Synaptic plasticity and memory addressing in biological and artificial neural networks

Biological brains are composed of neurons, interconnected by synapses to create large complex networks. Learning and memory occur, in large part, due to synaptic plasticity -- modifications in the efficacy of information transmission through these synaptic connections. Artificial neural networks model these with neural "units" which communicate through synaptic weights. Models of learning and memory propose synaptic plasticity rules that describe and predict the weight modifications. An equally important but under-evaluated question is the selection of \textit{which} synapses should be updated in response to a memory event. In this work, we attempt to separate the questions of synaptic plasticity from that of memory addressing. Chapter 1 provides an overview of the problem of memory addressing and a summary of the solutions that have been considered in computational neuroscience and artificial intelligence, as well as those that may exist in biology. Chapter 2 presents in detail a solution to memory addressing and synaptic plasticity in the context of familiarity detection, suggesting strong feedforward weights and anti-Hebbian plasticity as the respective mechanisms. Chapter 3 proposes a model of recall, with storage performed by addressing through local third factors and neo-Hebbian plasticity, and retrieval by content-based addressing. In Chapter 4, we consider the problem of concurrent memory consolidation and memorization. Both storage and retrieval are performed by content-based addressing, but the plasticity rule itself is implemented by gradient descent, modulated according to whether an item should be stored in a distributed manner or memorized verbatim. However, the classical method for computing gradients in recurrent neural networks, backpropagation through time, is generally considered unbiological. In Chapter 5 we suggest a more realistic implementation through an approximation of recurrent backpropagation. Taken together, these results propose a number of potential mechanisms for memory storage and retrieval, each of which separates the mechanism of synaptic updating -- plasticity -- from that of synapse selection -- addressing. Explicit studies of memory addressing may find applications not only in artificial intelligence but also in biology. In artificial networks, for example, selectively updating memories in large language models can help improve user privacy and security. In biological ones, understanding memory addressing can help with health outcomes and treating memory-based illnesses such as Alzheimers or PTSD

Meta-learning Hebbian plasticity for continual familiarity detection

Memories are stored and recalled throughout the lifetime of an animal, but many models of memory, including previous models of familiarity detection, do not operate in a continuous manner. We consider a family of models that recognize previously experienced stimuli and, importantly, operate and learn continuously. Specifically, we investigate a learning paradigm in which stimuli are presented in a streaming fashion with repetitions at various intervals, and the subject/model must report whether the current stimulus has previously appeared in the stream. We propose a feedforward network architecture with ongoing plasticity in the synaptic weight matrix. Parameters governing plasticity and static network parameters are meta-learned using gradient descent to optimize the continual familiarity detection process. This architecture, unlike recurrent networks without ongoing plasticity, generalizes easily over a range of repeat intervals even if trained with a single interval. We show that an anti-Hebbian plasticity rule (co-activated neurons cause synaptic depression) enables repeat detection over much longer intervals than a Hebbian one, and this is the solution most readily found by meta-learning. This rule leads to experimentally observed features such as repeat suppression in the hidden layer neurons. In contrast to previous theoretical work, the capacity of these networks remains constant across their lifetimes, meaning that pairs of stimuli with a given temporal separation are stored and recognized as familiar independent of the network's input history. We also consider learning rules that use an external gating circuit to control plasticity. Collectively, these models demonstrate a range of different psychometric curves that we compare to human performance. Keywords: learning, memory, recognition, familiarity, novelty detection, meta-learning, Hebbian, synaptic plasticit

Memory consolidation facilitated by burst-driven late-phase plasticity

peer reviewedHow do alternating periods of learning and rest contribute to memory consolidation? While it is recognized that learning relies on synaptic plasticity triggered by the spiking activity correlation between neurons, the role of rest periods and their biophysical mechanisms remain elusive. In this work, we leverage the interaction between the brain state fluctuations, reflecting changes in neuronal excitability, and memory, relying on synaptic plasticity occurring at different phases. Our approach involves a neural network model capable of transitioning between learning periods characterized by fast low-amplitude oscillations, and rest periods marked by slower large- amplitude oscillations. At the neuronal level, it is characterized by biophysical neurons capable of switching between input-driven tonic firing and the less-explored collective bursting. In our model, synapses exhibit calcium-based early-phase plasticity, as studied in previous work. Here, we propose a new additional burst-induced late-phase plasticity mechanism. During learning, the early-phase plasticity forms new memories, as traditionally observed. During rest, the early-phase plasticity resets, returning to its baseline set point. It provides a physiological trace to drive the late-phase plasticity facilitating memory consolidation. Validating our model through a memory task utilizing the MNIST dataset, we demonstrate that switching from tonic to burst, combined with early- and late-phase plasticity enables the network to acquire new information while preserving existing memories. The collective bursting activity during rest, combined with late-phase plasticity, represents the generation of new postsynaptic proteins and morphological synapse changes (termed structural plasticity). We find that substituting rest with an additional learning period impedes memory consolidation, rendering it susceptible to noise. These findings propose a potential biological mechanism for unsupervised memory consolidation during rest and explain how the brain balances synaptic homeostasis and memory processes. Moreover, they suggest the utility of incorporating rest periods into machine learning models, highlighting the importance of including collective bursting and structural plasticity.3. Good health and well-bein

Biological learning in key-value memory networks

In neuroscience, classical Hopfield networks are the standard biologically plausible model of long-term memory, relying on Hebbian plasticity for storage and attractor dynamics for recall. In contrast, memory-augmented neural networks in machine learning commonly use a key-value mechanism to store and read out memories in a single step. Such augmented networks achieve impressive feats of memory compared to traditional variants, yet it remains unclear whether they can be implemented by biological systems. In our work, we bridge this gap by proposing a set of of biologically plausible three-factor plasticity rules for a basic feedforward key-value memory network. Keys are stored in the input-to-hidden synaptic weights by a "non-Hebbian" rule, controlled only by pre-synaptic activity, and modulated by local third factors which represent dendtritic spikes. Values are stored in the hidden-to-output weights by a Hebbian rule, with the pre-synaptic neuron selected through softmax attention which represents recurrent inhibition. The same rules are recovered when network parameters are meta-learned. Our network performs on par with classical Hopfield networks on autoassociative memory tasks and can be naturally extended to correlated inputs, continual recall, heteroassociative memory, and sequence learning. Importantly, since memories are stored in slots indexed by hidden layer neurons, unlike the fully distributed representation in the classical Hopfield network, they can be individually selected for extended storage or rapid decay. Finally, our memory network can easily be incorporated into a larger neural system, either as a memory bank for an external controller, or as a fast learning system used in conjunction with a slow one. Overall, our results suggest a compelling alternative to the classical Hopfield network as a model of biological long-term memory. Keywords: learning, memory, synaptic plasticity, Hebbian, key-value memory, neural network, three-factor plasticit

Time-domain diffuse correlation spectroscopy

Physiological monitoring of oxygen delivery to the brain has great significance for improving the management of patients at risk for brain injury. Diffuse correlation spectroscopy (DCS) is a rapidly growing optical technology able to non-invasively assess the blood flow index (BFi) at the bedside. The current limitations of DCS are the contamination introduced by extracerebral tissue and the need to know the tissue's optical properties to correctly quantify the BFi. To overcome these limitations, we have developed a new technology for time-resolved diffuse correlation spectroscopy. By operating DCS in the time domain (TD-DCS), we are able to simultaneously acquire the temporal point-spread function to quantify tissue optical properties and the autocorrelation function to quantify the BFi. More importantly, by applying time-gated strategies to the DCS autocorrelation functions, we are able to differentiate between short and long photon paths through the tissue and determine the BFi for different depths. Here, we present the novel device and we report the first experiments in tissue-like phantoms and in rodents. The TD-DCS method opens many possibilities for improved non-invasive monitoring of oxygen delivery in humans

Time-domain diffuse correlation spectroscopy: instrument prototype, preliminary measurements, and theoretical modeling

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 86-91).Near-infrared spectroscopy (NIRS) is an emerging diffuse optical imaging tool with both clinical and academic applications such as functional brain imaging, breast cancer detection, and cerebral health monitoring. Due to its non-invasiveness, high spatial and temporal resolution, and portability, it has been rapidly growing in popularity over the last 40 years. The technique relies on near-infrared light to measure optical properties { scattering and absorption { which can then be used to infer details of the underlying tissue physiology. Diffuse correlation spectroscopy (DCS) is a complimentary optical technique that relies on long-coherence laser light, also in the near-infrared range, to measure dynamical properties of a medium { in the biomedical context, blood ow. While NIRS and DCS can be used in conjunction to provide even more powerful information, they require separate instrumentation, resulting in reduced portability and difficulty in bedside monitoring. In brain imaging applications, both NIRS and DCS suer from confounds due to layers surrounding the brain, such as the scalp and skull. While this issue has been addressed in NIRS using time-resolved instrumentation known as time-domain (TD) NIRS, it has been largely ignored in the context of DCS. In this work, we demonstrate a novel time-domain diffuse correlation spectroscopy (TD-DCS) technique embodied in a single instrument capable of simultaneously measuring optical and dynamical properties. Along with maintaining portability, the instrument reduces error by directly measuring the absorption and scattering values necessary for precise ow estimation, and removes a major confounding factor by suppressing unwanted signal from superficial layers through time-gating. We describe the construction of the first instrument prototype and demonstrate the depth resolution proof-of-concept with measurements of multi-layer media. We further discuss the theoretical considerations of modeling the light interaction with tissue, necessary for reliable estimates.by Danil Tyulmankov.M. Eng

Periodic flashing coordinated reset stimulation paradigm reduces sensitivity to ON and OFF period durations.

Pathological synchronization in the basal ganglia network has been considered an important component of Parkinson's disease pathophysiology. An established treatment for some patients with Parkinson's disease is deep brain stimulation, in which a tonic high-frequency pulse train is delivered to target regions of the brain. In recent years, a novel neuromodulation paradigm called coordinated reset stimulation has been proposed, which aims to reverse the pathological synchrony by sequentially delivering short high-frequency bursts to distinct sub-regions of the pathologically synchronized network, with an average intra-burst interval for each sub-region corresponding to period of the pathological oscillation. It has further been proposed that the resultant desynchronization can be enhanced when stimulation is interrupted periodically, and that it is particularly beneficial to precisely tune the stimulation ON and OFF time-windows to the underlying pathological frequency. Pre-clinical and clinical studies of coordinated reset stimulation have relied on these proposals for their stimulation protocols. In this study, we present a modified ON-OFF coordinated reset stimulation paradigm called periodic flashing and study its behavior through computational modeling using the Kuramoto coupled phase oscillator model. We demonstrate that in contrast to conventional coordinated reset stimulation, the periodic flashing variation does not exhibit a need for precise turning of the ON-OFF periods to the pathological frequency, and demonstrates desynchronization for a wide range of ON and OFF periods. We provide a mechanistic explanation for the previously observed sensitivities and demonstrate that they are an artifact of the specific ON-OFF cycling paradigm used. As a practical consequence, the periodic flashing paradigm simplifies the tuning of optimal stimulation parameters by decreasing the dimension of the search space. It also suggests new, more flexible ways of delivering coordinated reset stimulation

Memory consolidation through combined burst-induced homeostatic reset and structural plasticity

editorial reviewedNeurons adapt their connections with each other through synaptic plasticity, driven by correlations in their spiking activity (Fig. 1A). Additionally, neuronal networks undergo global changes in rhythmic activity that correspond to different brain states, defined by switches in neuronal activity and orchestrated by neuromodulators. A well-known example is the transition from a state of learning during active waking to rest during quiet waking, which corresponds to a switch in neuronal activity from tonic firing to bursting (Fig. 1B). This raises the question of how switching from tonic firing to bursting affects the outcome of synaptic plasticity and whether it can support memory consolidation. Recently, we have shown for a variety of synaptic plasticity models that bursting leads to a homeostatic reset, in which synaptic efficacy returns to a fixed baseline value irrespective of the starting point. This homeostatic reset causes the network to forget any learned information [1]. To address this issue, we propose an additional structural plasticity mechanism in which short-term changes in synaptic efficacy – evolving according to traditional plasticity rules – drive long-lasting morphological changes such as spine growth or insertion of new AMPA receptors. While synaptic efficacy undergoes homeostatic reset during bursting, information is consolidated through structural plasticity on a longer timescale. We demonstrate the utility of this mechanism in a network of neurons using a conductance-based neuronal model that can switch from tonic firing to bursting along with a calcium-based synaptic rule to drive changes in synaptic efficacy. We investigate three regimes of switches in neuronal activity and plasticity mechanisms, denoted S1, S2, S3 (Fig. 1C). In S1, as a control condition, tonic firing is interleaved with periods of neuronal inactivity – mimicking bursting blockers – and a traditional plasticity rule, while in S2, tonic firing is separated by periods of bursting, leading to homeostatic reset in synaptic efficacy. Configuration S3 is identical as S2 but also includes our proposed burst-driven structural plasticity. In our first memory task (Fig. 1D), we show that the signal-to-noise (SNR) is improved over repeated switches only in S3. In a simple pattern recognition task (Fig. 1E), blocking bursting activity (S1) makes the network fragile to noise, and blocking structural plasticity during bursting leads to complete forgetting (S2), neither of which occurs in S3. Finally, in a MNIST recognition task (Fig. 1F), we confirm that memory consolidation occurs with S3 by showing a stronger receptive field that consolidates during switches from tonic to burst and is robust to noise. In this work, we shed light on the under-investigated role of switches in neuronal firing patterns for synaptic plasticity. Traditional plasticity rules result in a burst-induced homeostatic reset of synaptic efficacy, which is incompatible with memory consolidation. Our burst-driven structural plasticity proposes a solution to this problem, bridging the gap between switches in tonic firing to bursting, learning, and memory consolidation, and suggesting new ways to improve machine learning algorithms. References [1] Jacquerie K, Minne C, Ponnet J, et al. Switches to slow rhythmic neuronal activity lead to a plasticity-induced reset in synaptic weights. preprint, Biorxiv (2022)

Switching from tonic firing to bursting: implications on learning and memory

editorial reviewedThe brain's ability to learn and remember information relies on synaptic plasticity, the process by which neurons change the strength of their connections in response to their spiking activity. In parallel, the switch between different brain states, such as from active to quiet wakefulness, involves a transition in neuronal activity from tonic firing to bursting. It raises questions about how switches between different states affect synaptic plasticity and memory consolidation. Recent research has revealed that bursting leads to a homeostatic reset of synaptic efficacy, where synaptic efficacy returns to a baseline value regardless of its starting point, causing the network to forget any learned information. To address this issue, we propose a new mechanism called burst-driven structural plasticity, that combines early changes in synaptic efficacy with long-lasting morphological changes such as spine growth or new protein synthesis. Using a conductance-based neuronal model with a calcium-based plasticity rule, we demonstrate the utility of the proposed mechanism in a network that learns to recognize hand-written digits from the MNIST dataset. Our results show that the combination of switching from tonic firing to bursting with structural plasticity improves memory consolidation and enhances network robustness to noise. Conversely, blocking bursting and its burst-driven structural plasticity leads to forgetting. In conclusion, this study highlights the importance of firing patterns in synaptic plasticity and proposes a solution to bridge the gap between the switches from tonic firing to bursting, learning and memory consolidation