Topic: recurrent connectivity

Experimental and computational studies suggest that motor cortex acts as a feedback controller, allowing for ‘on-the-fly’ movement corrections in response to afferent sensory feedback. However, it remains unclear whether feedback control relates to longer-term learning, and how this would be implemented in neural circuitry. Here, we tackled these questions by testing how a recurrent neural network (RNN) can use feedback to control its own output, and whether this process can enable learning. We built an RNN that received feedback signaling the error between its intended and observed output. An

Neural integrators are circuits that are able to code analog information such as spatial location or amplitude. Storing amplitude requires the network to have a large number of attractors. In classic models with recurrent excitation, such networks require very careful tuning to behave as integrators and are not robust to small mistuning of the recurrent weights. In this talk, I introduce a circuit with recurrent connectivity that is subjected to a slow subthreshold oscillation (such as the theta rhythm in the hippocampus). I show that such a network can robustly maintain many discrete attrac

In mammals, the selective transformation of transient experience into stored memory occurs in the hippocampus, which develops representations of specific events in the context in which they occur. In this talk, I will focus on the development of hippocampal circuits and the self-organized dynamics embedded in them since the latter critically support the role of the hippocampus in memory. I will discuss evidence that adult hippocampal cells and circuits are remarkably sculpted by development, as early as embryonic neurogenesis. We argue that these primary developmental programs provide a scaffo

All-optical techniques play a pivotal role in the direct interrogation of biological neural networks and the investigation of their computational properties. Such techniques combine spatiotemporally precise optogenetic stimulation at cellular resolution to manipulate neuronal states in the network with optical imaging of fluorescent genetically-encoded indicators for simultaneous readout of neuronal activity [1, 2]. The biophysical models of various optogenetic tools have been studied computationally in single neurons, accurately fitting experimental data [3, 4]. However, the effects of optoge

A hallmark of biological and artificial neural networks is that neurons tile the range of continuous sensory inputs and intrinsic variables with overlapping responses. It is characteristic for the underlying recurrent connectivity in the cortex that neurons with similar tuning predominantly excite each other. The reason for such an architecture is not clear. Using an analytically tractable model, we show that it can naturally arise from a cooperative coding scheme. In this scheme neurons with similar responses specifically support each other by sharing their computations to obtain the desired

Task-trained recurrent spiking neural networks (RSNNs) can provide insights into how the brain performs spike-based computations, especially those involved in temporal tasks. Training RSNNs with backpropagation through time (BPTT) faces the challenge of non-differentiable spiking functions, requiring an approximation gradient through each time state of the network. Evolutionary Algorithms (EAs) offer an alternative to BPTT by generating random populations of models and selecting those with the best performance to provide a broader initial search space and the ability to optimize non-differenti

A primary goal in systems neuroscience is to understand how neuronal connectivity impacts the activity and dynamics in mechanistic models of neural processing. New connectome datasets of the fruit fly provide access to the connectivity of every neuron in its neural circuits. However, the functional role of recurrent connections in the fruit fly remains incompletely understood. For instance, although its visual system is predominantly feedforward, strong feedback connections have been identified in its motion detecting pathways [1]. What is the role of recurrent connections in the visual percep

Two perspectives can be distinguished in computational neuroscience towards understanding the link between neuronal activity and behaviour.$^1$ On the one hand, focusing on single-neuron responses highlighted that a few functional properties, such as neuronal tuning$^2$ or selectivity,$^3$ often characterise individual responses. These properties determine each neuron's “position” in an abstract space, which we call the “(functional) similarity space”: for example, each cell's preferred head direction (HD) in the mouse HD system determines its position on an abstract ring.$^4$ It is commonly h

Coherent oscillations of neuronal activity are ubiquitous across brain spatial and temporal scales. Gamma oscillations (30-120Hz), detectable with Local Field Potentials (LFPs), are classically analyzed through massive trial averages. This approach leaves us with one average oscillation shape, one mean power and a view based on rigid frequency bands. Consistently, mechanisms based on biophysical models of neural networks typically predict periodic oscillations (e.g. PING models [1]). The functional role of oscillations have been largely investigated in the hippocampus, where, different gamma-f