Biology subjects

Cornford, J.

Publications and source records attributed to Cornford, J..

5 recordsLinked to original sources

bioRxiv2026

Learning using switching synaptic plasticity rules

Synapses differ in molecular and ultrastructural state, but the computational role of this heterogeneity remains unclear and computational models do not account for it. Local Hebbian-like plasticity is well supported experimentally, whereas learning complex behaviors most likely requires non-local credit-assignment signals. Recent cortical electron microscopy data suggest that strong and weak synapses occupy distinct structural states, with large synapses often containing a spine apparatus that can alter calcium dynamics and plasticity. Here, we assigned different computational roles to different morphological synaptic types: we asked how networks with state-dependent plasticity operate and whether the mechanisms and structural features they develop are consistent with biological observations. We built a plasticity-switching recurrent neural network (psRNN) in which synaptic state is modeled by recurrent-weight thresholds. Synapses in the low-weight state use a local Hebbian-like rule, whereas synapses in the high-weight state receive an idealized non-local credit-assignment signal implemented with backpropagation through time. psRNNs learned cognitive tasks with temporal and working-memory demands in fewer trials than fully backpropagation-trained RNNs, despite routing BP-based updates to fewer recurrent weights. The advantage arose from three interacting mechanisms: 1) Hebbian-like updates exposed the credit-assignment component to several nearby parameter configurations, 2) the network dynamically built a task-relevant recurrent initialization, and 3) synapses switched into the credit-assignment subset before Hebbian growth destabilized learning. Structurally, trained psRNNs produced more anti-symmetric weights, lower effective rank, and stronger feedforward structure than control RNNs. Our results shed light on the computational role of heterogeneous synapses, which may regulate distinct types of learning signals, and we generate falsifiable predictions for connectomic analyses of recurrent cortical circuits.

neuroscience↗

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↗

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↗

bioRxiv2020

Learning to live with Dale's principle: ANNs with separate excitatory and inhibitory units

AO_SCPLOWBSTRACTC_SCPLOWThe units in artificial neural networks (ANNs) can be thought of as abstractions of biological neurons, and ANNs are increasingly used in neuroscience research. However, there are many important differences between ANN units and real neurons. One of the most notable is the absence of Dales principle, which ensures that biological neurons are either exclusively excitatory or inhibitory. Dales principle is typically left out of ANNs because its inclusion impairs learning. This is problematic, because one of the great advantages of ANNs for neuroscience research is their ability to learn complicated, realistic tasks. Here, by taking inspiration from feedforward inhibitory interneurons in the brain we show that we can develop ANNs with separate populations of excitatory and inhibitory units that learn just as well as standard ANNs. We call these networks Dales ANNs (DANNs). We present two insights that enable DANNs to learn well: (1) DANNs are related to normalization schemes, and can be initialized such that the inhibition centres and standardizes the excitatory activity, (2) updates to inhibitory neuron parameters should be scaled using corrections based on the Fisher Information matrix. These results demonstrate how ANNs that respect Dales principle can be built without sacrificing learning performance, which is important for future work using ANNs as models of the brain. The results may also have interesting implications for how inhibitory plasticity in the real brain operates.

neuroscience↗

bioRxiv2018

CRISPRa-mediated Kcna1 upregulation decreases neuronal excitability and suppresses seizures in a rodent model of temporal lobe epilepsy

Epilepsy is a major health burden, calling for new mechanistic and therapeutic insights. CRISPR-mediated gene editing shows promise to cure genetic pathologies, although hitherto it has mostly been applied ex-vivo. Its translational potential for treating non-genetic pathologies is still unexplored. Furthermore, neurological diseases represent an important challenge for the application of CRISPR, because of the need in many cases to manipulate gene function of neurons in situ. A variant of CRISPR, CRISPRa, offers the possibility to modulate the expression of endogenous genes by directly targeting their promoters. We asked if this strategy can effectively treat acquired focal epilepsy, focusing on ion channels because their manipulation is known be effective in changing network hyperactivity and hypersynchronisation. We applied a doxycycline-inducible CRISPRa technology to increase the expression of the potassium channel gene Kcna1 (encoding Kv1.1) in mouse hippocampal excitatory neurons. CRISPRa-mediated Kv1.1 upregulation led to a substantial decrease in neuronal excitability. Continuous video-EEG telemetry showed that AAV9-mediated delivery of CRISPRa, upon doxycycline administration, decreased spontaneous generalized tonic-clonic seizures in a model of temporal lobe epilepsy, and rescued cognitive impairment and transcriptomic alterations associated with chronic epilepsy. The focal treatment minimizes concerns about off-target effects in other organs and brain areas. This study provides the proof of principle for a translational CRISPR-based approach to treat neurological diseases characterized by abnormal circuit excitability.

neuroscience↗