Biology subjects

Assisi, C.

Publications and source records attributed to Assisi, C..

5 recordsLinked to original sources

bioRxiv2026

Network topology determines neuronal phase-of-firing codes

Brain rhythms organize neural activity in time. When a shared oscillation is broadcast across a population of neurons, the phase at which the neurons fire with respect to the oscillation can itself carry information, a strategy the brain appears to use for navigation, memory, and sensory perception. In such a phase-of-firing code, information is conveyed not by how rapidly a neuron fires but by when it fires relative to the phase of the ongoing rhythm. Models that implement such phase-of-firing codes consider the neurons as independent. How such codes arise in cortical and hippocampal circuits, where neurons are densely interconnected rather than independent, remains unclear. Here we show that recurrent connectivity does not merely permit phase-of-firing codes but actively determines which patterns the network can hold onto. Using networks of model neurons wired in a manner determined by the constraints of a Sudoku puzzle, we find that the network supports an enormous repertoire of distinct rhythmic firing patterns, each corresponding to a valid Sudoku solution and selectable by brief targeted inputs against a common background oscillation. Some patterns persist after the input is removed while others dissolve. We show that a factor deciding this selectivity is network symmetry. Patterns that respect the symmetries of the wiring are stable, while those that break them are not, and small perturbations can switch the network between stable patterns. Adjusting synaptic weights to remove asymmetry stabilizes patterns that were previously unstable, giving the network a tunable memory. More broadly, our results suggest that a common oscillatory drive, targeted biases to the neurons, and recurrent network structure, act together as complementary control variables. The oscillation sets up a temporal scaffold, the biases select spatiotemporal patterns, and the connectivity determines the stability of the chosen patterns. Tuning these complementary variables can predictably and flexibly reshape the repertoire of activity a circuit expresses. Significance StatementMany brain regions generate rhythmic activity, and the precise moment at which individual neurons fire within each rhythm carries information that the brain can harness to encode memory, perception and location as an animal navigates its environment. In such a phase-of-firing code, information is conveyed not by how rapidly a neuron fires but by when it fires relative to the phase of the ongoing network rhythm. Existing theories of how phase-of-firing codes arise have modeled neurons as independent, leaving open how such codes emerge in the densely interconnected circuits of cortex and hippocampus. Using model networks wired to reflect the constraints of the Sudoku puzzle, we show that recurrent connectivity supports a vast repertoire of distinct rhythmic firing patterns, and that the symmetries of the wiring determines which of those patterns the network can stably hold. This identifies network symmetry as a general principle relating circuit architecture to temporal coding.

neuroscience↗

bioRxiv2025

Intrinsic and circuit mechanisms of predictive coding in a grid cell network model

Grid cells in the medial entorhinal cortex (MEC) fire at the vertices of a hexagonal lattice, forming an allocentric code for the animal's current position. Recent studies have identified a class of grid cells that represent locations ahead of the animal. How do these predictive representations emerge from the wetware of the MEC? We developed a detailed conductance-based model of the MEC network, constrained by empirical data on the biophysical properties of stellate cells and the topology of the MEC network. The model revealed two mechanisms by which grid cells can signal future locations. First, hyperpolarizing inhibition from interneurons activates HCN channels in stellate cells, whose slow kinetics maintain a depolarizing influence after inhibition ends, advancing spike timing and shifting the inferred position forward by ~5% of a grid field diameter. Second, introducing asymmetry into the inhibitory connectivity, by skewing the Gaussian profile of interneuron-to-stellate connections, causes inhibition to rise more steeply and enables earlier spiking, advancing the inferred position by up to ~25%. A corollary of our model is that the extent of the predictive code changes monotonically along the dorsoventral axis of the MEC, following the experimentally measured dorsoventral gradient in HCN time constants.

neuroscience↗

bioRxiv2019

Maximizing the coding capacity of neuronal networks

Information in neuronal networks is encoded as spatiotemporal patterns of activity. The capacity of a network may thus be thought of as the number of stable spatiotemporal patterns it can generate. To understand what structural attributes of a network enable it to generate a profusion of stable patterns, we simulated an array of 9 x 9 neurons modelled as pulse-coupled oscillators. The structure of the network was inspired by the popular puzzle Sudoku such that its periodic responses mapped to solutions of the puzzle. Given that there are nearly a 109 possible Sudokus, this networks could possibly generate 109 spatiotemporal patterns. We show that the number of stable patterns were maximized when excitatory and inhibitory inputs to each neuron were balanced. When this balance was disrupted, only a subset of patterns with certain symmetries survived.

neuroscience↗

bioRxiv2019

Theta oscillations gate the transmission of reliable sequences in the medial entorhinal cortex

Reliable sequential activity of neurons in the entorhinal cortex is necessary to encode spatially guided behavior and memory. In a realistic computational model of a medial entorhinal cortex (MEC) microcircuit, with stellate cells coupled via a network of inhibitory interneurons, we show how intrinsic and network mechanisms interact with theta oscillations to generate reliable outputs. Sensory inputs activate interneurons near their most excitable phase during each theta cycle. As the inputs change, different groups of interneurons are recruited and postsynaptic stellate cells are released from inhibition causing a sequence of rebound spikes. Since the rebound time scale of stellate cells matches theta oscillations, its spikes get relegated to the least excitable phase of theta ensuring that the network encodes only the external drive and ignores recurrent excitation by rebound spikes. In the absence of theta, rebound spikes compete with external inputs and disrupt the sequence that follows. Our simulations concur with experimental data that show, subduing theta oscillations disrupts the spatial periodicity of grid cell receptive fields. Further, the same mechanism where theta modulates the gain of incoming inputs may be used to select between competing sources of input and create transient functionally connected networks.

neuroscience↗

bioRxiv2019

Optimal sparse olfactory representations persist in a plastic network

The neural representation of a stimulus is repeatedly transformed as it moves from the sensory periphery to deeper layers of the nervous system. Sparsening transformations are thought to increase the separation between similar representations, encode stimuli with great specificity, maximize storage capacity as associative memories, and provide an energy efficient instantiation of information in neural circuits. In the insect olfactory system, odors are initially represented in the periphery as a combinatorial code with simple temporal dynamics. Subsequently, in the antennal lobe this representation is transformed into a dense spatiotemporal activity pattern. Next, in the mushroom body Kenyon cells (KCs), the representation is dramatically sparsened. Then in mushroom body output neurons (MBONs), the representation takes on a new dense spatiotemporal format. Here, we develop a computational model to simulate this chain of olfactory processing from the receptor neurons to MBONs. We demonstrate that representations of similar odorants are maximally separated, measured by the distance between the corresponding MBON activity vectors, when KC responses are sparse and that the sparseness is maintained across variations in odor concentration by adjusting the feedback inhibition KCs receive. Different odor concentrations require different strength and timing of feedback inhibition for optimal processing. Further, in vivo, the KC-MBON synapse is highly plastic, and changes in synaptic strength after learning can change the balance of excitation and inhibition and may lead to a change in the distance between MBON activity vectors of two odorants for the same level of KC population sparseness. Thus, what is an optimal degree of sparseness before odor learning, could be rendered sub-optimal post learning. Here, we show, however, that synaptic weight changes caused by spike timing dependent plasticity increase the distance between the odor representations from the perspective of MBONs and do not lead to a concomitant change in the optimal sparseness. Author SummaryKenyon cells (KCs) of the mushroom body represent odors as a sparse code. When viewed from the perspective of follower neurons, mushroom body output neurons (MBONs), an optimal level of KC sparseness maximally separates the representations of odors. However, the KC-MBON synapse is highly plastic and may be potentiated or depressed by odor-driven experience that could, in turn, perturb the optimality formed by pre-synaptic circuits. Contrary to this expectation, we show that synaptic plasticity based on spike timing of pre- and postsynaptic neurons improves the ability of the system to distinguish between the representations of similar odors while preserving the optimality determined by pre-synaptic circuits.

neuroscience↗