Biology subjects

Cone, I.

Publications and source records attributed to Cone, I..

6 recordsLinked to original sources

bioRxiv2026

Hunger reconfigures a reward learning circuit into a memory competent mode

Internal states such as hunger dynamically reshape activity across circuits to support resource seeking. Neuromodulation provides a means of controlling such physiological properties of neurons, but how this flexibility regulates memory networks remains unclear. Here, we describe how hunger reconfigures a dopaminergic food-reward circuit between two physiological modes that support memory formation, in Drosophila. Starvation suppresses baseline dopaminergic activity through peptidergic signalling, enabling reward-evoked, large-amplitude dopamine neuron spikes to reinforce learning. This spiking mode can be engaged by sugar consumption and persists beyond feeding, reflecting the fly's satiety state. Persistent dopaminergic large-amplitude spiking reinforces learning and transitions the memory network into a mode that prioritizes consolidation of recently acquired memories. The transition from hunger to satiation is also reflected in the activity of postsynaptic output neurons that shift from a tonic, decorrelated mode that is responsive to dopamine into a bursting, correlated mode in which further dopaminergic modulation is occluded. Therefore, nutrient deprivation reconfigures the memory circuit into a learning competent dopamine-receptive mode, which persistent reinforcing dopamine then switches into a satiated mode driving memory consolidation. Together these processes mechanistically intertwine state-dependent reward-signalling with subsequent memory stabilization through transitions in physiological mode.

neuroscience↗

bioRxiv2025

Credit Assignment via Behavioral Timescale Synaptic Plasticity: Theoretical Frameworks

Behavioral Timescale Synaptic Plasticity (BTSP) is a form of synaptic plasticity in which dendritic Ca2+ plateau potentials in hippocampal pyramidal neurons drive rapid place field formation. Unlike traditional learning rules, BTSP learns correlations on the timescales of seconds and rapidly changes single-unit activity in only a few trials. To explore how BTSP-like learning can be integrated into network models, we propose a generalized BTSP rule (gBTSP), which we apply to unsupervised and supervised learning tasks, in both feedforward and recurrent networks. Unsupervised gBTSP mirrors classical frameworks of competitive learning, learning place field maps (in the feed-forward case), and attractive memory networks (in the recurrent case). For supervised learning, we show that plateau events can reduce task error, enabling gBTSP to solve tasks such as trajectory matching and delayed non-match-to-sample. However, we find that credit assignment via gBTSP becomes harder to achieve with increased network depth or CA3-like recurrence. This suggests that additional features may be needed to support BTSP-mediated few-shot learning of complex tasks in the hippocampus.

neuroscience↗

bioRxiv2023

Latent Representations in Hippocampal Network Model Co-Evolve with Behavioral Exploration of Task Structure

Real-life behavioral tasks are often complex and depend on abstract combinations of sensory stimuli and internal logic. To successfully learn these tasks, animals must pair actions or decisions to the tasks complex structure. The hippocampus has been shown to contain fields which represent complex environmental and task variables, including place, lap, evidence accumulation, etc. Altogether, these representations have been hypothesized to form a "cognitive map" which encodes the complex real-world structure underlying behavior. However, it is still unclear how biophysical plasticity mechanisms at the single cell level can lead to the population-wide evolution of task-relevant maps. In this work we present a biophysically plausible model comprised of a recurrent hippocampal network and an action network, in which the latent representational structure co-evolves with behavior in a task-dependent manner. We demonstrate that the network develops latent structures that are needed for solving the task and does not integrate latent structures which do not support task learning. We show that, in agreement with experimental data, cue-dependent "splitters" can only be induced at the single cell level if the task requires a split representation to solve. Finally, our model makes specific predictions on how biases in behavior result from experimentally testable biases in the underlying latent representation.

neuroscience↗

bioRxiv2022

Learning and Expression of Dopaminergic Reward Prediction Error via Plastic Representations of Time

The dominant theoretical framework to account for reinforcement learning in the brain is temporal difference (TD) reinforcement learning. The normative motivation for TD theory is that the brain needs to learn about expected future rewards in order to learn how to maximize these rewards. The TD framework predicts that some neuronal elements should represent the reward prediction error (RPE), which means they signal the difference between the expected future rewards and the actual rewards. What makes the TD learning theory so prominent is that the firing properties of dopaminergic neurons in the ventral tegmental area (VTA) appear similar to those of RPE model-neurons in TD learning. Biologically plausible implementations of TD learning assume a fixed temporal basis for each stimulus that might eventually predict a reward. Here we show on the basis of first principles that such a fixed temporal basis is implausible. We also show that certain predictions of TD learning are inconsistent with experimental data. We propose instead an alternative theoretical framework, coined FLEX (Flexibly Learned Errors in Expected Reward). In FLEX, feature specific representations of time are learned, allowing for neural representations of stimuli to adjust their timing and relation to rewards in an online manner. As an indirect consequence, dopamine in FLEX resembles, but is not equivalent to RPE. In FLEX dopamine acts as an instructive signal which helps build temporal models of the environment. FLEX is a general theoretical framework that has many possible biophysical implementations. In order to show that FLEX is a feasible approach, we present a specific biophysically plausible model which implements the principles of FLEX. We show that this implementation can account for various reinforcement learning paradigms, and that its results and predictions are consistent with a preponderance of both existing and reanalyzed experimental data.

neuroscience↗

bioRxiv2020

Behavioral Time Scale Plasticity of Place Fields: Mathematical Analysis

Traditional synaptic plasticity experiments and models depend on tight temporal correlations between pre- and postsynaptic activity. These tight temporal correlations, on the order of tens of milliseconds, are incompatible with significantly longer behavioral time scales, and as such might not be able to account for plasticity induced by behavior. Indeed, recent findings in hippocampus suggest that rapid, bidirectional synaptic plasticity which modifies place fields in CA1 operates at behavioral time scales. These experimental results suggest that presynaptic activity generates synaptic eligibility traces both for potentiation and depression, which last on the order of seconds. These traces can be converted to changes in synaptic efficacies by the activation of an instructive signal that depends on naturally occurring or experimentally induced plateau potentials. We have developed a simple mathematical model that is consistent with these observations. This model can be fully analyzed to find the fixed points of induced place fields, the convergence to these fixed points, and how these fixed points depend on system parameters such as the size and shape of presynaptic place fields, the animals velocity, and the parameters of the plasticity rule.

neuroscience↗

bioRxiv2020

Learning precise spatiotemporal sequences via biophysically realistic circuits with modular structure

The ability to express and learn temporal sequences is an essential part of learning and memory. Learned temporal sequences are expressed in multiple brain regions and as such there may be common design in the circuits that mediate it. This work proposes a substrate for such representations, via a biophysically realistic network model that can robustly learn and recall discrete sequences of variable order and duration. The model consists of a network of spiking leaky-integrate-and-fire model neurons placed in a modular architecture designed to resemble cortical microcolumns. Learning is performed via a learning rule with "eligibility traces", which hold a history of synaptic activity before being converted into changes in synaptic strength upon neuromodulator activation. Before training, the network responds to incoming stimuli, and contains no memory of any particular sequence. After training, presentation of only the first element in that sequence is sufficient for the network to recall an entire learned representation of the sequence. An extended version of the model also demonstrates the ability to successfully learn and recall non-Markovian sequences. This model provides a possible framework for biologically realistic sequence learning and memory, and is in agreement with recent experimental results, which have shown sequence dependent plasticity in sensory cortex.

neuroscience↗