Biology subjects

Buonomano, D.

Publications and source records attributed to Buonomano, D..

6 recordsLinked to original sources

bioRxiv2025

Hippocampal sequences represent working memory and implicit timing

Working memory (WM) and timing are considered distinct cognitive functions, yet the neural signatures underlying both can be similar. To address the hypothesis that WM and timing may be multiplexed we developed a novel rodent task where 1st odor identity predicts the delay duration. We found that WM performance decreased when delay expectations were violated. Performance was worse for unexpected long delays than for unexpected short delays, suggesting that WM may be tuned to expire in a delay-dependent manner. Calcium imaging of dorsal CA1 neurons revealed odor-specific sequential activity tiling the short and long delays. Neural sequence structure also reflected expectation of the timing of the 2nd odor--i.e., of the expected delay. Consistent with the hypothesis that WM and timing may be multiplexed, our findings suggest that neural sequences in dorsal CA1 may encode cues and cue-specific elapsed time during the delay period of a WM task.

neuroscience↗

bioRxiv2024

Ex Vivo Cortical Circuits Learn to Predict and Spontaneously Replay Temporal Patterns

It has been proposed that prediction and timing are computational primitives of neocortical microcircuits, specifically, that neural mechanisms are in place to allow neocortical circuits to autonomously learn the temporal structure of external stimuli and generate internal predictions. To test this hypothesis, we trained cortical organotypic slices on two specific temporal patterns using dual-optical stimulation. After 24-hours of training, whole-cell recordings revealed network dynamics consistent with training-specific timed prediction. Unexpectedly, there was replay of the learned temporal structure during spontaneous activity. Furthermore, some neurons exhibited timed prediction errors. Mechanistically our results indicate that learning relied in part on asymmetric connectivity between distinct neuronal ensembles with temporally-ordered activation. These findings further suggest that local cortical microcircuits are intrinsically capable of learning temporal information and generating predictions, and that the learning rules underlying temporal learning and spontaneous replay can be intrinsic to local cortical microcircuits and not necessarily dependent on top-down interactions.

neuroscience↗

bioRxiv2023

Cross-homeostatic plasticity enables analog neuromorphic circuits to exhibit robust computational primitives

AbstractMany neural computations emerge from self-sustained patterns of activity in recurrent neural circuits, which rely on balanced excitation and inhibition. Neuromorphic electronic circuits that use the physics of silicon to emulate neuronal dynamics represent a promising approach for implementing the brains computational primitives, including self-sustained neural activity. However, achieving the same robustness of biological networks in neuromorphic computing systems remains a challenge, due to the high degree of heterogeneity and variability of their analog components. Inspired by the strategies used by real cortical networks, we apply a biologically-plausible cross-homeostatic learning rule to balance excitation and inhibition in neuromorphic implementations of spiking recurrent neural networks. We demonstrate how this learning rule allows the neuromorphic system to work in the presence of device mismatch and to autonomously tune the spiking network to produce robust, self-sustained, fixed-point attractor dynamics with irregular spiking in an inhibition-stabilized regime. We show that this rule can implement multiple, coexisting stable memories, with emergent soft-winner-take-all (sWTA) dynamics, and reproduce the so-called "paradoxical effect" widely observed in cortical circuits. In addition to validating neuroscience models on a substrate that shares many similar properties and limitations with biological systems, this work enables the construction of ultra-low power, mixed-signal neuromorphic technologies that can be automatically configured to compute reliably, despite the large on-chip and chip-to-chip variability of their analog components.

neuroscience↗

bioRxiv2022

Improvement of sensory deficits in Fragile X mice by increasing cortical interneuron activity after the critical period

Changes in the function of inhibitory interneurons (INs) during cortical development could contribute to the pathophysiology of neurodevelopmental disorders. Using all-optical in vivo approaches in postnatal mouse somatosensory cortex (S1), we find that parvalbumin (PV) IN precursors are hypoactive and decoupled from excitatory neurons in Fmr1-/- mice, a model of Fragile X Syndrome (FXS). This leads to a loss of PV-INs in both mice and humans with FXS. Increasing the activity of future PV-INs in neonatal Fmr1-/- mice restores PV density and ameliorates transcriptional dysregulation in S1, but not circuit dysfunction. Critically, administering a novel allosteric modulator of Kv3.1 channels after the S1 critical period does rescue circuit dynamics and tactile defensiveness. Symptoms in FXS and related disorders could be mitigated by targeting PV-INs.

neuroscience↗

bioRxiv2021

Orchestrated Excitatory and Inhibitory Learning Rules Lead to the Unsupervised Emergence of Up-states and Balanced Network Dynamics

Self-sustaining neural activity maintained through local recurrent connections is of fundamental importance to cortical function. We show that Up-states--an example of self-sustained, inhibition-stabilized network dynamics--emerge in cortical circuits across three weeks of ex vivo development, establishing the presence of unsupervised learning rules capable of generating self-sustained dynamics. Previous computational models have established that four sets of weights (WE[<-]E, WE[<-]I, WI[<-]E, WI[<-]I) must interact in an orchestrated manner to produce Up-states, but have not addressed how a family of learning rules can operate in parallel at all four weight classes to generate self-sustained inhibition-stabilized dynamics. Using numerical and analytical methods we show that, in part due to the paradoxical effect, standard homeostatic rules are only stable in a narrow parameter regime. In contrast, we show that a family of biologically plausible learning rules based on "cross-homeostatic" plasticity robustly lead to the emergence of self-sustained, inhibition-stabilized dynamics.

neuroscience↗

bioRxiv2020

The Orbitofrontal Cortex in Temporal Cognition

One of the most important factors in decision making is estimating the value of available options. Subregions of the prefrontal cortex, including the orbitofrontal cortex (OFC), have been deemed essential for this process. Value computations require a complex integration across numerous dimensions, including, reward magnitude, effort, internal state, and time. The importance of the temporal dimension is well-illustrated by temporal discounting tasks, in which subjects select between smaller-sooner versus larger-later rewards. The specific role of OFC in telling time and integrating temporal information into decision making remains unclear. Based on the current literature, in this review we reevaluate current theories of OFC function, accounting for the influence of time. Incorporating temporal information into value estimation and decision making requires distinct, yet interrelated, forms of temporal information including the ability to tell time, represent time, create temporal expectations, and the ability to use this information for optimal decision making in a wide range of tasks, including temporal discounting and wagering. We use the term temporal cognition to refer to the integrated use of these different aspects of temporal information. We suggest that the OFC may be a critical site for the integration of reward magnitude and delay, and thus important for temporal cognition.

neuroscience↗