Biology subjects

Stefanini, F.

Publications and source records attributed to Stefanini, F..

6 recordsLinked to original sources

bioRxiv2022

Neural representational geometry correlates with behavioral differences between monkeys

Animals likely use a variety of strategies to solve laboratory tasks. Traditionally, combined analysis of behavioral and neural recording data across subjects employing different strategies may obscure important signals and give confusing results. Hence, it is essential to develop techniques that can infer strategy at the single-subject level. We analyzed an experiment in which two monkeys performed a visually cued rule-based task. The analysis of their performance shows no indication that they used a different strategy. However, when we examined the geometry of stimulus representations in the state space of the neural activities recorded in dorsolateral prefrontal cortex, we found striking differences between the two monkeys. Our purely neural results induced us to reanalyze the behavior. The new analysis showed that the differences in representational geometry correlate with differences in the reaction times, revealing behavioral differences we were unaware of. All these analyses indicate that the monkeys are using different strategies. Finally, using recurrent neural network models trained to perform the same task, we show that these strategies correlate with the amount of training, suggesting a possible explanation for the observed neural and behavioral differences.

neuroscience↗

bioRxiv2021

Complementary population codes in the dorsal and ventral hippocampus during associative learning

Animals associate cues with outcomes and continually update these associations as new information is presented. The hippocampus is crucial for this, yet how neurons track changes in cue-outcome associations remains unclear. Using 2-photon calcium imaging, we tracked the same dCA1 and vCA1 neurons across days to determine how responses evolve across phases of odor-outcome learning. We find that, initially, odors elicited robust responses in dCA1, whereas in vCA1 responses emerged after learning, including broad representations that stretched across cue, trace, and outcome periods. Population dynamics in both regions rapidly reorganized with learning, then stabilized into ensembles that stored odor representations for days, even after extinction or pairing with a different outcome. Finally, we found stable, robust signals across CA1 when anticipating reward, but not when anticipating inescapable shock. These results identify how the hippocampus encodes, stores, and updates learned associations, and illuminates the unique contributions of dorsal and ventral hippocampus.

neuroscience↗

bioRxiv2019

Face familiarity detection with complex synapses

Synaptic plasticity is a complex phenomenon involving multiple biochemical processes that operate on different timescales. We recently showed that this complexity can greatly increase the memory capacity of neural networks when the variables that characterize the synaptic dynamics have limited precision, as in biological systems. These types of complex synapses have been tested mostly on simple memory retrieval problems involving random and uncorrelated patterns. Here we turn to a real-world problem, face familiarity detection, and we show that also in this case it is possible to take advantage of synaptic complexity to store in memory a large number of faces that can be recognized at a later time. In particular, we show that the familiarity memory capacity of a system with complex synapses grows almost linearly with the number of the synapses and quadratically with the number of neurons. Complex synapses are superior to simple ones, which are characterized by a single variable, even when the total number of dynamical variables is matched. We further show that complex and simple synapses have distinct signatures that are testable in proposed experiments. Our results indicate that a memory system with complex synapses can be used in real-world tasks such as face familiarity detection. SignificanceThe complexity of biological synapses is probably important for enabling us to remember the past for a long time and rapidly store new memories. The advantage of complex synapses in terms of memory capacity is significant when the variables that characterize the synaptic dynamics have limited precision. This advantage has been estimated under the simplifying assumption that the memories to be stored are random and uncorrelated. Here we show that synaptic complexity is important also in a more challenging and realistic face familiarity detection task. We built a simple neural circuit that can report whether a face has been previously seen or not. This circuit incorporates complex synapses that operate on multiple timescales. The memory performance of this circuit is significantly higher than in the case in which synapses are simple, indicating that the complexity of biological synapses can be important also in real-world memory tasks.

neuroscience↗

bioRxiv2019

Coding of social novelty in the hippocampal CA2 region and its disruption and rescue in a mouse model of schizophrenia

The hippocampal CA2 region is essential for social memory and has been implicated in neuropsychiatric disorders. However, little is known about how CA2 neural activity encodes social interactions and how this coding is altered in disease. We recorded from CA2 pyramidal neurons as mice engaged in social interactions and found that while CA2 failed to stably represent spatial location, CA2 activity encoded contextual changes and novel social stimuli. In the Df(16)A+/- mouse model of the human 22q11.2 microdeletion, a major schizophrenia risk factor, CA2 activity showed a surprising increase in spatial coding while failing to encode social novelty, consistent with the social memory deficit in these mice. Previous work has shown that CA2 pyramidal neurons are hyperpolarized in Df(16)A+/- mice, likely as a result of upregulation of TREK-1 K+ current. We found that administration of a TREK-1 antagonist rescued the social memory deficits and restored normal CA2 coding properties in Df(16)A+/- mice, supporting a crucial role for CA2 in the encoding of novel social stimuli and social dysfunction.

neuroscience↗

bioRxiv2019

Hippocampal network reorganization underlies the formation of a temporal association memory

Episodic memory requires linking events in time, a function dependent on the hippocampus. In \"trace\" fear conditioning, animals learn to associate a neutral cue with an aversive stimulus despite their separation in time by a delay period on the order of tens of seconds. But how this temporal association forms remains unclear. Here we use 2-photon calcium imaging to track neural population dynamics over the complete time-course of learning and show that, in contrast to previous theories, the hippocampus does not generate persistent activity to bridge the time delay. Instead, learning is concomitant with broad changes in the active neural population in CA1. While neural responses were highly stochastic in time, cue identity could be reliably read out from population activity rates over longer timescales after learning. These results question the ubiquity of neural sequences during temporal association learning, and suggest that trace fear conditioning relies on mechanisms that differ from persistent activity accounts of working memory.

neuroscience↗

bioRxiv2018

A distributed neural code in ensembles of dentate gyrus granule cells

The tuning properties of neurons in a given brain region have been traditionally viewed as the under-pinnings of computation in neural circuits. However, at the higher levels of processing, specialization is often elusive, instead a mix of sensory, cognitive and behavioural quantities drive neural activity. In such networks, ensembles of neurons, rather than single units with easily interpretable tuning properties, encode behaviourally relevant variables. Here we show that this is the case also in the dentate gyrus and CA1 subregions of the hippocampus. Using calcium imaging in freely moving mice, we decoded the instantaneous position, direction of motion and speed from the activity of hundreds of cells in the hippocampus of mice freely exploring an arena. For the vast majority of neurons in both regions, their response properties were not predictive of their importance for encoding position. Furthermore, we could decode position from populations of cells that were important for decoding direction of motion and vice versa, showing that these quantities are encoded by largely overlapping ensembles as in distributed neural code. Finally, we found that correlated activities had an impact on decoding performance in CA1 but not in dentate gyrus, suggesting different enconding strategies for these areas. Our analysis indicates that classical methods of analysis based on single cell response properties might be insufficient to accurately characterize the neural computation in a given area. In contrast, population analysis may help highlight previously overlooked properties of hippocampal circuits.

neuroscience↗