Biology subjects

Timme, N. M.

Publications and source records attributed to Timme, N. M..

5 recordsLinked to original sources

bioRxiv2026

Precise measurement of rodent drinking using CLiQR (Capacitive Lick Quantification in Rodents)

Accurate quantification of rodent licking behavior is essential for studies of fluid intake, including investigations of alcohol use disorder and obesity. Lickometry systems vary widely in sensing modality, cost, scalability, and data resolution. Also, many available systems require specialized housing or store only binary lick/no lick data based on thresholding. Here, we present CLiQR (Capacitive Lick Quantification in Rodents), an open-source capacitive lickometry system designed for high-throughput recording of licking behavior in home-cage environments while preserving the full capacitance time series. The system uses MPR121 capacitive sensors connected to custom metal-tipped serological pipette sippers and a centralized desktop computer to record data from up to 24 mice concurrently, with capacity for two-bottle choice experiments. Validation experiments demonstrated that the capacitive signals reliably distinguish licking from non-licking interactions. Total lick counts showed a positive correlation with measured fluid consumption (R2 = 0.849, p < 0.0001), confirming that detected events provide a meaningful proxy for intake. All information necessary to reproduce the system is shared openly in this manuscript and online. By combining scalability, full-trace data acquisition, and low cost, CLiQR provides a flexible and extensible platform for high-throughput behavioral neuroscience experiments and enables retrospective improvement of lick-detection algorithms.

animal behavior and cognition↗

bioRxiv2020

Synergistic neural computation is greater downstream of recurrent connectivity in organotypic cortical cultures

The directionality of network information flow dictates how networks process information. A central component of information processing in both biological and artificial neural networks is their ability to perform synergistic integration-a type of computation. We established previously that synergistic integration varies directly with the strength of feedforward information flow. However, the relationships between both recurrent and feedback information flow and synergistic integration remain unknown. To address this, we analyzed the spiking activity of hundreds of neurons in organotypic cultures of mouse cortex. We asked how empirically observed synergistic integration varied with local functional network structure that was categorized into motifs with varying recurrent and feedback information flow. We found that synergistic integration was elevated in motifs with greater recurrent information flow beyond that expected from the local feedforward information flow. Feedback information flow was interrelated with feedforward information flow and was associated with decreased synergistic integration. Our results indicate that synergistic integration is distinctly influenced by the directionality of local information flow. Author SummaryNetworks compute information. That is, they modify inputs to generate distinct outputs. These computations are an important component of network information processing. Knowing how the routing of information in a network influences computation is therefore crucial. Here we asked how a key form of computation--synergistic integration--is related to the direction of local information flow in networks of spiking cortical neurons. Specifically, we asked how information flow between input neurons (i.e., recurrent information flow) and information flow from output neurons to input neurons (i.e., feedback information flow) was related to the amount of synergistic integration performed by output neurons. We found that greater synergistic integration occurred where there was more recurrent information flow. And, lesser synergistic integration occurred where there was more feedback information flow relative to feedforward information flow. These results show that computation, in the form of synergistic integration, is distinctly influenced by the directionality of local information flow. Such work is valuable for predicting where and how network computation occurs and for designing networks with desired computational abilities.

neuroscience↗

bioRxiv2019

Correlated activity favors synergistic processing in local cortical networks at synaptically-relevant timescales

Neural information processing is widely understood to depend on correlations in neuronal activity. However, whether correlation is favorable or not is contentious. Here, we sought to determine how correlated activity and information processing are related in cortical circuits. Using recordings of hundreds of spiking neurons in organotypic cultures of mouse neocortex, we asked whether mutual information between neurons that feed into a common third neuron increased synergistic information processing by the receiving neuron. We found that mutual information and synergistic processing were positively related at synaptic timescales (0.05-14 ms), where mutual information values were low. This effect was mediated by the increase in information transmission--of which synergistic processing is a component--that resulted as mutual information grew. However, at extrasynaptic windows (up to 3000 ms), where mutual information values were high, the relationship between mutual information and synergistic processing became negative. In this regime, greater mutual information resulted in a disproportionate increase in redundancy relative to information transmission. These results indicate that the emergence of synergistic processing from correlated activity differs according to timescale and correlation regime. In a low-correlation regime, synergistic processing increases with greater correlation, and in a high correlation regime, synergistic processing decreases with greater correlation. AUTHOR SUMMARYIn the present work, we address the question of whether correlated activity in functional networks of cortical circuits supports neural computation. To do so, we combined network analysis with information theoretic tools to analyze the spiking activity of hundreds of neurons recorded from organotypic cultures of mouse somatosensory cortex. We found that, at timescales most relevant to direct neuronal communication, neurons with more correlated activity predicted greater computation, suggesting that correlated activity does support computation in cortical circuits. Importantly, this result reversed at timescales less relevant to direct neuronal communication, where even greater correlated activity predicted decreased computation. Thus, the relationship between correlated activity and computation depends on the timescale and the degree of correlation in neuronal interactions.

neuroscience↗

bioRxiv2018

Encoding of the Intent to Drink Alcohol by the Prefrontal Cortex is blunted in Rats with a Family History of Excessive Drinking

The prefrontal cortex plays a central role in guiding decision-making, and its function is altered by alcohol use and an individuals innate risk for excessive alcohol drinking. The primary goal of this work was to determine how neural activity in the prefrontal cortex guides the decision to drink. Towards this goal, the within-session changes in neural activity were measured from medial prefrontal cortex (mPFC) of rats performing a drinking procedure that allowed them to consume or abstain from alcohol in a self-paced manner. Recordings were obtained from rats that either lacked or expressed an innate risk for excessive alcohol intake - Wistar or Alcohol Preferring P rats, respectively. Wistar rats exhibited patterns of neural activity consistent with the intention to drink or abstain from drinking, whereas these patterns were blunted or absent in P rats. Collectively, these data indicate that neural activity patterns in mPFC associated with the intention to drink alcohol are influenced by innate risk for excessive alcohol drinking. This observation may indicate a lack of control over the decision to drink by this otherwise well-validated supervisory brain region.

neuroscience↗

bioRxiv2018

Computation is concentrated in rich clubs of local cortical neurons

To understand how neural circuits process information, it is essential to identify the relationship between computation and circuit topology. Rich-clubs, highly interconnected sets of neurons, are known to propagate a disproportionate amount of information within cortical circuits. Here, we test the hypothesis that rich-clubs also perform a disproportionate amount of computation. To do so, we recorded the spiking activity of on average [~]300 well-isolated individual neurons from organotypic cortical cultures. We then constructed weighted, directed networks reflecting the effective connectivity between the neurons. For each neuron, we quantified the amount of computation it performed based on its inputs. We found that rich-club neurons compute [~]200% more information than neurons outside of the rich club. Indeed, the amount of computation performed in the rich-club was proportional to the amount information propagation by the same neurons. This suggests that, in these circuits, information propagation drives computation. Comparing the computation-to-propagation ratio inside versus outside of the rich club showed that rich clubs compute at a slightly, though significantly, reduced level ([~]4% lower). In total, our findings indicate that rich club topology in effective cortical circuits supports not only information propagation but also neural computation.\n\nAUTHOR SUMMARYHere we answer the question of whether rich club topology in functional cortical circuits supports neural computation as it has been previously shown to do for information propagation. To do so, we combined network analysis with information theoretic tools to analyze the spiking activity of hundreds of neurons recorded from organotypic cultures of mouse somatosensory cortex. We found that neurons in rich clubs computed significantly more than neurons outside of rich clubs, suggesting that rich-clubs do support computation in cortical circuits. Indeed, the amount of computation that we found in the rich club was proportional to the amount of information they propagate suggesting that, in these circuits, information propagation drives computation.

neuroscience↗