Back-Propagation of Physiological Action Potential Output in Dendrites of Slender-Tufted L5A Pyramidal Neurons

Pyramidal neurons of layer 5A are a major neocortical output type and clearly distinguished from layer 5B pyramidal neurons with respect to morphology, in vivo firing patterns, and connectivity; yet knowledge of their dendritic properties is scant. We used a combination of whole-cell recordings and Ca2+ imaging techniques in vitro to explore the specific dendritic signaling role of physiological action potential patterns recorded in vivo in layer 5A pyramidal neurons of the whisker-related ‘barrel cortex’. Our data provide evidence that the temporal structure of physiological action potential patterns is crucial for an effective invasion of the main apical dendrites up to the major branch point. Both the critical frequency enabling action potential trains to invade efficiently and the dendritic calcium profile changed during postnatal development. In contrast to the main apical dendrite, the more passive properties of the short basal and apical tuft dendrites prevented an efficient back-propagation. Various Ca2+ channel types contributed to the enhanced calcium signals during high-frequency firing activity, whereas A-type K+ and BKCa channels strongly suppressed it. Our data support models in which the interaction of synaptic input with action potential output is a function of the timing, rate and pattern of action potentials, and dendritic location

On the reversed bias-variance tradeoff in deep ensembles

Deep ensembles aggregate predictions of diverse neural networks to improve generalisation and quantify uncertainty. Here, we investigate their behavior when increasing the ensemble mem- bers’ parameter size - a practice typically asso- ciated with better performance for single mod- els. We show that under practical assumptions in the overparametrized regime far into the dou- ble descent curve, not only the ensemble test loss degrades, but common out-of-distribution detec- tion and calibration metrics suffer as well. Rem- iniscent to deep double descent, we observe this phenomenon not only when increasing the single member’s capacity but also as we increase the training budget, suggesting deep ensembles can benefit from early stopping. This sheds light on the success and failure modes of deep ensembles and suggests that averaging finite width models perform better than the neural tangent kernel limit for these metrics

Bio-inspired, task-free continual learning through activity regularization

The ability to sequentially learn multiple tasks without forgetting is a key skill of biological brains, whereas it represents a major challenge to the field of deep learning. To avoid catastrophic forgetting, various continual learning (CL) approaches have been devised. However, these usually require discrete task boundaries. This requirement seems biologically implausible and often limits the application of CL methods in the real world where tasks are not always well defined. Here, we take inspiration from neuroscience, where sparse, non-overlapping neuronal representations have been suggested to prevent catastrophic forgetting. As in the brain, we argue that these sparse representations should be chosen on the basis of feed forward (stimulus-specific) as well as top-down (context-specific) information. To implement such selective sparsity, we use a bio-plausible form of hierarchical credit assignment known as Deep Feedback Control (DFC) and combine it with a winner-take-all sparsity mechanism. In addition to sparsity, we introduce lateral recurrent connections within each layer to further protect previously learned representations. We evaluate the new sparse-recurrent version of DFC on the split-MNIST computer vision benchmark and show that only the combination of sparsity and intra-layer recurrent connections improves CL performance with respect to standard backpropagation. Our method achieves similar performance to well-known CL methods, such as Elastic Weight Consolidation and Synaptic Intelligence, without requiring information about task boundaries. Overall, we showcase the idea of adopting computational principles from the brain to derive new, task-free learning algorithms for CL

Fast two-layer two-photon imaging of neuronal cell populations using an electrically tunable lens

Functional two-photon Ca2+-imaging is a versatile tool to study the dynamics of neuronal populations in brain slices and living animals. However, population imaging is typically restricted to a single two-dimensional image plane. By introducing an electrically tunable lens into the excitation path of a two-photon microscope we were able to realize fast axial focus shifts within 15 ms. The maximum axial scan range was 0.7 mm employing a 40x NA0.8 water immersion objective, plenty for typically required ranges of 0.2–0.3 mm. By combining the axial scanning method with 2D acousto-optic frame scanning and random-access scanning, we measured neuronal population activity of about 40 neurons across two imaging planes separated by 40 μm and achieved scan rates up to 20–30 Hz. The method presented is easily applicable and allows upgrading of existing two-photon microscopes for fast 3D scanning

Learning cortical hierarchies with temporal Hebbian updates

A key driver of mammalian intelligence is the ability to represent incoming sensory information across multiple abstraction levels. For example, in the visual ventral stream, incoming signals are first represented as low-level edge filters and then transformed into high-level object representations. Similar hierarchical structures routinely emerge in artificial neural networks (ANNs) trained for object recognition tasks, suggesting that similar structures may underlie biological neural networks. However, the classical ANN training algorithm, backpropagation, is considered biologically implausible, and thus alternative biologically plausible training methods have been developed such as Equilibrium Propagation, Deep Feedback Control, Supervised Predictive Coding, and Dendritic Error Backpropagation. Several of those models propose that local errors are calculated for each neuron by comparing apical and somatic activities. Notwithstanding, from a neuroscience perspective, it is not clear how a neuron could compare compartmental signals. Here, we propose a solution to this problem in that we let the apical feedback signal change the postsynaptic firing rate and combine this with a differential Hebbian update, a rate-based version of classical spiking time-dependent plasticity (STDP). We prove that weight updates of this form minimize two alternative loss functions that we prove to be equivalent to the error-based losses used in machine learning: the inference latency and the amount of top-down feedback necessary. Moreover, we show that the use of differential Hebbian updates works similarly well in other feedback-based deep learning frameworks such as Predictive Coding or Equilibrium Propagation. Finally, our work removes a key requirement of biologically plausible models for deep learning and proposes a learning mechanism that would explain how temporal Hebbian learning rules can implement supervised hierarchical learning

Continual Learning in Recurrent Neural Networks with Hypernetworks

The last decade has seen a surge of interest in continual learning (CL), and a variety of methods have been developed to alleviate catastrophic forgetting. However, most prior work has focused on tasks with static data, while CL on sequential data has remained largely unexplored. Here we address this gap in two ways. First, we evaluate the performance of established CL methods when applied to recurrent neural networks (RNNs). We primarily focus on elastic weight consolidation, which is limited by a stability-plasticity trade-off, and explore the particularities of this trade-off when using sequential data. We show that high working memory requirements, but not necessarily sequence length, lead to an increased need for stability at the cost of decreased performance on subsequent tasks. Second, to overcome this limitation we employ a recent method based on hypernetworks and apply it to RNNs to address catastrophic forgetting on sequential data. By generating the weights of a main RNN in a task-dependent manner, our approach disentangles stability and plasticity, and outperforms alternative methods in a range of experiments. Overall, our work provides several key insights on the differences between CL in feedforward networks and in RNNs, while offering a novel solution to effectively tackle CL on sequential data.Comment: 13 pages and 4 figures in the main text; 20 pages and 2 figures in the supplementary material

Efficient Biologically Plausible Adversarial Training

Artificial Neural Networks (ANNs) trained with Backpropagation (BP) show astounding performance and are increasingly often used in performing our daily life tasks. However, ANNs are highly vulnerable to adversarial attacks, which alter inputs with small targeted perturbations that drastically disrupt the models' performance. The most effective method to make ANNs robust against these attacks is adversarial training, in which the training dataset is augmented with exemplary adversarial samples. Unfortunately, this approach has the drawback of increased training complexity since generating adversarial samples is very computationally demanding. In contrast to ANNs, humans are not susceptible to adversarial attacks. Therefore, in this work, we investigate whether biologically-plausible learning algorithms are more robust against adversarial attacks than BP. In particular, we present an extensive comparative analysis of the adversarial robustness of BP and Present the Error to Perturb the Input To modulate Activity (PEPITA), a recently proposed biologically-plausible learning algorithm, on various computer vision tasks. We observe that PEPITA has higher intrinsic adversarial robustness and, with adversarial training, has a more favourable natural-vs-adversarial performance trade-off as, for the same natural accuracies, PEPITA's adversarial accuracies decrease in average by 0.26% and BP's by 8.05%