Biology subjects

Gallinaro, J. V.

Publications and source records attributed to Gallinaro, J. V..

5 recordsLinked to original sources

bioRxiv2022

Synaptic weights that correlate with presynaptic selectivity increase decoding performance

The activity of neurons in the visual cortex is often characterized by tuning curves, which are thought to be shaped by Hebbian plasticity during development and sensory experience. This leads to the prediction that neural circuits should be organized such that neurons with similar functional preference are connected with stronger weights. In support of this idea, previous experimental and theoretical work have provided evidence for a model of the visual cortex characterized by such functional subnetworks. A recent experimental study, however, have found that the postsynaptic preferred stimulus was defined by the total number of spines activated by a given stimulus and independent of their individual strength. While this result might seem to contradict previous literature, there are many factors that define how a given synaptic input influences postsynaptic selectivity. Here, we designed a computational model in which postsynaptic functional preference is defined by the number of inputs activated by a given stimulus. Using a plasticity rule where synaptic weights tend to correlate with presynaptic selectivity, and is independent of functional-similarity between pre- and postsynaptic activity, we find that this model can be used to decode presented stimuli in a manner that is comparable to maximum likelihood inference.

neuroscience↗

bioRxiv2021

Memories in a network with excitatory and inhibitory plasticity are encoded in the spiking irregularity

Cell assemblies are thought to be the substrate of memory in the brain. Theoretical studies have previously shown that assemblies can be formed in networks with multiple types of plasticity. But how exactly they are formed and how they encode information is yet to be fully understood. One possibility is that memories are stored in silent assemblies. Here we used a computational model to study the formation of silent assemblies in a network of spiking neurons with excitatory and inhibitory plasticity. We found that even though the formed assemblies were silent in terms of mean firing rate, they had an increased coefficient of variation of inter-spike intervals. We also found that this spiking irregularity could be readout with support of short-term plasticity, and that it could contribute to the longevity of memories.

neuroscience↗

bioRxiv2020

Time course of homeostatic structural plasticity in response to optogenetic stimulation in mouse anterior cingulate cortex

Plasticity is the mechanistic basis of development, aging, learning and memory, both in healthy and pathological brains. Structural plasticity is rarely accounted for in computational network models, due to a lack of insight into the underlying neuronal mechanisms and processes. Little is known about how the rewiring of networks is dynamically regulated. To inform such models, we characterized the time course of neural activity, the expression of synaptic proteins, and neural morphology employing an in vivo optogenetic mouse model. We stimulated pyramidal neurons in the anterior cingulate cortex of mice and harvested their brains at 1.5 h, 24 h, and 48 h after stimulation. Stimulus-induced cortical hyperactivity persisted up to 1.5 h and decayed to baseline after 24 h, indicated by c-Fos expression. The synaptic proteins VGLUT1 and PSD-95, in contrast, were upregulated at 24 h and downregulated at 48 h, respectively. Spine density and spine head volume were also increased at 24 h and decreased at 48 h. This specific sequence of events reflects a continuous joint evolution of activity and connectivity that is characteristic of the model of homeostatic structural plasticity. Our computer simulations thus corroborate the observed empirical evidence from our animal experiments.

neuroscience↗

bioRxiv2020

Homeostatic structural plasticity leads to the formation of memory engrams through synaptic rewiring in recurrent networks

Brain networks store new memories using functional and structural synaptic plasticity. Memory formation is generally attributed to Hebbian plasticity, while homeostatic plasticity is thought to have an ancillary role in stabilizing network dynamics. Here we report that homeostatic plasticity alone can also lead to the formation of stable memories. We analyze this phenomenon using a new theory of network remodeling, combined with numerical simulations of recurrent spiking neural networks that exhibit structural plasticity based on firing rate homeostasis. These networks are able to store repeatedly presented patterns and recall them upon the presentation of incomplete cues. Storage is fast, governed by the homeostatic drift. In contrast, forgetting is slow, driven by a diffusion process. Joint stimulation of neurons induces the growth of associative connections between them, leading to the formation of memory engrams. These memories are stored in a distributed fashion throughout connectivity matrix, and individual synaptic connections have only a small influence. Although memory-specific connections are increased in number, the total number of inputs and outputs of neurons undergo only small changes during stimulation. We find that homeostatic structural plasticity induces a specific type of "silent memories", different from conventional attractor states.

neuroscience↗

bioRxiv2018

Network remodeling induced by transcranial brain stimulation: A computational model of tDCS-triggered cell assembly formation

Transcranial direct current stimulation (tDCS) is a variant of non-invasive neuro-modulation, which promises treatment for diseases like major depressive disorder or chronic pain for patients resistant to conventional therapies. In experiments, long lasting after-effects were observed, suggesting that plastic changes were induced. The exact mechanism underlying the emergence and maintenance of these after-effects, however, remains elusive. Here we propose a model to explain how transcranial stimulation triggers a homeostatic response of the network involving growth and decay of synapses. In our model, the cortical tissue underneath the electrodes is conceived as a recurrent network of excitatory and inhibitory neurons, in which excitatory-to-excitatory synapses are subject to structural plasticity. Various aspects of stimulation were tested via numerical simulations of such networks, including size and montage of the electrode, as well as intensity and duration of the stimulation. Our results suggest that stimulation indeed perturbs the homeostatic equilibrium and leads to cell assembly formation. Strong focal stimulation, for example, enhances the connectivity of new cell assemblies by increasing the rate of synaptic remodeling. Repetitive stimulation with well-chosen duty cycles increases the impact of stimulation as well. The long-term goal of our work is to optimize the impact of tDCS in clinical applications.

neuroscience↗