Neurochats
Sign inTime: 18h
Place: UPF, ciutadella campus, Mercè Rodoreda building 24, room 24.S01.
SUBSCRIBE TO OUR
MAILING LIST
INTRODUCTION
The goal of the Neurochats seminar aims to bring together young researchers from Barcelona and to encourage interaction among the various research centers in the city and its surroundings. The event counts with the pleasure of free pizza and the excitement of scientific discovery, creating a relaxed and engaging atmosphere for knowledge exchange. The format includes informal talks lasting 45 minutes followed by a 15-minute discussion, allowing masters, PhD students, and postdoctoral fellows to familiarize themselves with their colleagues’ research.
next session
Date: December 17th, 2024
Time: 18h
Place: UPF, ciutadella campus, Mercè Rodoreda building 24, room 24.S01. MAP
Fast encoding of memory in a recurrent network model with Behavioral Time-scale
Plasticity
Abstract:
Recently, a new form of plasticity has been discovered in the CA1 subregion of the hippocampus in behaving mice. In contrast to the more traditional forms of Hebbian learning, this plasticity occurs over a timescale of seconds and is thus termed Behavioral Timescale Synaptic Plasticity (BTSP). It enables long-lasting synaptic changes after a single experience through dendritic plateau potentials, making it particularly suited for encoding episodic memories.
In this talk, we investigate BTSP’s role in memory storage in recurrent networks such as CA3 that support memory maintenance. Based on a simplified mathematical model, we examine its properties of memory storage and recall dynamics. We find that BTSP can encode and retrieve a large number of memories and that interference between the memories is crucially enhancing both processes. These results position BTSP as an exciting mechanism for the formation and retrieval of episodic memory.
Pan Ye
Centre de Recerca Matemàtica (CRM)
Spatiotemporal integration properties in MT neurons affect motion discrimination
Abstract:
Perception requires integrating noisy dynamic visual information across the visual field to identify relevant stimuli and guide decisions. While temporal integration process has been studied extensively, the nonlinear spatial computations observed in single neurons in the visual cortex are often neglected. In this talk, I will show how the spatial structure of the stimuli modulates neural responses and impacts perceptual choices. Fitting nonlinear regression models, we show that monkeys integrate spatial evidence sublinearly during a motion discrimination task, driven by (i) weaker impact of motion further away from the fovea, and (ii) surround suppression effects causing an attenuation of the responses to motion in the center of the stimulus. We then estimate the impact of the spatiotemporal components of the motion field on the instantaneous firing rates of neurons in the middle temporal area (MT). Our results reveal heterogeneous contextual modulation effects, suggesting that spatial integration occurs at the sensory level. Finally, I will summarise our results in a two-stage model of perceptual decision making in which spatial context effects modulate spatial stimulus integration in neurons of the visual cortex, and a decision area accumulates temporal evidence to give rise to perception. This model connects the spatial properties of MT neurons to behavioral suppression effects, and shows how the properties of sensory neurons shape spatiotemporal perceptual integration.
Lucía Arancibia
Centre de Recerca Matemàtica (CRM)
organizers
PAST SESSIONS
Date: November 7th, 2024
Time: 18h
Place: UPF, ciutadella campus, Mercè Rodoreda building 24, room 24.S01. MAP
Spontaneous fluctuations in brain activity determine the level of consciousness
Abstract: The quest for reliable and objective measures to accurately determine brain states and levels of consciousness is critical both in basic and clinical neuroscience. Methods such as the Perturbational Complexity Index (PCI), which involves perturbing the brain with techniques such as transcranial magnetic stimulation, have proven to capture levels of consciousness with considerable precision.
However, our study introduces an innovative approach that achieves comparable results without requiring exogenous brain perturbation. We developed generative whole-brain models simulating spontaneous activity in humans and rodents across various brain states, including wakefulness, sleep, and anaesthesia. Through these models, we quantified levels of non-equilibrium by identifying violations of the Fluctuation-Dissipation Theorem (FDT), a principle in statistical physics and thermodynamics that connects a system’s response to external forces with its internal equilibrium fluctuations. Findings aligned with experimental outcomes and exhibited a clear increase in PCI from states of anaesthesia and sleep to wakefulness. This equivalence between the modelled and experimental results not only reveals the utility of FDT in capturing the capability to maintain consciousness comparable to PCI but also highlights the correlation between FDT and PCI measurements.
Crucially, our whole-brain modelling proposes a novel, non-invasive methodology for determining the consciousness level of any given brain state, providing a tool to assess and address unresponsiveness in patients.
Speaker: Tomás Berjaga Buisan (UPF)
A dynamic attractor network model of memory coding
Abstract: Experimental evidence suggests that familiar items are represented by larger hippocampal neuronal assemblies than less familiar ones. Memory storage and recall in the hippocampus have been modelled using attractor neural networks, whose design poses stability challenges when dynamic learning rules are implemented. In this talk I will describe a computational modelling approach, based on a dynamic attractor network model, that we used to show how hippocampal neural assemblies can evolve differently, depending on the frequency of presentation of the stimuli (Boscaglia et al., 2023). I will illustrate the design choices that we made in order to have dynamic memory representations and the behaviour of the model in different experimental paradigms involving memory formation, reinforcement and forgetting. It will be discussed how our computational results align with findings from single-cell recordings in the human hippocampus, making this model suitable to explore other memory coding mechanisms.
Speaker: Marta Boscaglia (University of Leicester, Visiting Collaborator’ at IMIM al Lab of Neural Mechanisms of Perception and Memory)
______________________________
Date: April 25th, 2024
Time: 18h
Place: UPF, ciutadella campus, Mercè Rodoreda building 24, room 24.S01.
Ring Attractor dynamics underlies the learning of stable working memory representations in a dual task
Abstract: Working memory (WM) refers to the part of short-term memory that temporarily holds and processes information when it is no longer accessible to the senses. It relies on briefly storing stimulus features in the activity of neuronal populations. Despite its crucial role in cognitive functions, WM is especially vulnerable due to its limited capacity and stability, especially when facing distractions from internal and external sources. Recent studies have explored the neural mechanisms that safeguard WM. It is proposed that pre- and post-distraction neural activity decomposes into orthogonal subspaces, thus protecting information. However, whether orthogonalization is innate or acquired through learning is unknown, and the network mechanisms supporting it are unclear. Here, we probe WM orthogonalization using calcium imaging data from the mouse prelimbic (PrL) and anterior cingulate (ACC) cortices as they learn to perform an olfactory dual task. The dual task combines an outer Delayed Paired-Association task (DPA) with an inner Go-NoGo task. We examined how neuronal activity reflected the process of shielding the DPA sample information against Go/NoGo distractors. As mice learned the task, we measured the overlap between the neural activity onto the low-dimensional subspaces that encode sample or distractor odors. Early in the training, pre-distraction activity overlapped with both sample and distractor subspaces. Later in the training, pre-distraction activity was strictly confined to the sample subspace, resulting in a more robust sample code. We present a mechanistic insight into how these low-dimensional WM representations evolve with learning in a recurrent neural network model of excitatory and inhibitory neurons with low-rank connections. The model links learning to (1) the orthogonalization of sample and distractor WM subspaces and (2) the orthogonalization of each subspace with irrelevant inputs. We validated (1) by measuring the angular distance between the sample and distractor subspaces through learning in the data. Prediction (2) was validated in PrL through the photoinhibition of ACC to PrL inputs, which turned back late training into early training dynamics. Moreover, our model constrains the network dynamics to a double-well attractor on a one-dimensional ring and suggests that learning optimally adjusts the location of the attractors on this ring. We validated this theoretical prediction by estimating an energy landscape for the recorded neural dynamics. In sum, our study underscores the crucial role attractor dynamics plays in shielding WM representations from distracting tasks.
Speaker: Alexandre Mahrach (IDIBAPS)
______________________________
Date: June 20th, 2024
Unconscious perception of visual stimuli in Blindsight patients from a Dynamic Functional Connectivity standpoint
Abstract:
The most insightful and direct access to non-conscious perception of visual signals is provided by studies of patients with lesions to the primary visual cortex (V1), resulting in a phenomenon called Blindsight. In this study, we implemented a Dynamic Functional Connectivity (DFC) analysis, to look at the brain dynamics and their relationship to Blindsight functions. Moreover, we evaluated the Effective Connectivity (EC) of each dynamical state through a whole brain modeling approach, in order to assess local differences in the brain functional structure of Blindsight patients.
Our DFC analysis involved 16 patients with right V1 lesions and 17 healthy subjects (HC). To assess the residual visual ability of the lesioned patients and categorize them into Blindsight positive (B+) and Blindsight negative (B-) we employed a behavioral task. Through a DFC analysis we defined a set of metastable states of brain dynamics. Then, we characterized how these attractors differ between the three groups, studying their dynamics and topological properties and evaluating their EC.
Based on clustering analysis of brain dynamics we found 4 metastable states. From a dynamical point of view, B+ show a higher permanence in the state related to the Default Mode Network (DMN). For what concerns B-, they exhibit a more probable self-transition within the somatomotor state. Looking at the topological properties of each state, we found an enhanced inter-hemispheric connectivity within the DMN state for B+. Moreover, looking at the integration of the information within the networks represented by each dynamical state, we observed an enhanced integration capability for B+ in the Global Signal (GS) state, i.e., a state of high synchronization of the activity throughout the brain. Thanks to the evaluation of the EC within each state, we observed an enhanced connectivity of subcortical structures, in particular contro-lesional Superior Colliculus and Lateral Geniculate Nucleus, with the somatomotor network, confirming the crucial role of subcortical structures in integrating visual information throughout the brain in Blindsight patients.
Speaker: Alessio Borriero (Università degli Studi di Torino and visitor at UPF)
______________________________
Date: June 20th, 2024
Exploring the dynamics of choice ensembles in the frontal cortex during a multiple-choice delayed-response task
Abstract:
During perceptual decision-making, sensory information is transformed into decisions through the recruitment of specific neural ensembles that represent the subsequent choice. Previous studies in mice highlight the role of the Anterolateral Motor cortex (ALM), an area showing a significant proportion of neurons selectively encoding the upcoming choice (choice-ensembles) and whose photo-suppression causes pronounced deficits in choice performance (Guo et al. 2014; Pinto et al 2022). Moreover, modeling work has proposed that ALM choice-ensembles operate in a winner-take-all regime implying that one and only one ensemble is active during the choice maintenance period (Inagaki et al 2019).
To characterize the dynamics of ALM ensembles involved in choice selection and maintenance, we developed a multiple-choice delayed-response task (nDRT) for freely moving mice, where a brief stimulus is presented at one of three possible locations on a touchscreen. Animals had to retain the information during a short variable delay (~1s), after which they must respond by poking at the remembered stimulus location. We found that errors increased as a function of delay, indicating that mice made memory maintenance errors. In addition, we found a consistent dependence on previous responses (i.e. win-stay-lose-switch pattern) that remained unaffected by delay. Pharmacological ALM inactivation using GABA receptor agonists resulted in a decrease in choice accuracy, confirming the involvement of the area in the task.
We then aimed to characterize the interaction between ALM neural ensembles representing choice. We used a c-fos-driven tagging method (TRAP2 system) for the permanent expression of channelrhodopsin in ALM choice-ensembles. We TRAPed a neural ensemble specific to one choice by performing a behavioral session with a single choice. In subsequent sessions, photoactivation of the TRAPed ensemble did not bias task behavior towards the labeled choice. Instead, when stimulating at 5Hz, but not at 20Hz, it specifically impaired the accuracy for the labeled choice. These results suggest that the photostimulation of a specific choice ensemble in ALM does not interfere with the activation of other choice ensembles, but rather has a frequency-dependent choice-specific deleterious effect. Our findings provide important constraints on the network dynamics governing frontal cortex circuits during choice selection and maintenance.
Speaker: Balma Serrano (IDIBAPS)
 |
For inquiries about this event please contact the Scientific Events Coordinator Ms. Núria Hernández at nhernandez@crm.cat
|