Biology subjects

Richards, B.

Publications and source records attributed to Richards, B..

16 recordsLinked to original sources

bioRxiv2026

A generalizable speech neuroprosthesis

Intracortical brain-computer interfaces (BCIs) can restore communication to people with vocal tract paralysis by decoding cortical activity during attempted speech into text. State-of-the-art systems pairing neural-to-phoneme decoders with phoneme-to-word language models have achieved word error rates (WERs) as low as 1%, but only after collecting thousands of sentences of training data. Shortening the data collection process would facilitate scaling this new technology by reducing the time from device implant to high-accuracy communication. Here we introduce a transformer-based decoder model trained jointly across six intracortical speech BCI participants. For every participant -- regardless of sex, disease etiology, or attempted speaking strategy -- a multi-user model decoded speech more accurately (over 50% lower relative WER on average) than models trained on individual users data. Notably, the multi-user model could be finetuned on fewer than 200 sentences from a held-out user to achieve a WER below 7%. These results reveal how to pool intracortical data across people to yield more accurate, generalizable, and rapidly-deployable decoding models.

bioengineering↗

bioRxiv2026

Sex differences in neural circuits driving binge drinking: A female-specific role for an amygdalo-striatal pathway

BackgroundRates of binge drinking have converged significantly between the sexes over recent decades, driven by increased rates of alcohol misuse in women. However, understanding of fundamental circuitry and neurobiology driving alcohol use in females, or how this may differ from male subjects remains underexplored. MethodsWe quantified c-Fos expression across 40 brain regions in alcohol naive, alcohol anticipating and binge drinking male and female mice. In vivo fiber photometry examined sex differences in basolateral amygdala (BLA) activity changes to alcohol intake. Chemogenetic BLA inhibition investigated a functional role in binge drinking. We then assessed sex differences in BLA efferent projection activation following binge drinking. Finally, we functionally interrogated the BLA to nucleus accumbens core (AcbC) projection in binge drinking. ResultsBinge drinking reduced network modularity (number of communities with similar activation patterns) in both sexes relative to alcohol naive and anticipating same-sex counterparts. Female binge drinking mice had increased BLA c-Fos expression compared to female naive and male binge drinking counterparts. In vivo fiber photometry revealed greater and more prolonged BLA responsivity at the onset of alcohol intake in females. Global BLA inhibition reduced reward intake in both sexes. However, the BLA to AcbC projection was preferentially activated in female binge drinking mice, and inhibition of this pathway reduced binge alcohol intake exclusively in females. ConclusionsWe identified sex differences in the neural circuits engaged in binge drinking, highlighting the BLA to AcbC projection may in part underpin sex differences in alcohol misuse. This provides further evidence of distinct neurobiological drivers of alcohol-related behaviors between the sexes.

neuroscience↗

bioRxiv2026

Distinct CA1 inputs support shifts in neural dimensionality and memory resolution

Event-based memories may be encoded with high (e.g., episodic) or low (e.g., gist) mnemonic resolution. While the CA1 region of the hippocampus encodes events at both scales, it is unclear how such dual coding emerges. Consistent with theoretical predictions1,2, here we show that trisynaptic (from CA3) and monosynaptic (from the medial entorhinal cortex) projections to CA1 encode high- and low-resolution event-based memories, respectively. Recruitment of the trisynaptic pathway during contextual fear conditioning promotes feedforward inhibition of CA1 via activation of parvalbumin-positive (PV+) interneurons. This allows sparse encoding of event-based memories in high-dimensional neural states that support high-resolution contextual fear memories, where mice exhibit conditioned fear only in the training context and not similar contexts. Inhibiting this input during training reduces activation of CA1 PV+ interneurons and feedforward inhibition, shifting neural dynamics to low-dimensional states that support low-resolution contextual fear memories where mice freeze in the training context and similar contexts. These experiments identify a circuit-based mechanism that causally links the dimensionality of CA1 neural dynamics to mnemonic resolution.

neuroscience↗

bioRxiv2025

Preparatory encoding of diverse features of intended movement in the human motor cortex

Over the course of a voluntary movement, motor cortical activity exhibits a transition from preparation to execution, with markedly different activity across these phases. Preparatory activity in particular might be used to improve brain-computer interfaces (BCIs) that harness brain activity to control external assistive devices, for example by anticipating a users intended movement trajectory for quick and fluid performance. However, to leverage preparatory activity for clinical BCIs, we must first understand which features of upcoming movements are encoded by preparatory activity in humans. In this work, we collected intracortical recordings from 3 research participants in the BrainGate2 clinical trial to investigate whether diverse features of movement, such as direction, curvature, and distance, are encoded by preparatory activity in the human motor cortex. We first show that preparatory activity is tuned to the direction of upcoming movements, and this tuning is largely preserved across movements with different effectors. Further investigation demonstrated this preparatory activity is also informative of initial and endpoint directions of curved movement trajectories, and encodes movement distance and speed independently. Finally, we present an online control paradigm that leverages preparatory activity to predict movements towards intended directions in advance, yielding rapid, self-paced control of a computer cursor by human participants. Altogether, these results demonstrate that preparatory activity in the human motor cortex encodes rich features of upcoming movement, highlighting its potential use for high performance brain-computer interface applications.

neuroscience↗

bioRxiv2025

RetINaBox: A hands-on learning tool for experimental neuroscience

An exciting aspect of neuroscience is developing and testing hypotheses via experimentation. However, due to logistical and financial hurdles, the experiment and discovery component of neuroscience is generally lacking in classroom and outreach settings. To address this issue, here we introduce RetINaBox: a low-cost open-source electronic visual system simulator that provides users with a hands-on tool to discover how the visual system builds feature detectors. RetINaBox features an LED array for generating visual stimuli and a photodiode array that acts as a mosaic of model photoreceptors. Custom software on a Raspberry Pi computer reads out responses from model photoreceptors and allows users to control the polarity and delay of the signal transfer from model photoreceptors to model retinal ganglion cells. Interactive lesson plans are provided, guiding users to discover different types of visual feature detectors--including ON/OFF, center-surround, orientation selective, and direction selective receptive fields--as well as their underlying circuit computations.

neuroscience↗

bioRxiv2025

Assessing the Potential of Switchgrass (Panicum virgatum L.) for Storing Carbon Belowground: Insights from a Multi-Site US Study

Agricultural intensification depletes belowground carbon (C) stocks, partly due to the exclusion of deep-rooted perennial species from landscapes where they were once dominant. Reintroducing deep-rooted perennials to cultivated land may help to mitigate SOC loss and restore ecosystem function. We quantified the effect of replacing shallow-rooted annual crops with a deep-rooted perennial grass, switchgrass (Panicum virgatum L.), by comparing 10 to 30 year-old switchgrass stands with paired annual row crop fields across the central and eastern USA. We hypothesized that switchgrass would store more root C and SOC than neighboring shallow-rooted annual crops, and that these effects would extend deeper than 30 cm. We also evaluated whether switchgrass stimulates decomposition of SOC at depth using radiocarbon (14C) to quantify replacement of slow cycling isotopically depleted SOC. Finally, we explored whether the effect of switchgrass on SOC is moderated by soil chemical and physical properties. While the effect of switchgrass on SOC in the surface 100 cm was positive at most sites, the average effect was highly uncertain and statistically indistinguishable from zero (mean difference in SOC = 0.6 kg C m-2 [95% CI -0.8 to +1.9 kg C m-2]). By contrast, we found that root biomass C was consistently more deeply distributed and more abundant under switchgrass, yielding an estimated additional 0.6 kg C m-2 in the surface 100 cm of soil under switchgrass [95% CI +0.5 to +0.7 kg C m-2]. 14C measurements suggested that root C inputs were adding to existing SOC without stimulating decomposition. The effect of switchgrass on belowground C was not strongly related to any of the environmental factors that we evaluated. Our observations show that root biomass C can contribute substantially to belowground C stocks when deep-rooted perennial grasses replace shallow-rooted annual crops.

ecology↗

bioRxiv2024

Brain-like learning with exponentiated gradients

Computational neuroscience relies on gradient descent (GD) for training artificial neural network (ANN) models of the brain. The advantage of GD is that it is effective at learning difficult tasks. However, it produces ANNs that are a poor phenomenological fit to biology, making them less relevant as models of the brain. Specifically, it violates Dales law, by allowing synapses to change from excitatory to inhibitory, and leads to synaptic weights that are not log-normally distributed, contradicting experimental data. Here, starting from first principles of optimisation theory, we present an alternative learning algorithm, exponentiated gradient (EG), that respects Dales Law and produces log-normal weights, without losing the power of learning with gradients. We also show that in biologically relevant settings EG outperforms GD, including learning from sparsely relevant signals and dealing with synaptic pruning. Altogether, our results show that EG is a superior learning algorithm for modelling the brain with ANNs.

neuroscience↗

bioRxiv2024

The oneirogen hypothesis: modeling the hallucinatory effects of classical psychedelics in terms of replay-dependent plasticity mechanisms

Classical psychedelics induce complex visual hallucinations in humans, generating percepts that are coherent at a low level, but which have surreal, dream-like qualities at a high level. While there are many hypotheses as to how classical psychedelics could induce these effects, there are no concrete mechanistic models that capture the variety of observed effects in humans, while remaining consistent with the known pharmacological effects of classical psychedelics on neural circuits. In this work, we propose the "oneirogen hypothesis", which posits that the perceptual effects of classical psychedelics are a result of their pharmacological actions inducing neural activity states that truly are more similar to dream-like states. We simulate classical psychedelics effects via manipulating neural network models trained on perceptual tasks with the Wake-Sleep algorithm. This established machine learning algorithm leverages two activity phases: a perceptual phase (wake) where sensory inputs are encoded, and a generative phase (dream) where the network internally generates activity consistent with stimulus-evoked responses. We simulate the action of psychedelics by partially shifting the model to the Sleep state, which entails a greater influence of top-down connections, in line with the impact of psychedelics on apical dendrites. The effects resulting from this manipulation capture a number of experimentally observed phenomena including the emergence of hallucinations, increases in stimulus-conditioned variability, and large increases in synaptic plasticity. We further provide a number of testable predictions which could be used to validate or invalidate our oneirogen hypothesis.

neuroscience↗

bioRxiv2024

Stochastic Wiring of Cell Types Enhances Fitness by Generating Phenotypic Variability

The development of neural connectivity is a crucial biological process that gives rise to diverse brain circuits and behaviors. Neural development is a stochastic process, but this stochasticity is often treated as a nuisance to overcome rather than as a functional advantage. Here we use a computational model, in which connection probabilities between discrete cell types are genetically specified, to investigate the benefits of stochasticity in the development of neural wiring. We show that this model can be viewed as a generalization of a powerful class of artificial neural networks--Bayesian neural networks--where each network parameter is a sample from a distribution. Our results reveal that stochasticity confers a greater benefit in large networks and variable environments, which may explain its role in organisms with larger brains. Surprisingly, we find that the average fitness over a population of agents is higher than a single agent defined by the average connection probability. Our model reveals how developmental stochasticity, by inducing a form of non-heritable phenotypic variability, can increase the probability that at least some individuals will survive in rapidly changing, unpredictable environments. Our results suggest how stochasticity may be an important feature rather than a bug in neural development.

neuroscience↗

bioRxiv2023

The feature landscape of visual cortex

Understanding computations in the visual system requires a characterization of the distinct feature preferences of neurons in different visual cortical areas. However, we know little about how feature preferences of neurons within a given area relate to that areas role within the global organization of visual cortex. To address this, we recorded from thousands of neurons across six visual cortical areas in mouse and leveraged generative AI methods combined with closed-loop neuronal recordings to identify each neurons visual feature preference. First, we discovered that the mouses visual system is globally organized to encode features in a manner invariant to the types of image transformations induced by self-motion. Second, we found differences in the visual feature preferences of each area and that these differences generalized across animals. Finally, we observed that a given areas collection of preferred stimuli ( own-stimuli) drive neurons from the same area more effectively through their dynamic range compared to preferred stimuli from other areas ( other-stimuli). As a result, feature preferences of neurons within an area are organized to maximally encode differences among own-stimuli while remaining insensitive to differences among other-stimuli. These results reveal how visual areas work together to efficiently encode information about the external world.

neuroscience↗

bioRxiv2023

Learning better with Dale's Law: A Spectral Perspective

Most recurrent neural networks (RNNs) do not include a fundamental constraint of real neural circuits: Dales Law, which implies that neurons must be excitatory (E) or inhibitory (I). Dales Law is generally absent from RNNs because simply partitioning a standard networks units into E and I populations impairs learning. However, here we extend a recent feedforward bio-inspired EI network architecture, named Dales ANNs, to recurrent networks, and demonstrate that good performance is possible while respecting Dales Law. This begs the question: What makes some forms of EI network learn poorly and others learn well? And, why does the simple approach of incorporating Dales Law impair learning? Historically the answer was thought to be the sign constraints on EI network parameters, and this was a motivation behind Dales ANNs. However, here we show the spectral properties of the recurrent weight matrix at initialisation are more impactful on network performance than sign constraints. We find that simple EI partitioning results in a singular value distribution that is multimodal and dispersed, whereas standard RNNs have an unimodal, more clustered singular value distribution, as do recurrent Dales ANNs. We also show that the spectral properties and performance of partitioned EI networks are worse for small networks with fewer I units, and we present normalised SVD entropy as a measure of spectrum pathology that correlates with performance. Overall, this work sheds light on a long-standing mystery in neuroscience-inspired AI and computational neuroscience, paving the way for greater alignment between neural networks and biology.

neuroscience↗

bioRxiv2022

Can neurogenesis act as a neural regularizer?

New neurons are continuously generated in the subgranular zone of the dentate gyrus throughout adulthood. These new neurons gradually integrate into hippocampal circuits, forming new naive synapses. Viewed from this perspective, these new neurons may represent a significant source of wiring noise in hippocampal networks. In machine learning, such noise injection is commonly used as a regularization technique. Regularization techniques help prevent overfitting training data, and allow models to generalize learning to new, unseen data. Using a computational modeling approach, here we ask whether a neurogenesis-like process similarly acts as a regularizer, facilitating generalization in a category learning task. In a convolutional neural network (CNN) trained on the CIFAR-10 object recognition dataset, we modeled neurogenesis as a replacement/turnover mechanism, where weights for a randomly chosen small subset of neurons in a chosen hidden layer were re-initialized to new values as the model learned to categorize 10 different classes of objects. We found that neurogenesis enhanced generalization on unseen test data compared to networks with no neurogenesis. Moreover, neurogenic networks either outperformed or performed similarly to networks with conventional noise injection (i.e., dropout, weight decay, and neural noise). These results suggest that neurogenesis can enhance generalization in hippocampal learning through noise-injection, expanding on the roles that neurogenesis may have in cognition. Author SummaryIn deep neural networks, various forms of noise injection are used as regularization techniques to prevent overfitting and promote generalization on unseen test data. Here, we were interested in whether adult neurogenesis- the lifelong production of new neurons in the hippocampus- might similarly function as a regularizer in the brain. We explored this question computationally, assessing whether implementing a neurogenesis-like process in a hidden layer within a convolutional neural network trained in a category learning task would prevent overfitting and promote generalization. We found that neurogenesis regularization was as least as effective as, or more effective than, conventional regularizers (i.e., dropout, weight decay and neural noise) in improving model performance. These results suggest that optimal levels of hippocampal neurogenesis may improve memory-guided decision making by preventing overfitting, thereby promoting the formation of more generalized memories that can be applied in a broader range of circumstances. We outline how these predictions may be evaluated behaviorally in rodents with altered hippocampal neurogenesis.

neuroscience↗

bioRxiv2021

The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning

The visual system of mammals is comprised of parallel, hierarchical specialized pathways. Different pathways are specialized in so far as they use representations that are more suitable for supporting specific downstream behaviours. In particular, the clearest example is the specialization of the ventral ("what") and dorsal ("where") pathways of the visual cortex. These two pathways support behaviours related to visual recognition and movement, respectively. To-date, deep neural networks have mostly been used as models of the ventral, recognition pathway. However, it is unknown whether both pathways can be modelled with a single deep ANN. Here, we ask whether a single model with a single loss function can capture the properties of both the ventral and the dorsal pathways. We explore this question using data from mice, who like other mammals, have specialized pathways that appear to support recognition and movement behaviours. We show that when we train a deep neural network architecture with two parallel pathways using a self-supervised predictive loss function, we can outperform other models in fitting mouse visual cortex. Moreover, we can model both the dorsal and ventral pathways. These results demonstrate that a self-supervised predictive learning approach applied to parallel pathway architectures can account for some of the functional specialization seen in mammalian visual systems.

neuroscience↗

bioRxiv2020

Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well-established that it depends on pre and postsynaptic activity. However, models that rely solely on pre and postsynaptic activity for synaptic changes have, to date, not been able to account for learning complex tasks that demand credit assignment in hierarchical networks. Here, we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then neurons higher in a hierarchical circuit can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites, and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses, and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits.

neuroscience↗

bioRxiv2017

Constrained Instruments and their Application to Mendelian Randomization with Pleiotropy

In Mendelian randomization (MR), genetic variants are used to construct instrumental variables, which enable inference about the causal relationship between a phenotype of interest and a response or disease outcome. However, standard MR inference requires several assumptions, including the assumption that the genetic variants only influence the response through the phenotype of interest. Pleiotropy occurs when a genetic variant has an effect on more than one phenotype; therefore, a pleiotropic genetic variant may be an invalid instrumental variable. Hence, a naive method for constructing instrumental variables may lead to biased estimation of the causality between the phenotype and the response. Here, we present a set of intuitive methods (Constrained Instrumental Variable methods [CIV]) to construct valid instrumental variables and perform adjusted causal effect estimation when pleiotropy exists, focusing particularly on the situation where pleiotropic phenotypes have been measured. Our approach includes an automatic and valid selection of genetic variants when building the instrumental variables. We also provide details of the features of many existing methods, together with a comparison of their performance in a large series of simulations. CIV methods performed consistently better than many comparators across four different pleiotropic violations of the MR assumptions. We analyzed data from the Alzheimers Disease Neuroimaging Initiative (ADNI) Mueller et al. (2005) to disentangle causal relationships of several biomarkers with AD progression. The results showed that CIV methods can provide causal effect estimates, as well as selection of valid instruments while accounting for pleiotropy.

genetics↗

bioRxiv2017

Relevance learning via inhibitory plasticity and its implications for schizophrenia

Symptoms of schizophrenia may arise from a failure of cortical circuits to filter-out irrelevant inputs. Schizophrenia has also been linked to disruptions to cortical inhibitory interneurons, consistent with the possibility that in the normally functioning brain, these cells are in some part responsible for determining which inputs are relevant and which irrelevant. Here, we develop an abstract but biologically plausible neural network model that demonstrates how the cortex may learn to ignore irrelevant inputs through plasticity processes affecting inhibition. The model is based on the proposal that the amount of excitatory output from a cortical circuit encodes expected magnitude of reward or punishment (\"relevance\"), which can be trained using a temporal difference learning mechanism acting on feed-forward inputs to inhibitory interneurons. The model exhibits learned irrelevance and blocking, which become impaired following disruptions to inhibitory units. When excitatory units are connected to a competitive-learning output layer, the relevance code is capable of modulating learning and activity. Accordingly, the combined network is capable of recapitulating published experimental data linking inhibition in frontal cortex with fear learning and expression. Finally, the model demonstrates how relevance learning can take place in parallel with other types of learning, through plasticity rules involving inhibitory and excitatory components respectively. Altogether, this work offers a theory of how the cortex learns to selectively inhibit inputs, providing insight into how relevance-assignment problems may emerge in schizophrenia.

neuroscience↗