认知科学如何重塑神经科学：记忆与大脑计算

神经计算

人工智能

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"《记忆与大脑计算》是C. R. Gallistel 和 Adam Philip King合著的一本关于认知科学如何重塑神经科学的经典著作。书中深入探讨了记忆在大脑计算过程中的核心作用，以及认知科学对理解大脑功能的重要性。" 《记忆与大脑计算》一书揭示了记忆与神经计算之间的紧密联系，指出认知科学将在神经科学研究领域带来革命性的变革。作者C. R. Gallistel 和 Adam Philip King通过对大脑功能的深入分析，强调了记忆作为大脑处理信息的基础，对于理解大脑如何执行复杂的计算任务至关重要。记忆是大脑的重要组成部分，它不仅关乎个体经历的储存和检索，而且在信息处理、决策制定和学习过程中起着关键作用。在书中，作者讨论了记忆的不同类型，包括短期记忆、长期记忆以及工作记忆，这些不同类型的记忆共同构成了大脑的计算能力。他们解释了记忆系统如何通过神经网络的活动来编码、存储和检索信息，这些过程涉及神经元间的连接变化和突触可塑性。此外，书中还涉及了神经计算的概念，这是人工智能领域的一个重要分支，它试图模仿人脑的计算方式。通过对比人脑和人工神经网络，作者探讨了大脑如何通过分布式计算解决复杂问题，并且提出了大脑可能使用的一些算法和策略。这其中包括模式识别、联想记忆和序列处理等高级认知功能的神经基础。《记忆与大脑计算》进一步探讨了认知科学在解析大脑信息处理机制中的应用。作者认为，将认知科学的理论与神经生物学的实证研究相结合，可以更深入地理解大脑的工作原理，从而推动神经科学的发展。书中的内容涵盖了从分子水平到行为水平的记忆研究，涵盖了广泛的主题，如记忆编码的神经机制、记忆巩固的过程以及记忆的可塑性。通过这本书，读者可以了解到记忆系统如何为大脑提供计算能力，以及这种计算能力如何影响我们的感知、思考和行为。同时，书中也提出了许多关于大脑如何进行计算的理论，这些理论对人工智能领域的研究具有启示意义，可能推动人工智能技术的发展，使其更加接近人类智能。《记忆与大脑计算》是一本深入浅出的著作，它将认知科学的理论与神经科学的实践紧密结合，为理解大脑的计算过程提供了全新的视角，对于从事脑科学、神经计算以及人工智能研究的学者和学生来说，都是一本不可多得的参考书籍。

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xvi Preface

of interval timing. Here, again, we show the price that is paid by dispensing with

a read/write memory. Given a read/write memory, it is easy to model, at least to

a ﬁrst approximation, the data on interval timing (Gallistel & Gibbon, 2002; Gibbon,

Church, & Meck, 1984; Gibbon, 1977). Without such a mechanism, modeling these

phenomena is very hard. Because the representational burden is thrown onto the

conjectured dynamic properties of neurons, the models become prey to the prob-

lem of the inﬁnitude of the possible. Basically, you need too many neurons, because

you have to allocate resources to all possible intervals rather than just to those that

have actually been observed. Moreover, these models all fail to provide computa-

tional access to the information about previously experienced durations, because

the information resides not in the activity of the neurons, nor in the associations

between them, but rather in the intrinsic properties of the neurons in the arrays

used to represent durations. The rest of the system has no access to those intrinsic

properties.

Finally, in Chapter 16, we take up the question that will have been pressing

on the minds of many readers ever since it became clear that we are profoundly

skeptical about the hypothesis that the physical basis of memory is some form of

synaptic plasticity, the only hypothesis that has ever been seriously considered by

the neuroscience community. The obvious question is: Well, if it’s not synaptic

plasticity, what is it? Here, we refuse to be drawn. We do not think we know what

the mechanism of an addressable read/write memory is, and we have no faith in

our ability to conjecture a correct answer. We do, however, raise a number of

considerations that we believe should guide thinking about possible mechanisms.

Almost all of these considerations lead us to think that the answer is most likely

to be found deep within neurons, at the molecular or sub-molecular level of struc-

ture. It is easier and less demanding of physical resources to implement a read/write

memory at the level of molecular or sub-molecular structure. Indeed, most of what

is needed is already implemented at the sub-molecular level in the structure of DNA

and RNA.

9781405122870_1_pre.qxd 23/1/09 11:08 AM Page xvi

Information

Most cognitive scientists think about the brain and behavior within an informa-

tion-processing framework: Stimuli acting on sensory receptors provide informa-

tion about the state of the world. The sensory receptors transduce the stimuli into

neural signals, streams of action potentials (aka spikes). The spike trains transmit

the information contained in the stimuli from the receptors to the brain, which pro-

cesses the sensory signals in order to extract from them the information that they

convey. The extracted information may be used immediately to inform ongoing beha-

vior, or it may be kept in memory to be used in shaping behavior at some later time.

Cognitive scientists seek to understand the stages of processing by which informa-

tion is extracted, the representations that result, the motor planning processes through

which the information enters into the direction of behavior, the memory processes

that organize and preserve the information, and the retrieval processes that ﬁnd the

information in memory when it is needed. Cognitive neuroscientists want to under-

stand where these different aspects of information processing occur in the brain

and the neurobiological mechanisms by which they are physically implemented.

Historically, the information-processing framework in cognitive science is closely

linked to the development of information technology, which is used in electronic

computers and computer software to convert, store, protect, process, transmit, and

retrieve information. But what exactly is this “information” that is so central to

both cognitive science and computer science? Does it have a rigorous meaning?

In fact, it does. Moreover, the conceptual system that has grown up around this

rigorous meaning – information theory – is central to many aspects of modern

science and engineering, including some aspects of cognitive neuroscience. For

example, it is central to our emerging understanding of how neural signals trans-

mit information about the ever-changing state of the world from sensory receptors

to the brain (Rieke, Warland, de Ruyter van Steveninck, & Bialek, 1997). For us,

it is an essential foundation for our central claim, which is that the function of

the neurobiological memory mechanism is to carry information forward in time in

a computationally accessible form.

9781405122870_4_001.qxd 23/1/09 11:18 AM Page 1

Memory and the Computational Brain: Why Cognitive Science Will Transform Neuroscience C. R. Gallistel

2 Information

Shannon’s Theory of Communication

The modern quantitative understanding of information rests on the work of

Claude Shannon. A telecommunications engineer at Bell Laboratories, he laid the

mathematical foundations of information theory in a famous paper published in

1948, at the dawn of the computer age (Shannon, 1948). Shannon’s concern was

understanding communication (the transmission of information), which he schem-

atized as illustrated in Figure 1.1.

The schematic begins with an information source. The source might be a person

who hands in a written message at a telegraph ofﬁce. Or, it might be an orchestra

playing a Beethoven symphony. In order for the message to be communicated to

you, you must receive a signal that allows you to reconstitute the message. In this

example, you are the destination of the message. Shannon’s analysis ends when the

destination has received the signal and reconstituted the message that was present

at the source.

The transmitter is the system that converts the messages into transmitted signals,

that is, into ﬂuctuations of a physical quantity that travels from a source location

to a receiving location and that can be detected at the receiving location. Encoding

is the process by which the messages are converted into transmitted signals. The

rules governing or specifying this conversion are the code. The mechanism in the

transmitter that implements the conversion is the encoder.

Following Shannon, we will continue to use two illustrative examples, a telegraphic

communication and a symphonic broadcast. In the telegraphic example, the source

messages are written English phrases handed to the telegrapher, for example,

“Arriving tomorrow, 10 am.” In the symphonic example, the source messages are

sound waves arriving at a microphone. Any one particular short message written

in English and handed to a telegraph operator can be thought of as coming from

a ﬁnite set of possible messages. If we stipulate a maximum length of, say, 1,000

INFORMATION

SOURCE

TRANSMITTER

MESSAGE

NOISE

SOURCE

SIGNAL RECEIVED

SIGNAL

MESSAGE

RECEIVER DESTINATION

Figure 1.1 Shannon’s schematization of communication (Shannon, 1948).

9781405122870_4_001.qxd 23/1/09 11:18 AM Page 2

Information 3

characters, with each character being one of 45 or so different characters (26 letters,

10 digits, and punctuation marks), then there is a very large but ﬁnite number of

possible messages. Moreover, only a very small fraction of these messages are intel-

ligible English, so the size of the set of possible messages – deﬁned as intelligible

English messages of 1,000 characters or less – is further reduced. It is less clear

that the sound waves generated by an orchestra playing Beethoven’s Fifth can be

conceived of as coming from a ﬁnite set of messages. That is why Shannon chose

this as his second example. It serves to illustrate the generality of his theory.

In the telegraphy example, the telegraph system is the transmitter of the mes-

sages. The signals are the short current pulses in the telegraph wire, which travel

from the sending key to the sounder at the receiving end. The encoder is the tele-

graph operator. The code generally used is the Morse code. This code uses pulses

of two different durations to encode the characters – a short mark (dot), and a

long mark (dash). It also uses four different inter-pulse intervals for separations

– an intra-character gap (between the dots and dashes within characters), a short

gap (between the letters), a medium gap (between words), and a long gap (between

sentences).

In the orchestral example, the broadcast system transmitting radio signals from

the microphone to your radio is the transmitter. The encoder is the electronic device

that converts the sound waves into electromagnetic signals. The type of code is likely

to be one of three different codes that have been used in the history of radio (see

Figure 1.2), all of which are in current use. All of them vary a parameter of a high-

frequency sinusoidal carrier signal. The earliest code was the AM (amplitude

modulated) code. In this code, the encoder modulates the amplitude of the carrier

signal so that this amplitude of the sinusoidal carrier signal varies in time in a way

that closely follows the variation in time of the sound pressure at the microphone’s

membrane.

When the FM (frequency modulated) code is used, the encoder modulates the

frequency of the carrier signal within a limited range. When the digital code is used,

as it is in satellite radio, parameters of the carrier frequency are modulated so as

to implement a binary code, a code in which there are only two characters, cus-

tomarily called the ‘0’ and the ‘1’ character. In this system, time is divided into

extremely short intervals. During any one interval, the carrier signal is either low

(‘0’) or high (‘1’). The relation between the sound wave arriving at the microphone

with its associated encoding electronics and the transmitted binary signal is not

easily described, because the encoding system is a sophisticated one that makes use

of what we have learned about the statistics of broadcast messages to create efﬁcient

codes. The development of these codes rests on the foundations laid by Shannon.

In the history of radio broadcasting, we see an interesting evolution (Figure 1.2):

We see ﬁrst (historically) in Figure 1.2a a code in which there is a transparent (eas-

ily comprehended) relation between the message and the signal that transmits it

(AM). The code is transparent because variation in the amplitude of the message

is converted into variation in the amplitude of the carrier signal that transmits

the message. This code is, however, inefﬁcient and highly vulnerable to noise. It

is low tech. In Figure 1.2b, we see a code in which the relation is somewhat less

transparent, because variation in the amplitude of the message is converted into

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