Biology subjects

Agnes, E. J.

Publications and source records attributed to Agnes, E. J..

7 recordsLinked to original sources

bioRxiv2026

Dendritic excitatory-inhibitory balance and branch-specific gating enable selective recall of associative memories

The ability to reconstruct complete memories from fragmented cues is a defining feature of associative memory, which requires neural circuits to store many overlapping and conflicting memory representations while selectively controlling memory accessibility. Attractor dynamics provide the dominant modelling framework for associative memory and have been used to interpret connectivity and behaviour. Yet despite this influence, how attractor-based memory is implemented in biophysical spiking circuits with compartmental dendrites and structured recurrent interactions has remained unresolved, with no unifying theoretical framework for interpreting emerging dendritic imaging and connectomic data. Here we show that local dendritic excitatory-inhibitory balance creates binary-like membrane-potential states that enable associative recall, and that branch-selective inhibition gates access to memory sets stored across dendritic trees, limiting interference between distinct sets. We further show that continuous dendritic dynamics enlarge basins of attraction by integrating partial cues over time, and that a winner-take-all readout circuit implements memory-set selection autonomously, generating interneuron activity that encodes set identity. Our framework predicts branch-specific voltage and calcium signatures that provide a basis for interpreting dendritic measurements in terms of engram organisation, bridging classical associative memory theory with dendritic circuit mechanisms.

neuroscience↗

bioRxiv2024

Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks.

Synaptic plasticity is a key player in the brains life-long learning abilities. However, due to experimental limitations, the mechanistic link between synaptic plasticity rules and the network-level computations they enable remain opaque. Here we use evolutionary strategies (ES) to meta-learn local co-active plasticity rules in large recurrent spiking net-works, using parameterizations of increasing complexity. We discover rules that robustly stabilize network dynamics for all four synapse types acting in isolation (E-to-E, E-to-I, I-to-E and I-to-I). More complex functions such as familiarity detection can also be included in the search constraints. However, our meta-learning strategy begins to fail for co-active rules of increasing complexity, as it is challenging to devise loss functions that effectively constrain net-work dynamics to plausible solutions a priori. Moreover, in line with previous work, we can [fi]nd multiple degenerate solutions with identical network behaviour. As a local optimization strategy, ES provides one solution at a time and makes exploration of this degeneracy cumbersome. Regardless, we can glean the interdependecies of various plasticity parameters by considering the covariance matrix learned alongside the optimal rule with ES. Our work provides a proof of principle for the success of machine-learning-guided discovery of plasticity rules in large spiking networks, and points at the necessity of more elaborate search strategies going forward.

neuroscience↗

bioRxiv2021

Interacting synapses stabilise both learning and neuronal dynamics in biological networks

The brains functionality is developed and maintained through synaptic plasticity. As synapses undergo plasticity they also affect each other. The nature of such "codependency" is difficult to disentangle experimentally, because multiple synapses must be monitored simultaneously. To help understand the experimentally observed phenomena, we introduce a framework that formalises synaptic codependency between different connection types. The resulting model explains how inhibition can gate excitatory plasticity, while neighbouring excitatory-excitatory interactions determine the strength of long-term potentiation. Furthermore, we show how the interplay between excitatory and inhibitory synapses can account for the quick rise and long-term stability of a variety of synaptic weight profiles, such as orientation tuning and dendritic clustering of co-active synapses. In recurrent neuronal networks, codependent plasticity produces rich and stable motor cortex-like dynamics with high input sensitivity. Our results suggest an essential role for the neighbourly synaptic interaction during learning, connecting micro-level physiology with network-wide phenomena.

neuroscience↗

bioRxiv2021

Regimes and mechanisms of transient amplification in abstract and biological networks

We use upper triangular matrices as abstract representations of neuronal networks and directly manipulate their eigenspectra and non-normality to explore different regimes of transient amplification. Counter-intuitively, manipulating the imaginary distribution can lead to highly amplifying regimes. This is noteworthy, because biological networks are constrained by Dales law and the non-existence of neuronal self-loops, limiting the range of manipulations in the real dimension. Within these constraints we can further manipulate transient amplification by controlling global inhibition.

neuroscience↗

bioRxiv2020

A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network

The search for biologically faithful synaptic plasticity rules has resulted in a large body of models. They are usually inspired by - and fitted to - experimental data, but they rarely produce neural dynamics that serve complex functions. These failures suggest that current plasticity models are still under-constrained by existing data. Here, we present an alternative approach that uses meta-learning to discover plausible synaptic plasticity rules. Instead of experimental data, the rules are constrained by the functions they implement and the structure they are meant to produce. Briefly, we parameterize synaptic plasticity rules by a Volterra expansion and then use supervised learning methods (gradient descent or evolutionary strategies) to minimize a problem-dependent loss function that quantifies how effectively a candidate plasticity rule transforms an initially random network into one with the desired function. We first validate our approach by re-discovering previously described plasticity rules, starting at the single-neuron level and "Ojas rule", a simple Hebbian plasticity rule that captures the direction of most variability of inputs to a neuron (i.e., the first principal component). We expand the problem to the network level and ask the framework to find Ojas rule together with an anti-Hebbian rule such that an initially random two-layer firing-rate network will recover several principal components of the input space after learning. Next, we move to networks of integrate-and-fire neurons with plastic inhibitory afferents. We train for rules that achieve a target firing rate by countering tuned excitation. Our algorithm discovers a specific subset of the manifold of rules that can solve this task. Our work is a proof of principle of an automated and unbiased approach to unveil synaptic plasticity rules that obey biological constraints and can solve complex functions.

neuroscience↗

bioRxiv2020

Context-modular memory networks support high-capacity, flexible, and robust associative memories

Biological memory is known to be flexible -- memory formation and recall depend on factors such as the behavioral context of the organism. However, this property is often ignored in associative memory models. Here, we bring this dynamic nature of memory to the fore by introducing a novel model of associative memory, which we refer to as the context-modular memory network. In our model, stored memory patterns are associated to one of several background network states, or contexts. Memories are accessible when their corresponding context is active, and are otherwise inaccessible. Context modulates the effective network connectivity by imposing a specific configuration of neuronal and synaptic gating - gated neurons (respectively synapses) have their activity (respectively weights) momentarily silenced, thereby reducing interference from memories belonging to other contexts. Memory patterns are randomly and independently chosen, while neuronal and synaptic gates may be selected randomly or optimized through a process of contextual synaptic refinement. Through signal-to-noise and mean field analyses, we show that context-modular memory networks can exhibit substantially increased memory capacity with random neuronal gating, but not with random synaptic gating. For contextual synaptic refinement, we devise a method in which synapses are gated off for a given context if they destabilize the memory patterns in that context, drastically improving memory capacity. Notably, synaptic refinement allows for patterns to be accessible in multiple contexts, stabilizing memory patterns even for weight matrices that do not contain any information about the memory patterns such as Gaussian random matrices. Lastly, we show that context modulates the relative stability of accessible versus inaccessible memories, thereby confirming that contextual control acts as a mechanism to temporarily hide or reveal particular memories. Overall, our model integrates recent ideas about context-dependent memory organization with classic associative memory models, highlights an intriguing trade-off between memory capacity and accessibility, and carries important implications for the understanding of biological memory storage and recall in the brain.

neuroscience↗

bioRxiv2019

Complementary inhibitory receptive fields emerge from synaptic plasticity and create an attentional switch in sensory circuits

Cortical areas comprise multiple types of inhibitory interneurons with stereotypical connectivity motifs, but their combined effect on postsynaptic dynamics has been largely unexplored. Here, we analyse the response of a single postsynaptic model neuron receiving tuned excitatory connections alongside inhibition from two plastic populations. Depending on the inhibitory plasticity rule, synapses remain unspecific (flat), become anti-correlated to, or mirror excitatory synapses. Crucially, the neurons receptive field, i.e., its response to presynaptic stimuli, depends on the modulatory state of inhibition. When both inhibitory populations are active, inhibition balances excitation, resulting in uncorrelated postsynaptic responses regardless of the inhibitory tuning profiles. Modulating the activity of a given inhibitory population produces strong correlations to either preferred or non-preferred inputs, in line with recent experimental findings showing dramatic context-dependent changes of neurons receptive fields. We thus confirm that a neurons receptive field doesnt follow directly from the weight profiles of its presynaptic afferents.

neuroscience↗