The document discusses bionic systems that connect biological tissues with artificial devices. Two case studies are described: 1) A lamprey experiment where the reticulospinal pathway was replaced with an electromechanical device, allowing investigation of the relationship between input and output. 2) A monkey experiment where neural activity was used to control a cursor, then an artificial actuator. Performance declined initially but improved with feedback, showing plasticity in representing actuator dynamics. While the artificial components don't directly explain biological mechanisms, they can provide local functional accounts and global insights by allowing investigation of hybrid biological-artificial system functioning.

1. Analyzing the Explanatory Power of Bionic Systems With the
Minimal Cognitive Grid
Antonio Lieto
Università di Torino, Dipartimento di Informatica, IT
ICAR-CNR, Palermo, IT
Polish Academy of Science, Warsaw, 10 October 2022

2. Cognitive AI/Computational CogSci

3. When a biologically/cognitively inspired computational system/architecture
has an explanatory power w.r.t. the natural system taken as source of
inspiration ?
Which are the requirements to consider in order to design a computational
model of cognition with an explanatory power?
Functionalist vs Structuralist Design Approaches
3

4. Functionalist vs Structuralist Models
Same input-out spec. and surface
resemblance of the internal components
and of their working mechanisms
between arti
fi
cial and natural system
Same input-out spec. + constrained
resemblance of the internal components
and of their working mechanisms
between arti
fi
cial and natural system
Functionalist Models Structuralist Models
continuum
Mechanistic
Explanation
Teleological
Explanation
Functional
Explanation
Evolutionistic
Explanation
IBE
Causal
Explanation

5. Modern successful (Functionalist) AI systems
5
IBM Watson
(symbolic)
Alpha Go (Deep Mind)
(connectionist)

6. Non HUMAN ERRORS

7. Jeopardy
7

8. 8

9. 9

10. Wiener’s “Paradox”
“The best material model of a cat is another or possibly the same cat” (Rosenblueth & Wiener45)
Z.Pylyshyn (’79): “if we do not formulate any restriction about a model we obtain the functionalism of a Turing
machine. If we apply all the possible restrictions we reproduce a whole human being”
- Also for complete simulation of complete models (e.g. very simple organisms like the
Caenorhabditis elegans, Kitano et al. 98) it is problematic a full understanding and testing of
biological hypotheses.

11. When a biologically/cognitively inspired computational system/architecture
has an explanatory power w.r.t. the natural system taken as source of
inspiration ?
Which are the requirements to consider in order to design a computational
model of cognition with an explanatory power?
11

12. 12

13. Webb’s dimensions
13
1. Biological Relevance: this dimension shows if and, eventually to what extent, a
computational model can be used to generate and test hypotheses about a given
biological system taken as a source of inspiration.
2. Level: “what are the basic elements of the model that have no internal structure
or their internal structures are ignored”. In other words it identifies the
modelling focus.
3. Generality: the range of biological systems the model can represent.
4. Abstraction: the amount of details included in the artificial model with respect
to the natural system taken as source of inspiration.
5. Structural accuracy: the similarity of the mechanisms behind the behaviour of
an artificial model with respect to those of the target biological system.
6. Performance match: similarity of the performances of the model with respect
to the performances obtained by the target biological system.
7. Medium: the physical medium that has been used to implement the model.

14. Limits of the Webb’s account
1) the concept of “biological relevance” or “structural accuracy” are highly overlapping and
there is not a clearly defined method that one could use in order to determine how such
elements are/can be operationally declined.
2) “Medium” the Webb’s proposal explicitly limits the considerations on this aspect to the
presence (or not) of an embodied agent. The “medium”, in her view, is the physical body of the
agent (a robot).
It does not consider - for example - alternative physical models of computations based, for
example, on quantum computers or on hybrid biological/artificial neural networks realized in
the field of bionics and neuromorphing computing
14

15. Northwest AGI Forum, April 28 2021
Lieto, 2021, Cognitive Design for Arti
fi
cial Minds, Routledge (Taylor & Francis, UK).

16. Minimal Cognitive Grid (MCG)
“a non subjective, graded, evaluation framework allowing both
quantitative and qualitative analysis about the cognitive adequacy
and the human-like performances of artificial systems in both single
and multi-tasking settings.” (Lieto, 2021)
Functional/Structural Ratio
Generality
Performance match (including errors and psychometric measures)
Functionalist Models Structuralist Models

18. 18

19. 19

20. Bionic systems
Bionic systems are a well-known class of hybrid artificial systems connecting biological
tissues with computers or robotic devices through brain–machine interfaces
20
Datteri, 2007

21. Lamprey Experiment
21
Zelenin et al., 2001

22. Lamprey Experiment
22
- example of the simulation–replacement methodology
- development of a mechanistic model of the lamprey sensory
motor system
- replacement of the reticulospinal pathway of the lampreys
with an electromechanical device
Zelenin et al., 2001

23. Lamprey Experiment
23
- example of the simulation–replacement methodology
- development of a mechanistic model of the lamprey sensory
motor system
- replacement of the reticulospinal pathway of the lampreys
with an electromechanical device
Zelenin et al., 2001
Outcome: input–output behavior of such a device corresponds
to the hypothesis on the relationship between the “input” of the
reticular neurons (RS) and the roll angles of the animal, which
vary as a function of the activity of the “output” spinal neurons.
The arti
f
cial component picked up the activity of the reticular
neurons and produced stabilization movements in line with the
hypothesized regularity.
the RS component actually exhibited the hypothesized
input–output regularity r (rs).
comparison in this case has been made purely at the functional
level of the input–output behavior

24. Monkey experiment
•
24

25. Monkey experiment (pole control)
•
25
A reliable correlation was identified between the neural activity and motor
behavior of the monkeys. More precisely, a linear model had been trained
to predict various motor parameters—hand position, hand velocity, and
gripping force—from brain activity.
Tasks:
1) move a cursor displayed on a screen and reach a target
2) change the size of the cursor by applying a gripping force to the pole.
3) a combination of the first two above

26. Monkey experiment (brain control)
26
The linear model derived in the pole phase was used in the “brain control
experiment” to drive the behavior of the decoding system.
The cursor positions were controlled not by the pole but by the output of
the linear model receiving brain activity as the input.

27. Monkey experiment (brain control)
27
Ex of stimulation-connection method: Even if there is an “artificial
replacement”, the resulting behavior is obtained by applying relatively
traditional electrophysiological analysis techniques aimed at analyzing
the responses of the biological tissues connected to the artificial device
and, as a consequence, at understanding or hypothesizing some of their
unknown mechanisms.

28. Monkey experiment (brain control)
28
Outcome: performance suddenly declined in the switch between
pole and brain control modes => lack of a neural representation of
motor control and dynamics.
Performance comparison between the brain/pole control conditions
about the analysis of directional tuning (DT) profiles.

29. Monkey experiment (brain control)
29
Outcome: performance suddenly declined in the switch between
pole and brain control modes => lack of a neural representation of
motor control and dynamics.
Performance comparison between the brain/pole control conditions
about the analysis of directional tuning (DT) profiles.
DT profiles model the relationship between neural activity and direction of movement (e.g., by
outlining that a particular neuron fires maximally whenever the monkey moves its arm leftward)

30. • Author thesis: monkeys’ brains can progressively acquire a
neural representation of the movements of the new
actuator based on visual feedback only.
• “The gradual increase in the behavioral performance
during brain control of the BMI emerged as a consequence
of a plastic reorganization whose main outcome was the
assimilation of the dynamics of an artificial actuator into
the physiological properties of frontoparietal neurons”
(Carmena et al. (2003); p. 205).

31. 31
The MCG shows how in both these cases the artificial component does not
play any direct explanatory role with respect to the mechanisms of the substituted
biological component but shows an explanatory difference between the two
types of experiments

32. But…
These kinds of hybrid bionic systems can have an indirect explanatory role similar
to the one played by some AI systems built by using a structural design approach
obtained with the help of some functional components.
In particular, the artificial replacement of a part of a biological system can provide:
i) a local functional account of that part in the context of the overall
functioning of the hybrid biological–artificial system
ii) global insights about the structural mechanisms of the biological
elements connected to such artificial devices.
32

33. Local functional account
i) a local functional account of that part in the context of the overall
functioning of the hybrid biological–artificial system
> Ex: the Lamprey experiment: It is possible to have a functional
account of the substituted part.
“Computational models that embody functional explanations explain the capacities of a
system in terms of its sub-capacities. But this explanation is given by the assumptions
embodied in the model, not by the computations performed by the model on the grounds
of the assumptions” (Piccinini, 2007)
33

34. Global insights about structural
mechanism…
ii) global insights about the structural mechanisms of the biological elements
connected to such artificial devices.
indirect explanatory role similar to the one played by some AI systems built by using a
structural design approach obtained with the help of some functional components.
34

35. Global insights about structural
mechanism…
ii) global insights about the structural mechanisms of the biological elements
connected to such artificial devices.
indirect explanatory role similar to the one played by some AI systems built by using a
structural design approach obtained with the help of some functional components.
35
Similarity: a functional component of the system can provide insights
on the structural mechanistic concerning the global behavior of the
hybrid system.
Difference: the indirect explanatory role played by the overall bionic
system is in the mechanistic hypotheses that can be drawn over
the biological components of the system (and not on the arti
f
cial one)

36. Upshots
• I have shown how the Minimal Cognitive Grid can be applied to hybrid
bionic systems by showing that there is not a direct mechanistic explanatory
role of the arti
fi
cial component substituting a biological one.
• The Minimal Cognitive Grid analysis con
fi
rms the existence of
different types of experiments in bionic systems (simulation
replacement vs stimulation-connection) and attributes a different
explanatory power to these methodologies.
• Despite the lack of a direct mechanistic account, the replacement of a part
of a biological system in bionic ones can provide:
i) a local functional account of that part in the context of the overall functioning of the
hybrid biological–artificial system
ii) global insights about the structural mechanisms of the biological elements connected to
such artificial devices

37. References
Lieto, A. (2021). Cognitive Design for Artificial Minds, Routledge.
Lieto, A. (2022). Analyzing the Explanatory Power of Bionic Systems With
the Minimal Cognitive Grid. Frontiers in Robotics and AI, 9.