Biology subjects

Cecchini, G.

Publications and source records attributed to Cecchini, G..

5 recordsLinked to original sources

bioRxiv2025

Locally balanced inhibition allows for robust learning of input-output associations in feedforward networks with Hebbian plasticity

In neural networks within the brain, the activity of a post-synaptic neuron is determined by the combined influence of many pre-synaptic neurons. This distributed processing enables mechanisms like Hebbian plasticity to associate sensory inputs with specific internal states, as seen in feedforward structures such as the CA1 region of the hippocampus. By modifying synaptic weights through Hebbian rules, sensory inputs can subsequently elicit outputs that consistently reflect their corresponding internal states. When input and output patterns are uncorrelated, this approach allows for the encoding of a large number of distinct associations, enabling efficient memory storage. Our study demonstrates a critical limitation when output patterns become weakly correlated with input patterns through the intrinsic feedforward networks connectivity. In these cases, the Hebbian rule preferentially strengthens synaptic weights shared across patterns, leading to a "freezing" of the networks structure. This results in highly correlated output patterns over time, effectively reducing the networks capacity to store diverse associations and limiting its flexibility in learning. To address this challenge, we find that including a mechanism of locally balanced inhibition, which has been shown to be a key feature of cortical circuits in-vivo, counteracts the undesired correlations between inputs and outputs. By dynamically regulating inhibitory input, locally balanced inhibition prevents the over-strengthening of shared weights, restoring the networks ability to maintain robust and flexible learning. This finding underscores the importance of inhibitory mechanisms in enabling efficient and adaptive information processing in neural circuits, offering insights into how biological networks maintain their remarkable capacity for associative learning.

neuroscience↗

bioRxiv2025

Hebbian learning accounts for the effects of experience and novel exposure on representational drift in CA1 of hippocampus

Neuronal activity patterns slowly change over time even when sensory stimuli and animal behavior remain stable, a phenomenon known as representational drift. In area CA1 of the hippocampus, the amount of drift in the tuning of place cells on a familiar linear track is proportional to the time the animal spends exploring that track while the drift in cells mean rate depends on the absolute time elapsed between sessions. Recently it was shown that exploration of a novel, enriched environment between sessions on the familiar track actually decreases the drift in tuning on that track, i.e. it stabilizes place fields compared to a baseline condition without the novel learning. This finding challenges computational models of drift in which new learning leads to overwriting and hence one would expect more drift, not less. Here we show that such models are indeed compatible with the observed findings as long as spatially-tuned input populations to CA1 which are active on the track versus the enriched environment are largely non-overlapping. Furthermore, in order to reproduce the findings, we must assume that the total amount of learning between the baseline and novel-exposure conditions is the same. Namely, the synaptic resources available for encoding patterns, or episodes, is fixed over a given period of time, but can be preferentially allocated to episodes of particular salience, such as the exploration of novel environments.

neuroscience↗

bioRxiv2024

Representational drift as the consequence of ongoing memory storage

Memory systems with biologically constrained synapses have been the topic of intense theoretical study for over thirty years. Perhaps the most fundamental and far-reaching finding from this work is that the storage of new memories implies the partial erasure of already-stored ones. This overwriting leads to a decorrelation of sensory-driven activity patterns over time, even if the input patterns remain similar. Representational drift (RD) should therefore be an expected and inevitable consequence of ongoing memory storage. We tested this hypothesis by fitting a network model to data from long-term chronic calcium imaging experiments in mouse hippocampus. Synaptic turnover in the model inputs, consistent with the ongoing encoding of new activity patterns, accounted for the observed statistics of RD. This mechanism also provides a parsimonious explanation for the diverse effects of experience on drift found in experiment. Our results suggest that RD should be observed wherever neuronal circuits are involved in a process of ongoing learning or memory storage.

neuroscience↗

bioRxiv2023

A biologically plausible decision-making model based on interacting cortical columns

We present a novel decision-making model with two populations. Each population is composed of Regularly Spiking (excitatory) and Fast Spiking (inhibitory) cells in cortical layer 2/3. Each population votes for one of the two visual alternatives shown on a monitor in human and macaque experiments. The model is biophysically plausible since it is based on long-range cortico-cortical connections between the layer 2/3 populations. These connections are excitatory. They contact both Regularly Spiking and Fast Spiking cells. This long-range excitation is conflicted by an inhibition based on local connections within the populations. This configuration introduces a competition between the layer 2/3 populations, sufficient for making a decision to choose between two alternatives shown on the monitor. We integrate the model with a reward-driven learning mechanism. This allows the model to learn the optimal strategy maximizing the cumulative reward in the long term. We test the model on two decision-making tasks applied on human and macaque. This model elaborates certain biophysical details which were not considered by simpler phenomenological models proposed for similar decision-making tasks. Finally, the model can be embedded in a brain simulator such as The Virtual Brain to study decision-making in terms of large-scale brain dynamics.

neuroscience↗

bioRxiv2023

A Theoretical Formalization of Consequence-Based Decision-Making

Learning to make adaptive decisions depends on exploring options, experiencing their consequence, and reassessing ones strategy for the future. Although several studies have analyzed various aspects of value-based decision-making, most of them have focused on decisions in which gratification is cued and immediate. By contrast, how the brain gauges delayed consequence for decision-making remains poorly understood. To investigate this, we designed a decision-making task in which each decision altered future options. The task was organized in groups of consecutively dependent trials, and the participants were instructed to maximize the cumulative reward value within each group. In the absence of any explicit performance feedback, the participants had to test and internally assess specific criteria to make decisions. This task was designed to specifically study how the assessment of consequence forms and influences decisions as learning progresses. We analyzed behavior results to characterize individual differences in reaction times, decision strategies, and learning rates. We formalized this operation mathematically by means of a multi-layered decision-making model. By using a mean-field approximation, the first layer of the model described the dynamics of two populations of neurons which characterized the binary decision-making process. The other two layers modulated the decision-making policy by dynamically adapting an oversight learning mechanism. The model was validated by fitting each individual participants behavior and it faithfully predicted non-trivial patterns of decision-making, regardless of performance level. These findings provided an explanation to how delayed consequence may be computed and incorporated into the neural dynamics of decision-making, and to how learning occurs in the absence of explicit feedback.

neuroscience↗