Biology subjects

Galella, S.

Publications and source records attributed to Galella, S..

3 recordsLinked to original sources

bioRxiv2026

Random network structure stabilizes neural manifolds

Neuronal activity patterns change continuously over days and weeks, a phenomenon known as representational drift. Despite this, the geometric structure of population representations, namely the pairwise similarities between stimulus-evoked activity patterns, remains remarkably stable. How can ongoing changes in activity be consistent with stable representational similarity? We show that this is a generic consequence of random connectivity: in networks with random connectivity, output similarity is a monotonically increasing function of input similarity, independent of the specific connectivity pattern. Drift, whether driven by random synaptic turnover or Hebbian plasticity, merely transitions the network between random instantiations, leaving similarity intact. This extends to recurrent architectures and to deep neural networks, where continued training beyond performance saturation produces activity drift while preserving representational similarity. Although connectivity in the brain is not random, networks trained on high-dimensional inputs acquire connectivity that behaves statistically like a random projection, making these results broadly applicable to biological neural circuits.

neuroscience↗

bioRxiv2023

Learning Neural Representations in Task-Switching Guided by Context Biases

AO_SCPLOWBSTRACTC_SCPLOWOur brain can filter and integrate external information with internal representations to accomplish goal-directed behavior. The ability to switch between tasks effectively in response to context and external stimuli is a hallmark of cognitive control. Task switching occurs rapidly and efficiently, allowing us to perform multiple tasks with ease. Similarly, artificial intelligence can be tailored to exhibit multitask capabilities and achieve high performance across domains. In this study, we delve into neural representations learned by task-switching feedforward networks, which use task-specific biases for multitasking mediated by context inputs. Task-specific biases are learned by alternating the tasks the neural network learns during training. By using two-alternative choice tasks, we find that task-switching networks produce representations that resemble other multitasking paradigms, namely parallel networks in the early stages of processing and independent subnetworks in later stages. This transition in information processing is akin to that in the cortex. We then analyze the impact of inserting task contexts in different stages of processing, and the role of its location in the alignment between the task and the stimulus features. To confirm the generality of results, we display neural representations during task switching for different task and data sets. In summary, the use of context inputs improves the interpretability of feedforward neural networks for multitasking, setting the basis for studying architectures and tasks of higher complexity, including biological microcircuits in the brain carrying out context-dependent decision making.

neuroscience↗

bioRxiv2020

Soft-wired long-term memory in a natural recurrent neuronal network

Neuronal networks provide living organisms with the ability to process information. They are also characterized by abundant recurrent connections, which give rise to strong feed-back that dictates their dynamics and endows them with fading (short-term) memory. The role of recurrence in long-term memory, on the other hand, is still unclear. Here we use the neuronal network of the roundworm C. elegans to show that recurrent architectures in living organisms can exhibit long-term memory without relying on specific hard-wired modules. A genetic algorithm reveals that the experimentally observed dynamics of the worms neuronal network exhibits maximal complexity (as measured by permutation entropy). In that complex regime, the response of the system to repeated presentations of a time-varying stimulus reveals a consistent behavior that can be interpreted as soft-wired long-term memory. A common manifestation of our ability to remember the past is the consistence of our responses to repeated presentations of stimuli across time. Complex chaotic dynamics is known to produce such reliable responses in spite of its characteristic sensitive dependence on initial conditions. In neuronal networks, complex behavior is known to result from a combination of (i) recurrent connections and (ii) a balance between excitation and inhibition. Here we show that those features concur in the neuronal network of a living organism, namely C. elegans. This enables long-term memory to arise in an on-line manner, without having to be hard-wired in the brain.

systems biology↗