Biology subjects

Brinkman, B. A. W.

Publications and source records attributed to Brinkman, B. A. W..

5 recordsLinked to original sources

bioRxiv2023

Spiking network model of A1 learns temporal filters with frequency preferences

The sparse coding hypothesis has successfully predicted neural response properties of several sensory brain areas. For example, sparse basis representations of natural images match edge-detecting receptive fields observed in simple cells of primary visual cortex (V1), and sparse representations of natural sounds mimic auditory nerve waveforms. SAILnet, a leaky integrate-and-fire network model ("Sparse and Independently Local network") has previously been shown to learn simple V1 receptive fields when trained on natural images. Experimental work rewiring visual input to auditory cortex found that auditory neurons developed visual response properties, suggesting that developmental rules may be shared across sensory cortices. In this work we adapt SAILnet to train it on waveforms of auditory sounds and learn temporal receptive fields (filters), in contrast with previous work that trained SAILnet or other network models on spectrograms. In our model network of primary auditory cortex (A1) neurons receive synaptic current from input neurons who temporally filter the direct sound waveforms. To show the network learns frequency-dependent filters naturally, we do not parametrize the temporal filters, and only restrict the total number of time points in the filters. To make training feasible, we simplify the model to a single input neuron and 768 A1 neurons, and we train the network on "lo-fi" music, whose spectral power is limited to frequencies of[~] 10, 000 Hz or less, giving a manageable temporal resolution of the stimulus and filters. The learned filters develop distinct frequency preferences, and reconstruction of novel stimuli captures the low-frequency content of signals in reasonable detail, with audio playback capturing clear aspects of the original stimulus. Lastly, our work also has a pedagogical benefit: the learned stimulus features can be played as sounds, which aids in teaching sensory coding to learners with visual impairments who cannot perceive stimulus features learned by V1 models.

neuroscience↗

bioRxiv2023

Statistically inferred neuronal connections in subsampled neural networks strongly correlate with spike train covariance

Statistically inferred neuronal connections from observed spike train data are often skewed from ground truth by factors such as model mismatch, unobserved neurons, and limited data. Spike train covariances, sometimes referred to as "functional connections," are often used as a proxy for the connections between pairs of neurons, but reflect statistical relationships between neurons, not anatomical connections, and moreover are not casual. Connections inferred by maximum likelihood inference, by contrast, can be constrained to be causal. However, we show in this work that the inferred connections in spontaneously active networks modeled by stochastic leaky integrate-and-fire networks strongly reflect covariances between neurons, not causal information, when many neurons are unobserved or when neurons are weakly coupled. This phenomenon occurs across different network structures, including random networks and balanced excitatory-inhibitory networks.

neuroscience↗

bioRxiv2021

Evolution of innate behavioral strategies through competitive population dynamics

Many organism behaviors are innate or instinctual and have been "hard-coded" through evolution. Current approaches to understanding these behaviors model evolution as an optimization problem in which the traits of organisms are assumed to optimize an objective function representing evolutionary fitness. Here, we use a mechanistic birth-death dynamics approach to study the evolution of innate behavioral strategies in a population of organisms in silico. In particular, we performed agent-based stochastic simulations and mean-field analyses of organisms exploring random environments and competing with each other to find locations with plentiful resources. We find that when organism density is low, the mean-field model allows us to derive an effective objective function, predicting how the most competitive phenotypes depend on the exploration-exploitation trade-off between the scarcity of high-resource sites and the increase in birth rate those sites offer organisms. However, increasing organism density alters the most competitive behavioral strategies and precludes the existence of a well-defined objective function. Moreover, there exists a range of densities for which the coexistence of many phenotypes persists for evolutionarily long times.

animal behavior and cognition↗

bioRxiv2020

Dysregulation of neural excitability replicates physiological and functional changes in aging visual cortex

The mammalian visual system has been the focus of countless experimental and theoretical studies designed to elucidate principles of neural computation and sensory coding. Most theoretical work has focused on networks intended to reflect developing or mature neural circuitry, in both health and disease. Few computational studies have attempted to model changes that occur in neural circuitry as an organism ages non-pathologically. In this work we contribute to closing this gap, studying how physiological changes correlated with advanced age impact the computational performance of a spiking network model of primary visual cortex (V1). Our results demonstrate that deterioration of homeostatic regulation of excitatory firing, coupled with long-term synaptic plasticity, is a sufficient mechanism to reproduce features of observed physiological and functional changes in neural activity data, specifically declines in inhibition and in selectivity to oriented stimuli. This suggests a potential causality between dysregulation of neuron firing and age-induced changes in brain physiology and performance. While this does not rule out deeper underlying causes or other mechanisms that could give rise to these changes, our approach opens new avenues for exploring these underlying mechanisms in greater depth and making predictions for future experiments.

neuroscience↗

bioRxiv2017

Effective synaptic interactions in subsampled nonlinear networks with strong coupling

A major obstacle to understanding neural coding and computation is the fact that experimental recordings typically sample only a small fraction of the neurons in a circuit. Measured neural properties are skewed by interactions between recorded neurons and the \"hidden\" portion of the network. To properly interpret neural data and determine how biological structure gives rise to neural circuit function, we thus need a better understanding of the relationships between measured effective neural properties and the true underlying physiological properties. Here, we focus on how the effective spatiotemporal dynamics of the synaptic interactions between neurons are reshaped by coupling to unobserved neurons. We find that the effective interactions from a pre-synaptic neuron r' to a post-synaptic neuron r can be decomposed into a sum of the true interaction from r' to r plus corrections from every directed path from r' to r through unobserved neurons. Importantly, the resulting formula reveals when the hidden units have--or do not have--major effects on reshaping the interactions among observed neurons. As a particular example of interest, we derive a formula for the impact of hidden units in random networks with \"strong\" coupling--connection weights that scale with [Formula], where N is the network size, precisely the scaling observed in recent experiments. With this quantitative relationship between measured and true interactions, we can study how network properties shape effective interactions, which properties are relevant for neural computations, and how to manipulate effective interactions.

neuroscience↗