Topic: Dale’s law

We study randomly connected neural networks to understand the effects of network architectures on network dynamics. Specifically, we examine the impact of sparse connectivity combined with Dale’s law on network stability by analysing the eigenspectrum of the networked Jacobian. Previous work has examined the effects of Dale’s law for fully connected random matrices that are balanced (zero mean) [3] or unbalanced [1] and sparse matrices with constant weights for each population [2]. Our work significantly extends these results by integrating both Dale’s law and sparsity into both balanced and u

Network models are often designed to capture selective aspects of cortical circuits. On one end, mechanistic models such as balanced spiking networks resemble activity regimes observed in data, but are often limited to simple computations. On the other end, functional models like trained deep networks can show comparable performance and dynamical motifs, but are far removed from experimental physiology. Here, we put forth a new framework for excitatory-inhibitory spiking networks which retains key properties of both mechanistic and functional models. Based on previous studies of the geometry o

Relating connectivity features to the function of a recurrent neuronal network is a longstanding problem that still remains at large. To this end we describe the combinatorics of equilibria and steady states of neurons in recurrent networks that satisfy the Dale’s law. It turns out that the combinatorial code of a Dale network can be characterized in terms of two properties: (i) the network connectivity graph, and (ii) a spectral condition on the synaptic matrix. We find that in the weak coupling regime the combinatorial code depends only on the connectivity graph, and not on the particulars o

Neural activity in many sensory systems is organized on low-dimensional manifolds by means of convex receptive fields. Neural codes in these areas are constrained by this organization, as not every neural code is compatible with convex receptive fields. The same codes are also constrained by the structure of the underlying neural network. In my talk I will attempt to provide answers to the following natural questions: (i) How do recurrent circuits generate codes that are compatible with the convexity of receptive fields? (ii) How can we utilize the constraints imposed by the convex receptive

We performed extra-cellular recordings simultaneously from the whisker primary sensory cortex (wS1) and the medial prefrontal cortex (mPFC) of mice performing a tactile detection task and then fit a recurrent spiking neural network (RSNN) to the recorded activity. We assume that neural dynamics are defined by standard conductance-based spiking neurons with adaptation. We optimize the entire connectivity matrix (within and across areas) using back-propagation through time (BPTT) for spiking neural networks [1, 2] and synaptic rewiring [3] while respecting Dale’s law. After optimization, the res

The hippocampus plays a central role in memory processing. In order to avoid deleterious interference between stored and ongoing experience, theoretical considerations require a separation between phases of memory encoding and recall within the same neural circuit, putatively controlled by hippocampal theta oscillations [1]. However, the neural mechanisms subserving this separation remain unknown. Classical models either remain moot about these mechanisms [2], or assume purpose-built neuromodulatory interactions that are in conflict with biologically realistic timescales of synaptic modulation

Computational neuroscience increasingly leverages gradient descent (GD) for training artificial neural network (ANN) models of the brain. GD’s advantage is that it is very effective at learning difficult tasks. This is important for the role of computational models as the interface between theory and empirical data (Levenstein et al., 2023), since models that can learn realistic tasks from realistic, high-dimensional inputs can be more directly compared to experimental data (Doerig et al., 2023). However, GD produces ANNs that are poor phenomenological fits to biology, making them less relevan

Recurrent neural networks (RNNs) have increasingly been employed to model cortical function, but conventional architectures lack biological realism. Notably, they often ignore two key biological principles: i) Dale’s Law, which constrains neurons to either be strictly excitatory or inhibitory, and ii) Sparse, structured connectivity motifs observed in the brain. Incorporating these constraints however poses challenges, as they often affect model performance negatively. Our work presents a novel approach that integrates Dale’s backpropagation, a sign-constrained training method, with topologica

Correlative Hebbian learning rules are broadly supported by experimental evidence, but do not prescribe a mechanism to coordinate plasticity across multiple circuit layers during target-directed learning. Recent experimental and theoretical work suggests that top-down input to the apical dendrites of pyramidal cells may provide a solution to this deep credit assignment problem. Experiments have shown that dendritic-spike-dependent plasticity is promoted by top-down excitatory inputs carrying information about learning targets, and it is suppressed by lateral feedback inhibition. However, exist

The brain’s limited information processing capacity leads to competition among concurrent inputs at the neuronal level. Selective attention is a cognitive process that resolves this competition by filtering out irrelevant sensory information while enhancing the neural representation of relevant stimuli. This phenomenon is well-explained by the theory of biased competition, which posits that attention biases neural processing towards behaviorally relevant stimuli when multiple objects are present. However, the mechanism of attention remains under debate. We show how biased competition can be so

Deep feedforward and recurrent networks have become popular choices for models of neural computation, but they do not provide a clear link to the details of the underlying biophysical processes. Here we argue that the reason this link has been difficult to establish is because biological circuits operate in a fundamentally different way. To illustrate this, we present a new theory of neural computation in excitatory-inhibitory spiking networks, which captures the precise computational role of each neuron and every spike. By assuming low-rank recurrent connectivity, we show that spiking populat