Curr. Opin. Neurol. 75:102558; Tesler et al. (2024) Front Comput Neurosci. 18, 1432593].
The hippocampus has been recognised as a predominant brain structure for the consolidation of new information from short-term to long-term memory [O’Keefe and Dostrovsky (1971) Brain Res. 34:171]. Many studies have been conducted since, given the increasing evidence of the importance of hippocampal cell units for self-localisation and information recall. The current consensus is that the hippocampus displays a particular activity pattern, called sharp wave ripples (SWR), at a frequency of 150-200 Hz, which is temporally compressed with respect to the timescale of the neural activity happening during the real spatial experience, and thus enhances spike-timing dependent plasticity (STDP) [Dan and Poo (2004) Neuron 44:23]. Hippocampal SWR could encode temporally structured spatial patterns and drive the initial storage and the later retrieval of relevant experience [Pfeiffer (2020) Hipp. 30:6]. Models of memory formation, consolidation, and retrieval have been proposed at different levels of brain abstraction, from decision-making [Cazé et al. (2018) Journ. Neurophys. 120:2877] to spiking neural networks (SNN) [Tan et al. (2013) IEEE CIS], tested on artificial agents [Massi et al. (2022) Front. Neurorob. 16: 864380] and compared to animals’ neural recordings [Mattar and Daw (2018) Nat Neurosci. 21:1609]. Exogenous stimuli and the subsequent emotions play an important role in this, by modulating the activity of brain areas such as the amygdala, the prefrontal cortex and the hippocampus [Tyng et al. (2017) Front.Psychol. 8:1454]. In animal and human behavior, the two most studied conditioning stimuli concern sustenance and survival and they can be of opposite emotional strength, with two different neural circuits involved in the process [Yacubian et al. (2006) J Neurosci 26:9530].
From a decision-making and reinforcement learning (RL) perspective, the question was addressed recently in [Bryzgalov, Massi, et al., in preparation], and preliminary results show that higher learning rate and more intense replay activity are needed while learning to avoid than learning to approach. Brain circuits for encoding aversive and appetitive stimuli can be considered as orthogonal in the brain [Tye (2018) Neuron 100:436], but the same neurotransmitters, such as dopamine, could be implied in the learning process. In fact, the release of dopamine is linked to unexpected reward signals during learning, but it is shown that it can be linked to unexpected punishment too [Sands et al. (2023) Sci. Adv. 9:eadi4927]. Dopamine but also other neurotransmitters, has been proved to enhance synaptic consolidation and recall, but the precise dynamics of this mechanism have just started to be investigated [Lehr et al. (2022) Sci. Rep. 12.1:17772]. The dopaminergic influence in a SNN model of the striatum has been modeled for RL tasks [González-Redondo et al. (2023) Neurocomp. 548:126377] and, it has also been implemented as the joint action of short-latency excitation and long-latency inhibition for the modulation of both STDP and neuronal excitability [Chorley and Anil (2011) Front.Comp.Neuro. 5:21]. Finally, [Bono et al. (2023) Elife, 12:e80671] have used a mechanism inspired by hippocampal replay to learn successor representations and cognitive maps in a SNN.
The Research Objectives (ROs) of this proposal can be summarized as follows:
- RO1: Large scale SNN setup and implementation of synaptic/structural plasticity rules and neuromodulation features: Theoretical and computational understanding of the relevant combined relevant mechanisms for memory formation, storage and retrieval.
- RO2: Study the effects in the neural excitation-inhibition balance of positive and negative delivered emotional states in the hippocampus large scale SNN.
We will use a Hodgkin-Huxley-based spiking neuron model that supports a wide range of firing patterns, including tonic spiking, bursting, and seizure-like events [Bandyopadhyay et al. (2021) J. Comput. Neurosci. 50:33]. Such a SNN model allows us to implement and investigate dopamine effects via voltage-gated calcium and potassium channels while will allow us to design large neural networks to explore various structural and synaptic connectivity patterns, aiming to identify configurations that favor memory formation and retrieval. More details can be found in the “Feasibility of the project in 3 years - Specify the steps” section.
In summary, this research proposal will allow us to build a large-scale SNN to (i) explore different mechanisms associated with optimal memory storage and retrieval and (ii) computationally investigate the role of neural excitation-inhibition balance in these processes when positive and negative emotional states are delivered in the hippocampus.