What is the functional role of adult neurogenesis in the hippocampus?

The dentate gyrus is part of the hippocampal memory system and special in that it generates new neurons throughout life. Here we discuss the question of what the functional role of these new neurons might be. Our hypothesis is that they help the dentate gyrus to avoid the problem of catastrophic interference when adapting to new environments. We assume that old neurons are rather stable and preserve an optimal encoding learned for known environments while new neurons are plastic to adapt to those features that are qualitatively new in a new environment. A simple network simulation demonstrates that adding new plastic neurons is indeed a successful strategy for adaptation without catastrophic interference

Efficient ConvNets for Analog Arrays

Analog arrays are a promising upcoming hardware technology with the potential to drastically speed up deep learning. Their main advantage is that they compute matrix-vector products in constant time, irrespective of the size of the matrix. However, early convolution layers in ConvNets map very unfavorably onto analog arrays, because kernel matrices are typically small and the constant time operation needs to be sequentially iterated a large number of times, reducing the speed up advantage for ConvNets. Here, we propose to replicate the kernel matrix of a convolution layer on distinct analog arrays, and randomly divide parts of the compute among them, so that multiple kernel matrices are trained in parallel. With this modification, analog arrays execute ConvNets with an acceleration factor that is proportional to the number of kernel matrices used per layer (here tested 16-128). Despite having more free parameters, we show analytically and in numerical experiments that this convolution architecture is self-regularizing and implicitly learns similar filters across arrays. We also report superior performance on a number of datasets and increased robustness to adversarial attacks. Our investigation suggests to revise the notion that mixed analog-digital hardware is not suitable for ConvNets

Training large-scale ANNs on simulated resistive crossbar arrays

Accelerating training of artificial neural networks (ANN) with analog resistive crossbar arrays is a promising idea. While the concept has been verified on very small ANNs and toy data sets (such as MNIST), more realistically sized ANNs and datasets have not yet been tackled. However, it is to be expected that device materials and hardware design constraints, such as noisy computations, finite number of resistive states of the device materials, saturating weight and activation ranges, and limited precision of analog-to-digital converters, will cause significant challenges to the successful training of state-of-the-art ANNs. By using analog hardware aware ANN training simulations, we here explore a number of simple algorithmic compensatory measures to cope with analog noise and limited weight and output ranges and resolutions, that dramatically improve the simulated training performances on RPU arrays on intermediately to large-scale ANNs

Nonlinear multiplicative dendritic integration in neuron and network models

Neurons receive inputs from thousands of synapses distributed across dendritic trees of complex morphology. It is known that dendritic integration of excitatory and inhibitory synapses can be highly non-linear in reality and can heavily depend on the exact location and spatial arrangement of inhibitory and excitatory synapses on the dendrite. Despite this known fact, most neuron models used in artificial neural networks today still only describe the voltage potential of a single somatic compartment and assume a simple linear summation of all individual synaptic inputs. We here suggest a new biophysical motivated derivation of a single compartment model that integrates the non-linear effects of shunting inhibition, where an inhibitory input on the route of an excitatory input to the soma cancels or “shunts” the excitatory potential. In particular, our integration of non-linear dendritic processing into the neuron model follows a simple multiplicative rule, suggested recently by experiments, and allows for strict mathematical treatment of network effects. Using our new formulation, we further devised a spiking network model where inhibitory neurons act as global shunting gates, and show that the network exhibits persistent activity in a low firing regime

A kernel method for the two-sample-problem

We propose two statistical tests to determine if two samples are from different dis-tributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a reproducing kernel Hilbert space (RKHS). The first test is based on a large deviation bound for the test statistic, while the second is based on the asymptotic distribution of this statistic. The test statistic can be com-puted in O(m2) time. We apply our approach to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where our test performs strongly. We also demonstrate excellent performance when compar-ing distributions over graphs, for which no alternative tests currently exist

A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays

We introduce the IBM Analog Hardware Acceleration Kit, a new and first of a kind open source toolkit to simulate analog crossbar arrays in a convenient fashion from within PyTorch (freely available at https://github.com/IBM/aihwkit). The toolkit is under active development and is centered around the concept of an "analog tile" which captures the computations performed on a crossbar array. Analog tiles are building blocks that can be used to extend existing network modules with analog components and compose arbitrary artificial neural networks (ANNs) using the flexibility of the PyTorch framework. Analog tiles can be conveniently configured to emulate a plethora of different analog hardware characteristics and their non-idealities, such as device-to-device and cycle-to-cycle variations, resistive device response curves, and weight and output noise. Additionally, the toolkit makes it possible to design custom unit cell configurations and to use advanced analog optimization algorithms such as Tiki-Taka. Moreover, the backward and update behavior can be set to "ideal" to enable hardware-aware training features for chips that target inference acceleration only. To evaluate the inference accuracy of such chips over time, we provide statistical programming noise and drift models calibrated on phase-change memory hardware. Our new toolkit is fully GPU accelerated and can be used to conveniently estimate the impact of material properties and non-idealities of future analog technology on the accuracy for arbitrary ANNs.Comment: Submitted to AICAS202

A Kernel Two-Sample Test

We propose a framework for analyzing and comparing distributions, which we use to construct statistical tests to determine if two samples are drawn from different distributions. Our test statistic is the largest difference in expectations over functions in the unit ball of a reproducing kernel Hilbert space (RKHS), and is called the maximum mean discrepancy (MMD). We present two distribution-free tests based on large deviation bounds for the MMD, and a third test based on the asymptotic distribution of this statistic. The MMD can be computed in quadratic time, although efficient linear time approximations are available. Our statistic is an instance of an integral probability metric, and various classical metrics on distributions are obtained when alternative function classes are used in place of an RKHS. We apply our two-sample tests to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where they perform strongly. Excellent performance is also obtained when comparing distributions over graphs, for which these are the first such tests

Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference

Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics, and the non-ideal peripheral circuitry in AIMC chips, require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In this tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit. The AIHWKit is a Python library that simulates inference and training of DNNs using AIMC. We present an in-depth description of the AIHWKit design, functionality, and best practices to properly perform inference and training. We also present an overview of the Analog AI Cloud Composer, that provides the benefits of using the AIHWKit simulation platform in a fully managed cloud setting. Finally, we show examples on how users can expand and customize AIHWKit for their own needs. This tutorial is accompanied by comprehensive Jupyter Notebook code examples that can be run using AIHWKit, which can be downloaded from https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators

Analog in-memory computing (AIMC) -- a promising approach for energy-efficient acceleration of deep learning workloads -- computes matrix-vector multiplications (MVMs) but only approximately, due to nonidealities that often are non-deterministic or nonlinear. This can adversely impact the achievable deep neural network (DNN) inference accuracy as compared to a conventional floating point (FP) implementation. While retraining has previously been suggested to improve robustness, prior work has explored only a few DNN topologies, using disparate and overly simplified AIMC hardware models. Here, we use hardware-aware (HWA) training to systematically examine the accuracy of AIMC for multiple common artificial intelligence (AI) workloads across multiple DNN topologies, and investigate sensitivity and robustness to a broad set of nonidealities. By introducing a new and highly realistic AIMC crossbar-model, we improve significantly on earlier retraining approaches. We show that many large-scale DNNs of various topologies, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, can in fact be successfully retrained to show iso-accuracy on AIMC. Our results further suggest that AIMC nonidealities that add noise to the inputs or outputs, not the weights, have the largest impact on DNN accuracy, and that RNNs are particularly robust to all nonidealities.Comment: 35 pages, 7 figures, 5 table

Type 1 Autoimmune Pancreatitis in Europe: Clinical Profile and Response to Treatment.

Background and aimsAutoimmune pancreatitis (AIP) is an immune-mediated disease of the pancreas with distinct pathophysiology and manifestations. Our aims were to characterize type 1 AIP in a large pan-European cohort and study the effectiveness of current treatment regimens.MethodsWe retrospectively analyzed adults diagnosed since 2005 with type 1 or not-otherwise-specified AIP in 42 European university hospitals. Type 1 AIP was uniformly diagnosed using specific diagnostic criteria. Patients with type 2 AIP and those who had undergone pancreatic surgery were excluded. The primary endpoint was complete remission, defined as the absence of clinical symptoms and resolution of the index radiological pancreatic abnormalities attributed to AIP.ResultsWe included 735 individuals with AIP (69% male; median age 57 years; 85% White). Steroid treatment was started in 634 patients, of whom 9 (1%) were lost to follow-up. The remaining 625 had a 79% (496/625) complete, 18% (111/625) partial, and 97% (607/625) cumulative remission rate, while 3% (18/625) did not achieve remission. No treatment was given in 95 patients, who had a 61% complete (58/95), 19% partial (18/95), and 80% cumulative (76/95) spontaneous remission rate. Higher (≥0.4 mg/kg/day) corticosteroid doses were no more effective than lower ( 2 weeks (OR 0.908; 95%CI 0.818-1.009). Elevated IgG4 levels were independently associated with a decreased chance of complete remission (OR 0.639; 95%CI 0.427-0.955). Relapse occurred in 30% of patients. Relapses within 6 months of remission induction were independent of the steroid tapering duration, induction treatment duration, and total cumulative dose.ConclusionPatients with type 1 AIP and elevated IgG4 level may need closer monitoring. For remission induction, a starting dose of 0.4 mg/kg/day for 2 weeks followed by a short taper period seems effective. This study provides no evidence to support more aggressive regimens