Biology subjects

Goodman, D. F. M.

Publications and source records attributed to Goodman, D. F. M..

13 recordsLinked to original sources

bioRxiv2026

Factorization and spatial encodings: a hypothesis about the foundations of the genomic code

The genomic mechanisms that efficiently encode the initial architecture and synaptic connectivity of neural circuits remain poorly understood. We hypothesise that two primary mechanisms -- spatial encoding and factorisation -- enable a limited genome to initialise networks of billions of neurons. Spatial encoding, a form of indirect representation, compresses neural network parameters while enforcing structural continuity. Complementarily, we introduce a factorisation mechanism inspired by reaction-diffusion models, comprising a spatially invariant reaction rule and spatially variant diffusion dynamics. Based on this insight, we can efficiently abstract a neural network layer as a spatially invariant weight kernel (or filter) and its spatially variant transformations along the spatial dimensions. Thus, coupling this variant-invariant decomposition with spatial encodings can substantially reduce the size of the solution space explored by the genome. In addition, we show that this coupling leads to efficient initialisation of cortical maps, such as V1 orientation maps, and neural networks with the ability to generalise. In summary, coupling factorisation and spatial encodings can offer functional advantages to the evolving genome.

neuroscience↗

bioRxiv2025

Neuromodulation enhances dynamic sensory processing in spiking neural network models

Spiking neurons underlie the brains extreme energy efficiency, and therefore have great potential in neuromorphic computing, although realising this efficiency in practice has proven challenging. We use neuromodulation, a biological mechanism that lets the network dynamically and contextually modify its own parameters. We find it substantially increases performance across a range of sensory processing tasks, including a challenging new speech-in-noise dataset we introduce, with very few additional resources (neurons, energy, parameters). Neuromodulatory networks are space and energy efficient thanks to two mechanisms that are directly relevant to neuromorphic computing and biology: firstly, they allow for an order-of-magnitude reduction in the number of neurons required; and secondly, they enable very sparse firing, achieving better results using orders of magnitude fewer spikes. Together, these properties may throw light on the computational role of neuromodulation in biology, and make neuromodulation an ideal mechanism to improve performance and efficiency for neuromorphic devices.

neuroscience↗

bioRxiv2025

Partial recurrence enables robust and efficient computation

AO_SCPLOWBSTRACTC_SCPLOWNeural circuits are sparse and bidirectional. Meaning that signals flow from early sensory areas to later regions and back. Yet, between connected areas there exist some but not all pathways. How does this structure, somewhere between feedforward and fully recurrent, shape circuit function? To address this question, we designed a recurrent neural network model in which a set of weight matrices (i.e. pathways) can be combined to generate every network structure between feedforward and fully recurrent. We term these architectures partially recurrent neural networks (pRNNs). We trained over 25,000 pRNNs on a novel set of reinforcement learning tasks, designed to mimic multisensory navigation, and compared their performance across multiple functional metrics. Our findings reveal three key insights. First, many architectures match or exceed the performance of fully recurrent networks, despite using as few as one-quarter the number of parameters; demonstrating that partial recurrence enables energy efficient, yet performant solutions. Second, each pathways functional impact is both task and circuit dependent. For instance, feedback connections enhance robustness to noise in some, but not all contexts. Third, different pRNN architectures learn solutions with distinct input sensitivities and memory dynamics, and these computational traits help to explain their functional capabilities. Overall, our results demonstrate that partial recurrence enables robust and efficient computation - a finding that helps to explain why neural circuits are sparse and bidirectional, and how these principles could inform the design of artificial systems. 1 Key PointsO_LIWe define a neural network model in which a set of weight matrices (i.e. pathways) can be combined to generate every network structure between feedforward and fully recurrent. C_LIO_LIMany of these partially recurrent (pRNN) architectures perform as well as, or even better than, fully recurrent networks, in terms of their task performance, sample efficiency and robustness to various perturbations. C_LIO_LIEach pathways functional impact is both task and circuit dependent. For example, input to output skip connections boost learning speed in some, but not all tasks or architectures. C_LIO_LIWe propose a conceptual model, whereby: networks with different structures learn solutions with distinct computational traits, which shape their function. C_LI

neuroscience↗

bioRxiv2024

Fusing multisensory signals across channels and time

Animals continuously combine information across sensory modalities and time, and use these combined signals to guide their behaviour. Picture a predator watching their prey sprint and screech through a field. To date, a range of multisensory algorithms have been proposed to model this process including linear and nonlinear fusion, which combine the inputs from multiple sensory channels via either a sum or nonlinear function. However, many multisensory algorithms treat successive observations independently, and so cannot leverage the temporal structure inherent to naturalistic stimuli. To investigate this, we introduce a novel multisensory task in which we provide the same number of task-relevant signals per trial but vary how this information is presented: from many short bursts to a few long sequences. We demonstrate that multisensory algorithms that treat different time steps as independent, perform sub-optimally on this task. However, simply augmenting these algorithms to integrate across sensory channels and short temporal windows allows them to perform surprisingly well, and comparably to fully recurrent neural networks. Overall, our work: highlights the benefits of fusing multisensory information across channels and time, shows that small increases in circuit/model complexity can lead to significant gains in performance, and provides a novel multisensory task for testing the relevance of this in biological systems. 1 Author summaryWe constantly detect sensory inputs, like sights and sounds, and use combinations of these signals to guide our actions. For example, by reading someones lips we can better converse with them in a noisy environment. Several mathematical models have been proposed to describe this process. However, these models are "blind" to time. That is, following the example above, if we took the audio and visual signals from our friend and mixed them up over time; current models would not notice any difference, but we would find the result incomprehensible. Motivated by this, we introduce a new set of models which describe how animals could fuse sensory signals across time. Surprisingly, we find that combining signals across senses and short periods of time, works as well as a more complex model. 2 Key PointsO_LIWe introduce a novel multisensory task in which we provide task relevant evidence via bursts of varying duration, amidst a noisy background. C_LIO_LIPrior multisensory algorithms perform sub-optimally on this task, as they cannot leverage temporal structure. C_LIO_LIHowever, they can perform better by integrating across sensory channels and short temporal windows. C_LIO_LISurprisingly, this allows for comparable performance to fully recurrent neural networks, while using less than one tenth the number of parameters. C_LI

neuroscience↗

bioRxiv2024

Spiking neural network models of sound localisation via a massively collaborative process

AbstractNeuroscientists are increasingly initiating large-scale collaborations which bring together tens to hundreds of researchers. However, while these projects represent a step-change in scale, they retain a traditional structure with centralised funding, participating laboratories and data sharing on publication. Inspired by an open-source project in pure mathematics, we set out to test the feasibility of an alternative structure by running a grassroots, massively collaborative project in computational neuroscience. To do so, we launched a public Git repository, with code for training spiking neural networks to solve a sound localisation task via surrogate gradient descent. We then invited anyone, anywhere to use this code as a springboard for exploring questions of interest to them, and encouraged participants to share their work both asynchro-nously through Git and synchronously at monthly online workshops. At a scientific level, our work investigated how a range of biologically-relevant parameters, from time delays to mem-brane time constants and levels of inhibition, could impact sound localisation in networks of spiking units. At a more macro-level, our project brought together 31 researchers from multiple countries, provided hands-on research experience to early career participants, and opportunities for supervision and teaching to later career participants. Looking ahead, our project provides a glimpse of what open, collaborative science could look like and provides a necessary, tentative step towards it.

neuroscience↗

bioRxiv2023

Multimodal units fuse-then-accumulate evidence across channels

Animals continuously detect information via multiple sensory channels, like vision and hearing, and integrate these signals to realise faster and more accurate decisions; a fundamental neural computation known as multisensory integration. A widespread view of this process is that multimodal neurons linearly fuse information across sensory channels. However, does linear fusion generalise beyond the classical tasks used to explore multisensory integration? Here, we develop novel multisensory tasks, which focus on the underlying statistical relationships between channels, and deploy models at three levels of abstraction: from probabilistic ideal observers to artificial and spiking neural networks. Using these models, we demonstrate that when information provided by different channels is not independent, linear fusion performs sub-optimally and even fails in extreme cases. This leads us to propose a simple nonlinear algorithm for multisensory integration which is compatible with our current knowledge of multimodal circuits, excels in naturalistic settings and is optimal for a wide class of multisensory tasks. Thus, our work emphasises the role of nonlinear fusion in multisensory integration, and provides testable hypotheses for the field to explore at multiple levels: from single neurons to behaviour. Key PointsO_LIWe introduce a novel set of tasks, based on comodulating the signals from multiple channels. C_LIO_LIWe show that linear fusion performs sub-optimally on these tasks and even fails in extreme cases. C_LIO_LIIn contrast, nonlinear fusion is optimal and excels in naturalistic settings, like predator-prey interactions. C_LIO_LIWe demonstrate that networks of spiking neurons approximate the behaviour of this nonlinear algorithm, when trained on these tasks. C_LIO_LIFinally, we explore how single neuron properties allow networks to approximate nonlinear fusion. C_LI

neuroscience↗

bioRxiv2021

The Psychoacoustics of Automatic Speech Recognition

Deep neural networks have had considerable success in neuroscience as models of the visual system, and recent work has suggested this may also extend to the auditory system. We tested the behaviour of a range of state of the art deep learning-based automatic speech recognition systems on a wide collection of manipulated sounds used in standard human psychometric experiments. While some systems showed qualitative agreement with humans in certain tests, in others all tested systems diverged markedly from humans. In particular, all systems used spectral invariance, temporal fine structure and speech periodicity differently from humans. We conclude that despite some promising results, none of the tested automatic speech recognition systems can yet act as a strong proxy for human speech recognition. However, we note that the more recent systems with better performance also tend to better match human results, suggesting that continued cross-fertilisation of ideas between human and automatic speech recognition may be fruitful. Our open source toolbox allows researchers to assess future automatic speech recognition systems or add additional psychoacoustic measures.

neuroscience↗

bioRxiv2020

Neural heterogeneity promotes robust learning

The brain has a hugely diverse, heterogeneous structure. Whether or not heterogeneity at the neural level plays a functional role remains unclear, and has been relatively little explored in models which are often highly homogeneous. We compared the performance of spiking neural networks trained to carry out tasks of real-world difficulty, with varying degrees of heterogeneity, and found that it substantially improved task performance. Learning was more stable and robust, particularly for tasks with a rich temporal structure. In addition, the distribution of neuronal parameters in the trained networks closely matches those observed experimentally. We suggest that the heterogeneity observed in the brain may be more than just the byproduct of noisy processes, but rather may serve an active and important role in allowing animals to learn in changing environments. SummaryNeural heterogeneity is metabolically efficient for learning, and optimal parameter distribution matches experimental data.

neuroscience↗

bioRxiv2019

Canonical brain computations account for perceived sound source location

Sound localization in reverberant environments is a difficult task that human listeners perform effortlessly. Many neural mechanisms have been proposed to account for this behavior. Generally they rely on emphasizing localization information at the onset of the incoming sound while discarding localization cues that arrive later. We modelled several of these mechanisms using neural circuits commonly found in the brain and tested their performance in the context of experiments showing that, in the dominant frequency region for sound localisation, we have a preference for auditory cues arriving during the rising slope of the sound energy (Dietz et al., 2013). We found that both single cell mechanisms (onset and adaptation) and population mechanisms (lateral inhibition) were easily able to reproduce the results across a very wide range of parameter settings. This suggests that sound localization in reverberant environments may not require specialised mechanisms specific to perform that task, but could instead rely on common neural circuits in the brain. This would allow for the possibility of individual differences in learnt strategies or neuronal parameters. This research is fully reproducible, and we made our code available to edit and run online via interactive live notebooks.

neuroscience↗

bioRxiv2018

Brian2GeNN: a system for accelerating alarge variety of spiking neural networkswith graphics hardware

\"Brian\" is a popular Python-based simulator for spiking neural networks, commonly used in computational neuroscience. GeNN is a C++-based meta-compiler for accelerating spiking neural network simulations using consumer or high performance grade graphics processing units (GPUs). Here we introduce a new software package, Brian2GeNN, that connects the two systems so that users can make use of GeNN GPU acceleration when developing their models in Brian, without requiring any technical knowledge about GPUs, C++ or GeNN. The new Brian2GeNN software uses a pipeline of code generation to translate Brian scripts into C++ code that can be used as input to GeNN, and subsequently can be run on suitable NVIDIA GPU accelerators. From the users perspective, the entire pipeline is invoked by adding two simple lines to their Brian scripts. We have shown that using Brian2GeNN, typical models can run tens to hundreds of times faster than on CPU.

neuroscience↗

bioRxiv2017

Effects of gamification and active listening on short-term sound localization training in virtual reality

Head-related transfer functions (HRTFs) capture the direction-dependant way that sound interacts with the head and torso. In virtual audio systems, which aim to emulate these effects, non-individualized, generic HRTFs are typically used leading to an inaccurate perception of virtual sound location. Training has the potential to exploit the brains ability to adapt to these unfamiliar cues. In this study, three virtual sound localization training paradigms were evaluated; one provided simple visual positional confirmation of sound source location, a second introduced game design elements ("gamification") and a final version additionally utilized head-tracking to provide listeners with experience of relative sound source motion ("active listening"). The results demonstrate a significant effect of training after a small number of short (12-minute) training sessions, which is retained across multiple days. Gamification alone had no significant effect on the efficacy of the training, but active listening resulted in a significantly greater improvements in localization accuracy. In general, improvements in virtual sound localization following training generalized to a second set of non-individualized HRTFs, although some HRTF-specific changes were observed in polar angle judgement for the active listening group. The implications of this on the putative mechanisms of the adaptation process are discussed.

animal behavior and cognition↗

bioRxiv2017

Modeling neuron-glia interactions with the Brian 2 simulator

Despite compelling evidence that glial cells could crucially regulate neural network activity, the vast majority of available neural simulators ignores the possible contribution of glia to neuronal physiology. Here, we show how to model glial physiology and neuron-glia interactions in the Brian 2 simulator. Brian 2 offers facilities to explicitly describe any model in mathematical terms with limited and simple simulator-specific syntax, automatically generating high-performance code from the user-provided descriptions. The flexibility of this approach allows us to model not only networks of neurons, but also individual glial cells, electrical coupling of glial cells, and the interaction between glial cells and synapses. We therefore conclude that Brian 2 provides an ideal platform to efficiently simulate glial physiology, and specifically, the influence of astrocytes on neural activity.

neuroscience↗

bioRxiv2017

Changes In Firing Regularity In The Normal And Impaired Auditory System

The auditory system processes temporal information at multiple scales, and disruptions to this temporal processing may lead to deficits in auditory tasks such as detecting and discriminating sounds in a noisy environment. Here, a modelling approach is used to study the temporal regularity of firing by chopper cells in the ventral cochlear nucleus, in both the normal and impaired auditory system. Chopper cells, which have a strikingly regular firing response, divide into two classes, sustained and transient, based on the time course of this regularity. Several hypotheses have been proposed to explain the behaviour of chopper cells, and the difference between sustained and transient cells in particular. However, there is no conclusive evidence so far. Here, a reduced mathematical model is developed and used to compare and test a wide range of hypotheses with a limited number of parameters. Simulation results show a continuum of cell types and behaviours: chopper-like behaviour arises for a wide range of parameters, suggesting that multiple mechanisms may underlie this behaviour. The model accounts for systematic trends in regularity as a function of stimulus level that have previously only been reported anecdotally. Finally, the model is used to predict the effects of a reduction in the number of auditory nerve fibres (deafferentation due to, for example, cochlear synaptopathy). An interactive version of this paper in which all the model parameters can be changed is available online.\n\nHighlightsO_LIA low parameter model reproduces chopper cell firing regularity\nC_LIO_LIMultiple factors can account for sustained vs transient chopper cell response\nC_LIO_LIThe model explains stimulus level dependence of firing regularity\nC_LIO_LIThe model predicts chopper cells fire more irregularly after deafferentation\nC_LIO_LIAn interactive version of the paper allows readers to change parameters\nC_LI

neuroscience↗