Dendritic Computation

AR245-NE28-18 ARI 13 May 2005 14:15 Dendritic Computation Michael London and Michael Hausser¨ Wolfson Institute for Biomedical Research and Department of Physiology, University College London, London WC1E 6BT; email: [email protected], [email protected] Annu. Rev. Neurosci. Key Words 2005. 28:503–32 dendrites, coding, synaptic integration, spikes, ion channels doi: 10.1146/ annurev.neuro.28.061604.135703 by University of California - San Diego on 09/20/09. For personal use only. Abstract Copyright c 2005 by Annual Reviews. All rights One of the central questions in neuroscience is how particular tasks, Annu. Rev. Neurosci. 2005.28:503-532. Downloaded from arjournals.annualreviews.org reserved or computations, are implemented by neural networks to generate 0147-006X/05/0721- behavior. The prevailing view has been that information processing 0503$20.00 in neural networks results primarily from the properties of synapses and the connectivity of neurons within the network, with the intrinsic excitability of single neurons playing a lesser role. As a consequence, the contribution of single neurons to computation in the brain has long been underestimated. Here we review recent work showing that neuronal dendrites exhibit a range of linear and nonlinear mechanisms that allow them to implement elementary computations. We discuss why these dendritic properties may be essential for the computations performed by the neuron and the network and provide theoretical and experimental examples to support this view. 503 AR245-NE28-18 ARI 13 May 2005 14:15 ties have been attributed to the individual Contents neuron, with the complex computations that are the hallmark of brains being performed INTRODUCTION................. 504 by the network of these simple elements. In THE DENDRITIC “TOOLKIT”. 505 this framework, the neuron (often called a Computations in Passive Dendrites 505 “Perceptron,” “Spin,” or “Unit”) sums up the Computations in Active Dendrites. 509 synaptic input and, by comparing this sum EXAMPLES OF REAL-WORLD against a threshold, “decides” whether to ini- DENDRITIC COMPUTATION 516 tiate an action potential. In computational Directional Selectivity ............ 517 terms this process includes only one nonlinear Coincidence Detection in Auditory term (thresholding), which is usually counted Neurons ...................... 519 as a single operation. Thus, the neuron op- Temporal Integration Over Long erates as a device where analog computations Timescales .................... 520 are at some decision point transformed into Image Processing in Dendrites of a digital output signal. Such a design forms Fly Neurons .................. 520 the backbone of many artificial neuronal net- Looming Sensitive Neurons works, starting from the original work of in the Locust .................. 522 McCullough & Pitts (1943) to the present Forward Masking of Cricket Songs 522 day. In this review we argue that this model Dendritic Mechanisms and is oversimplified in view of the properties of Behavior ...................... 524 real neurons and the computations they per- CHALLENGES FOR THE form. Rather, additional linear and nonlinear FUTURE: A WISH LIST FOR mechanisms in the dendritic tree are likely to DENDRITIC COMPUTATION 524 serve as computational building blocks, which For Molecular Biologists: combined together play a key role in the over- Designer Dendrites............ 524 all computation performed by the neuron. For Neurophysiologists: Putting In the first part of this review we describe Dendrites Back into the the dendritic computational toolkit, i.e., the Network ...................... 526 biophysical mechanisms in dendrites, which For the Theorist: Proving the endow them with potential computational Benefits of Dendritic powers. We focus on the most recent find- Computation.................. 526 ings and group them according to the type of CONCLUSIONS................... 527 computations being carried out. In the second part of the review, we present several exam- by University of California - San Diego on 09/20/09. For personal use only. ples where the role of dendritic mechanisms in computation has strong circumstantial sup- Annu. Rev. Neurosci. 2005.28:503-532. Downloaded from arjournals.annualreviews.org INTRODUCTION port, such as directional selectivity in retinal Brains compute. This means that they pro- neurons or coincidence detection in the au- cess information, creating abstract represen- ditory system. We conclude with a discussion tations of physical entities and performing of how we can ultimately prove the role of operations on this information in order to ex- dendrites in neural computation and outline ecute tasks. One of the main goals of com- a process for achieving this goal. Through- putational neuroscience is to describe these out the review, we focus on the online aspects transformations as a sequence of simple el- of computation. Naturally, the results of such ementary steps organized in an algorithmic computations must ultimately be read out and way. The mechanistic substrate for these com- stored within the network. Given that den- putations has long been debated. Tradition- drites are also the site where synaptic plastic- ally, relatively simple computational proper- ity takes place, their properties are likely to 504 London · Häusser AR245-NE28-18 ARI 13 May 2005 14:15 affect the induction and expression of plastic- tion potential, or are there decision points on ity. These issues have been discussed in several the way? recent reviews (Hausser¨ & Mel 2003, Linden Although the flowchart representation 1999, Mainen 1999, Chklovskii et al. 2004), may not be the most appropriate and compre- and we therefore do not address this aspect in hensive way to describe what neurons are do- detail here. ing, it can nevertheless be very instructive to define and explore the computational building blocks (i.e., the boxes on the flowchart) that neurons can perform. During the past decade THE DENDRITIC “TOOLKIT” a rapidly increasing number of studies in- Why discuss what a neuron does in terms vestigating signaling mechanisms in dendrites of computation? We use a simple example have appeared, and several recent reviews have to illustrate the problem. When one watches discussed these findings in depth (Euler & a movie, each frame is presented for about Denk 2001, Hausser¨ et al. 2000, Koch & 50 ms, during which time we have to pro- Segev 2000, Magee 2000, Segev & London cess any changes from the last frame before 2000, Williams & Stuart 2003). Rather than the next frame appears. To a first approxima- listing all the known facts about dendritic sig- tion the timescale of the computational cycle naling, in this section we focus on identify- of a neuron involved in this computation is ing the unique dendritic mechanisms that can thus on the order of milliseconds to tens of act as computational building blocks. We start milliseconds. Because a typical neuron in the with a brief discussion of how passive den- brain receives thousands of synaptic inputs, a drites transform their inputs and implement neuron involved in processing the visual input nonlinear interactions between synaptic in- thus receives hundreds to thousands of 50-ms puts. Then we review the role of dendritic input spike trains. But the neuron has only one voltage-dependent channels and conclude by axon, which provides the output signal, and evaluating the possible role of dendrites in thus the final conversion represents a com- chemical computation. pression into a much smaller amount of information. Because there are so many inputs, an essential feature of this transformation is the Computations in Passive Dendrites amount of input information that must be dis- It is important to recognize that the passive carded by the neuron. This process is analo- properties of the dendritic tree provide the gous to what some mathematical functions are backbone for the electrical signaling in den- doing: projecting a huge space onto a narrow drites, even when they are richly endowed by University of California - San Diego on 09/20/09. For personal use only. one. Thus, the neuron throws away the irrel- with voltage-dependent ionic currents. For evant information and selects only the infor- example, the threshold for initiation of a den- Annu. Rev. Neurosci. 2005.28:503-532. Downloaded from arjournals.annualreviews.org mation relevant to its function. The essence dritic spike is determined in part by the avail- of computing a function in computer science ability of sodium channels, but perhaps even is usually considered to be implementing an more by the passive load of the surrounding algorithm, that is, a sequence of simple com- dendrites, which dictates how much of the in- putational steps organized in a flowchart that put current will depolarize the membrane and leads from the input to the output (Figure 1). how much will flow axially toward other den- Are there ways to deconstruct into such sim- dritic regions (Segev & London 1999). Thus, pler building blocks the very complex map- understanding the passive properties of den- ping done by a neuron? Can we identify the dritic trees remains crucial for understanding crucial steps that occur during this process? computation in dendritic trees. Aside from Are decisions taken only at the final step of their role in regulating the conditions for ac- transforming the somatic voltage into an active dendritic signaling, the passive properties www.annualreviews.org • Dendritic Computation 505 AR245-NE28-18 ARI 13 May 2005 14:15 Figure 1 Dendritic computation. The task of a brainstem auditory neuron performing coincidence detection in the sound localization system of birds is to respond only if the inputs arriving from both ears coincide in by University of California - San Diego on 09/20/09. For personal use only. a precise manner (10–100 µs), while avoiding a response when the input comes from only one ear. (A)A flowchart of a simplistic algorithm to achieve this computation. Inputs to each ear are summed Annu. Rev. Neurosci. 2005.28:503-532. Downloaded from arjournals.annualreviews.org sublinearly, and the input from both ears is then summed linearly and compared with threshold.

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