Pattern completion

The brain stores a large number of memories, which are recalled through external stimuli or spontaneously during memory replay, a process essential for memory consolidation. Memory storage has traditionally been attributed to excitatory synaptic plasticity and the effect of inhibitory plasticity on memory capacity and recall remains underexplored. To elucidate how inhibitory plasticity affects memory capacity and recall, we compare two inhibitory plasticity rules in spiking autoassociative networks: a homeostatic plasticity rule and a rule that maintains firing rate heterogeneity. Our results

Over the years, excitatory neurons received much attention for their role in brain dynamics, while the inhibitory neurons were understood to play more of a support, stabilizing role. However, recent advances have revealed that memories are encoded both by excitatory and inhibitory neurons (EI engrams), adding to the evidence of the functional importance of inhibitory neurons beyond mere stability. Still, the computations enabled by such EI engrams are unclear, and the synaptic plasticity rules that could create and sustain them are unknown. Here, we explore in silico a mechanism for EI engram

…CA3 thought to be the substrate of pattern completion [1]. However, its underlying computational and synaptic mechanisms are not yet understood. Here we analyze the mechanisms behind sequence replay through simulations of spiking neurons and a time-discrete model of moving membrane potential distributions. First, we simulated spiking networks of excitatory leaky integrate-and-fire cells forming a sequence of assemblies. The sequence contains both recurrent and feedforward connections (within and across assemblies, respect

How changes in synaptic connections underlie learning and memory is a central question in Neuroscience. Previous modeling efforts focused on biologically realistic learning rules and dynamics, but these rules usually produce bimodal weight distributions that are not aligned with experimentally observed lognormal distributions of synaptic weights. How biological learning rules preserve such unimodal distributions and at the same time retain information, i.e., learn, remains unknown. Here, we take a game-theoretical approach, asking whether learning is possible under the constraint of retaining

Network models of cortex typically focus on excitatory population dynamics, relegating inhibition to a support role. However, recent studies have shown that memories can be encoded by both excitatory and inhibitory neurons (EI engrams), adding to the evidence of the functional importance of inhibitory neurons beyond mere stabilization. The functional consequence of such EI engrams is unclear, and the synaptic plasticity rules that could sustain them are unknown. Here, we explore in silico a mechanism for EI engrams storage and recall to glean possible computational benefits of such engrams ove

Correct memory recall requires two cognitive processes. Pattern separation (PS) is an encoding process that enables the discrimination among similar experiences. Conversely, Pattern completion (PC) allows memory retrieval from partial information. In aging and Alzheimer’s disease (AD), the balance between PS and PC is shifted, in favor of PC over PS, inducing overgeneralization and overlapping memories. Mechanistically, PS and PC rely on the dentate gyrus (DG) and the CA3 hippocampal areas. We previously showed that the DG conditional knock-out of Vangl2 – a core Planar Cell Polarity (PCP) gen

Pattern separation and pattern completion are important functions of the hippocampal formation involving particularly the dentate gyrus (DG)-CA3 mossy fiber system. These functions allow for the differentiation of similar memories as well as a generalization of a memory trace in response to incomplete sensory inputs. The salience of hippocampus-dependent contextual fear memories is controlled by local somatostatin (SST)-positive interneurons that regulate the excitability of DG granule cells. In the current study we investigate if SST-positive interneurons contribute to fear memory specificity

The design of neural circuits, with large numbers of neurons interconnected in vast networks, strongly suggest that they are specifically build to generate emergent functional properties (1). To explore this hypothesis, we have developed two-photon holographic methods to selective image and manipulate the activity of neuronal populations in 3D in vivo (2). Using them we find that groups of synchronous neurons (neuronal ensembles) dominate the evoked and spontaneous activity of mouse primary visual cortex (3). Ensembles can be optogenetically imprinted for several days and some of their neurons