Synaptic weights

Computational neuroscience increasingly leverages gradient descent (GD) for training artificial neural network (ANN) models of the brain. GD’s advantage is that it is very effective at learning difficult tasks. This is important for the role of computational models as the interface between theory and empirical data (Levenstein et al., 2023), since models that can learn realistic tasks from realistic, high-dimensional inputs can be more directly compared to experimental data (Doerig et al., 2023). However, GD produces ANNs that are poor phenomenological fits to biology, making them less relevan

Animals learn complex cognitive maps to navigate through physical environments or reason about abstract concepts. These maps are believed to be encoded by mammalian hippocampal grid and place cells, forming low-dimensional neural manifolds that support flexible navigation. However, beyond simple line and ring attractors, how the brain represents and navigates more complex neural manifolds remains unclear. In this study, we propose a solution for representing arbitrary nonlinear neural manifolds by approximating them with piecewise linear spaces composed of simplexes, known as “simplicial compl

Although memories are largely thought to be encoded in the brain via modifications to synaptic weights, technical and conceptual issues with this view (e.g., the large disparity between the time scale of synaptic turnover and long-term human memories) lead some to hypothesize that intracellular processes like gene regulation and post-translational modifications support complementary memory storage mechanisms. While this possibility is appealing, it lacks the mature theoretical apparatus associated with canonical theories of neural network memory storage and recall. To ameliorate this issue, we

Understanding the functional consequences of synaptic heterogeneity within neuronal populations is a critical challenge in neuroscience. Recent connectomic reconstructions, such as the complete fly brain connectome, provide key insights into the structural constraints on neural networks. One prominent observation from these datasets is the variability in synaptic connection strengths among functionally similar neurons, where certain neurons exhibit significantly more synapses than others. However, the role of this heterogeneity in neural function remains unclear. To address this, we present a

Memories are thought to be stored in synapses and retrieved through the reactivation of neural ensembles. However, neural representations exhibit significant dynamics even in stable environments, a phenomenon called representational drift. Theoretical studies suggest that multiple neural representations can correspond to a memory, with post-learning exploration of these representation solutions driving drift. However, the functional implications of drift-explored representations versus those initially learned remain poorly understood. Here we show that drift uncovers robust, sparsely engaged r

…of learning these delays along with synaptic weights, both in terms of accuracy and memory efficiency. However, these approaches suffer from drawbacks in terms of precision and learning efficiency, as they operate in discrete time and with approximate gradients, while also requiring membrane potential recordings for calculating para

Spiking neural networks (SNNs) are characterized by low power consumption, highly parallel computation and event-driven information processing [1]. Many of the concepts that promise to improve the efficiency of SNNs compared to conventional deep neural networks, such as spikes, sparsity, and static suppression, are inspired by our understanding of how the brain processes information. Hence, we can further advance the development of SNNs by improving our current knowledge of how biological neural networks learn through spike-based local plasticity mechanisms. In recent years, many spike-based

Background: Many animals use celestial cues to determine their heading. In the insect brain, the central complex is crucial for spatial orientation. In desert locusts and fruit flies, compass neurons in the central complex encode the animal’s heading relative to the sun [2, 5]. However, the neural computations underlying the transformations of visual inputs to a compass signal remain elusive. In fruit flies, it is hypothesised that visual information is relayed from tangential neu- rons to compass neurons through likely inhibitory all-to-all connections with plastic synapses [1]. We aim to exp

…that the genetic code determines synaptic connection probabilities between neurons based on their genetic types and spatial distances. We propose that these connection probabilities, rather than individual synaptic weight

Biological neural network models with synaptic plasticity pose challenges for both theoretical and numerical analyses. The numerical bottleneck arises from the $N^2$ scaling of synapses. Additionally, the intricate coupling between neuron and synapse dynamics, along with plasticity-induced heterogeneity, hinders the use of classical tools from theory. One approach to address these challenges has been to assume plasticity to be very slow compared to neural activity and leverage slow-fast theory [1]. However, this slowness is not universally applicable in the brain, leaving unclear how to analys

…governed by the intricate interplay of synaptic connections within neural populations. Extensive theoretical and empirical investigations have underscored the influential role of synaptic weights in shaping both the collective dynamics and the fluctuations of neural activity. A salient inquiry arising from this understanding pertains to the mech

How changes in synaptic connections underlie learning and memory is a central question in Neuroscience. Previous modeling efforts focused on biologically realistic learning rules and dynamics, but these rules usually produce bimodal weight distributions that are not aligned with experimentally observed lognormal distributions of synaptic weights. How biological learning rules preserve such unimodal distributions and at the same time retain information, i.e., learn, remains unknown. Here, we take a game-theoretical approach, asking whether learning is possible under the constraint of retaining

…as time constants, input currents, and synaptic weights. While we have a good understanding of how these parameters affect the behavior of isolated neurons, a similar intuition for connected groups of neurons is lacking. Here, we simulated millions of neuronal network models using a range of parameters, building a library of dynamics that catalogs network parameters and their resulting dynamics. Towards this end, we collected spike-trains by simulating network mod

…is believed to be caused by changes in synaptic weights. These changes impact the population readout and consequently require adaptation of downstream areas to maintain stable function, a costly

…In addition to the afferent synaptic weights, the nonlinear gain function is optimized,

…only use local information to update synaptic weights, and are sometimes combined with weight constraints to reflec

…cortical brain activity and induce synaptic plasticity in aberrant neural circuitry. When successful, rTMS results in lasting therapeutic changes that may reflect an amelioration in cognitive symptoms. The salient parameters that comprise rTMS protocols, such as pulse frequency, amplitude, etc., interact to determine

…optimized response nonlinearities and synaptic weights can maximize encoded information, but these solutions depend on tightly tuned response functions and connectivities. Our study found that incorporating generic, non-invertible, selectivity-inducing nonlinearities into a circuit with divergent and convergent structure can

…frequent reactivations such that the weights within the assemblies remain at a high level. In this work, we extend the model such that memories are maintained independently of reactivations. We suggest that long-term memories are mainly represented by the number of connections between assembly

Modification of synaptic connections is thought to be the core mechanism of long-term memory formation. Despite the recent experimental development, there are still challenges inferring the synaptic plasticity rule from the experimental data. Here, we considered a biologically plausible synaptic plasticity model, and explored its key properties that enable efficient inference. As a recent theoretical work suggested the firing rates to be the most critical under in vivo-like conditions, we considered firing-rate dependent plasticity and its matrix representation. To enable efficient recovery

Synaptic inputs onto single cortical neurons exhibit substantial diversity in their sensory-driven activity. What this diversity reflects is unclear, and appears counter-productive in generating selective somatic responses to specific stimuli. We propose that synaptic diversity supports computations that most effectively use information from upstream populations. To test this idea, we directly compare in silico synaptic weights for probabilistic decoder with in vivo synaptic input measurements. We define a decoder for a single sensory variable, orientation, that reads out the stimulus orientat

The accumulation and storage of information in memory is a fundamental computation underlying animal behavior. In many brain regions and task paradigms, ranging from motor control to navigation to decision-making, such accumulation is accomplished through neural integrator circuits that enable external inputs to move a system’s population-wide patterns of neural activity along a continuous attractor. In the first portion of the talk, I will discuss our efforts to dissect the circuit mechanisms underlying a neural integrator from a rich array of anatomical, physiological, and perturbation expe

…of inputs, firing thresholds, synaptic weights, or transmission delays) can all be understood as def

…of parallel fiber-Purkinje cell synaptic weights, the expansion in neuron number of the granule cell layer and their synaptic in-degree, and sparse coding by granule cells. Typically, these theories focus on the learning of random